The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

We introduce the Symplectic Structure of Information Geometry based on Souriau’s Lie Group Thermodynamics model, with a covariant definition of Gibbs equilibrium via invariances through co-adjoint action of a group on its moment space, defining physical observables like energy, heat, and moment as pure geometrical objects. Using Geometric (Planck) Temperature of Souriau model and Symplectic cocycle notion, the Fisher metric is identified as a Souriau Geometric Heat Capacity. Souriau model is based on affine representation of Lie Group and Lie algebra that we compare with Koszul works on G/K homogeneous space and bijective correspondence between the set of G-invariant flat connections on G/K and the set of affine representations of the Lie algebra of G. In the framework of Lie Group Thermodynamics, an Euler-Poincaré equation is elaborated with respect to thermodynamic variables, and a new variational principal for thermodynamics is built through an invariant Poincaré-Cartan-Souriau integral. The Souriau-Fisher metric is linked to KKS (Kostant-Kirillov-Souriau) 2-form that associates a canonical homogeneous symplectic manifold to the co-adjoint orbits. We apply this model in the framework of Information Geometry for the action of an affine Group for exponentiel families, and provide some illustrations of use cases for multivariate Gaussian densities. Information Geometry is presented in the context of seminal work of Fréchet and his Clairaut-Legendre equation. Souriau model of Statistical Physics is validated as compatible with Balian gauge model of thermodynamics. We recall the precursor work of Casalis on affine group invariance for natural exponential families.

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here we use joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical input across the country, Sentinel-1 12-day backscatter composites were created after incidence angle normalization, and Sentinel-2 NDVI images were smoothed to yield dekadal cloud-free composites. An optimized random forest classifier predicted the 8 crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types are largest. Furthermore we showed that the concept of classification confidence derived from the random forest classifier provided insight in the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood utilizing calculated masks over each component of the LAB colorspace, as well an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying the hysteresis thresholding segmentation. The proposed method is tested and compared with variations, and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-Score, respectively. The results and computational efficiency of the proposed method show that it is suitable for on board data execution and decision-making during UAVs flight.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Domain generalization has led to remarkable achievements in Hyperspectral Image (HSI) classification. Inspired by contrastive language-image pre-training (CLIP), the language-aware domain generalization method has been explored to learn cross-domain-invariant representation. However, existing methods face some challenges: 1) The weak capacity to extract long-range contextual information and inter-class correlation. 2) Due to the inadequacies of the large-scale pre-training for HSI data, the spatial-spectral features of HSI and linguistic features can not be straightforwardly alignment. To address the above problems, a novel network has been proposed with a CLIP framework, which consists of an image encoder, based on an encoder-only transformer to obtain the global contextual information and inter-class correlation, a frozen text encoder, and a cross-attention mechanism, named Linguistic-Interact-with-Visual Engager (LIVE), enhances the interaction between two modalities. Extensive experiments demonstrating superior performance over state-of-the-art methods in HSI Domain Generalization with a CLIP framework.

The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities, and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

This paper investigates the feasibility of a backend design for real-time, multiple-channel processing digital phased array system, particularly for high-performance embedded computing platforms constructed of using general purpose digital signal processors. Frist, we obtained the lab-scale backend performance benchmark from simulating beamforming, pulse compression, and Doppler filtering based on MicroTCA chassis using Serial RapidIO protocol in backplane communication. Next, a field-scale demonstrator of a multifunctional phased array radar is emulated by using the similar configuration. Interestingly, the performance of a barebone design is compared to that of emerging tools that systematically take advantage of parallelism and multicore capabilities, including Open Computing Language.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

MIMO (multiple-input multiple-output) radar provides much more flexibility than the traditional radar for its ability to realize far more observation channels than the actual number of T/R (transmit and receive) elements. Designing the array of MIMO imaging radar, the commonly used virtual array theory generally assumes that all elements are placed on the same line. However, due to the physical size of the antennas and coupling effect between T/R elements, a certain height difference between T/R arrays is essential, resulting in the defocusing of edge points of the scene. On the other hand, the virtual array theory implies far-field approximation, leading to inevitable high grating lobes in the imaging result of near-field edge points of the scene observed by common MIMO array. To tackle these problems, this paper derives the relationship between target’s PSF (point spread function) and pattern of T/R arrays, by which the design criterion of near-field imaging MIMO array is presented. Firstly, the proper height between T/R arrays is designed to focus the near-field edge points well. Secondly, the far-field array is modified to suppress the grating lobes in the near-field area. Finally, the validity of the proposed methods is verified by simulations and an experiment.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (<0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003-2010-2014-2016). The scenarios were de-signed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios Business-As-Usual (BAU), Rapid Economic Growth (REG) and Sustainable Management of the Environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+ 84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.

Here, satellite images from 2006 to 2022 were collected to assess changes in the sandbar topography of the Nakdong River Estuary region, southeast of South Korea. The employed method of satellite image acquisition involves capturing images at the same time each year to facilitate a comparison of topographical changes over time. The shorelines of the islands in the study area, including Jinwoo Island, Shinja Island, and Doyo Sandbar, were analyzed. Shoreline analysis was performed using the digital shoreline analysis system (DSAS) package in ArcGIS. The results indicate that Jinwoo Island is expanding at 6.46 m/year in the direction of Nulcha Island. The western side of Shinja Island is expanding at 4.76 m/year, whereas the eastern side is retreating at a rate of 24.41 m/year. In the case of the Doyo Sandbar, the southern shoreline is retreating at a rate of 14.69 m/year; however, the speed is gradually decreasing. Marked topographical changes are evident in the Nakdong River Estuary, and the interlinked barrier islands undergo deposition and erosion, leading to alterations in the shoreline. These findings serve as foundational data for predicting and addressing environmental and social issues arising in the Nakdong River Estuary region due to climate change.

The Action Control Theory (ACT) is an enactive theory of perception and mental imagery with six components: Schemata, Objects, Actions, Affect, Goals and Others’ Behavior. The evidence support-ing these six connected processes is reviewed in light of research on mental imagery vividness. The six processes and their interconnections receive empirical support from a wide range of studies. All six domains of perception and mental imagery are influenced by individual differences in vividness. Real-world applications of ACT show interesting potential to improve human well-being in both healthy people and patients. Mental imagery can be applied in creative ways to make new collective goals and actions for change that are necessary to maximize the future prospects of the planet.

Visual imagery, i.e., the mental experience of an object occurring in the absence of external visual stimulus, might encompass diverse content according to different observers. Further, subjectively experienced time is encoded in the later entorhinal cortex. Starting from these two observations, and considering Einstein’s account of spacetime, we show how, in terms of special relativity, the content of visual imagery is not stationary and fixed, rather depends on the standpoint of the observer. We elucidate how the subjective definition of time (perceived by our mind as static) might give rise to modifications in the length of the imagined object which are experimentally assessable and quantifiable. In particular, based on recent neuroscientific literature, we show how changes in our mental time windows are able to squeeze the visual content of mental imagery.

Byers Peninsula (62°34’-62°40’S-60°54’61°13’W), 60 km2 in size, is considered one of the largest ice-free areas in Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes, limnology and microbiology, live cycles, but not albedo. Surface albedo is one of the key physical parameters in the surface energy budget of polar regions. Most of Antarctica is covered by ice sheets; only about 0.44% of the area is permanently ice-free. However, in maritime Antarctica, the ice-free areas, corresponding to small islands, peninsulas, and coastal beaches, account for about 3% of the territory. To incorporate the contribution of these areas into global albedo models, it is necessary to relate the soil properties of the ice-free areas to the surface albedo response. Also, mapping soil properties and albedo have and special interest in these ice-free areas. Image classification using machine learning methods trained with georeferenced soil data could be useful for mapping soil properties and albedo in multispectral optical satellite images. A shallow neural network implemented using the Keras Python module was used to define and train models of soil properties using 15 explanatory variables corresponding to bands and spectral indices of a Sentinel image and a population of 49 soil samples taken from the top 5 centimeters of the soil profile. The soil samples were analyzed in the laboratory and a spectral library in the Vis-Nir range (350-2500 nm) was created. The albedo of the samples was integrated from the ADS spectra. At the same time, a linear regression model of albedo using the soil properties as explanatory variables was performed. The R2 fit of this new model was about 0.82 and the error of estimation was 4.1. The model was extended to the entire Byers Peninsula using the ML soil property models as explanatory variables. The RMSE of the extended models increased up to 8.2. We think that the main cause of this increase (2.6 points) is related to the error propagation, phenomenon due to the use of models as explanatory variables. However, another important part of the error increase could be due, among other reasons, to the use of an image that, although corrected, is not completely free of clouds.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

This work presents a new scheme for autonomous control and obstacle detection of a catamaran unmanned surface vessel. Based on the Newtonian mechanics, hydrodynamics, and maneuvering modelling group modelling principles in the attached coordinate system, a three-degree-of-freedom motion model of the catamaran unmanned surface vessel is constructed. The fuzzy inference method is used to overcome the drawback that the traditional PID control cannot tune the PID parameters online, and the trajectory controller is designed. Meanwhile, 3D LIDAR is used to achieve obstacle detection. The results of the trajectory tracking simulations and experiments verify the accuracy of the model design and the effectiveness of the autonomous control design.

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explores the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalysed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets are statistically analysed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums are examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. But our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of adf. Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses edf and the so-called dvm to effectively quantify deviations between distributions. By applying this metric, the study measures the consistency, reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behaviour under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.

COMPASS is a mission concept aiming to determine the recent climate history of planet Mars. Aboard this mission, there would be a dual mode radar instrument capable of acting both as a Synthetic Aperture Radar (SAR) and a Subsurface Sounder for mapping shallow ice. The COMPASS Radar Observer for Mars Exploration (CROME) instrument will help advance science and exploration. First, the COMPASS mission is described. Then, the CROME instrument concept and its implementation are presented. Finally, the procedure used to predict the performance is detailed for both the Side-Looking SAR and the Nadir-looking Sounder modes.

Observed sea surface Ka-band normalized radar backscatter cross section (NRCS) and Doppler velocity (DV) exhibit energy at low frequencies (LF) below the surface wave range. It is shown that non-linearity in NRCS-wave slope Modulation Transfer Function (MTF) and inherent NRCS averaging within the footprint account for the NRCS and DV LF variance with the exception of VV NRCS for which almost half of the LF variance is attributable to wind fluctuations. Although the distribution of radar DV is quasi-Gaussian suggesting virtually little impact of non-linearity, the LF DV variations arise due to footprint averaging of correlated local DV and non-linear NRCS. Numerical simulations demonstrate that MTF non-linearity weakly affects traditional linear MTF estimate (less than 10% for |MTF|< 20). Thus the linear MTF is a good approximation to evaluate the DV averaged over large footprints typical of satellite observations.

Mapping short-term wetland vegetation and water storage changes is valuable for monitoring the biogeochemical processes of wetland systems. Old Woman Creek National Estuarine Research Reserve is a dynamic freshwater estuary that experiences intermittent changes in water level over the course of a year. Small unmanned aerial systems (sUAS) are useful tools in monitoring changes as they are rapidly deployed, repeatable, and high-resolution. In this study, commercial quadcopters were paired with a red/green/near-infrared MAPIR Survey 3W camera to produce normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) maps to observe short-term changes at OWC. Orthomosaics were produced for flights on 8 days throughout 2018 and early 2019. The orthomosaics were calibrated to bottom-of-atmosphere reflectance using the Empirical Line Correction method and NDVI and NDWI maps were created. The NDVI pixel values were used to generate maps of vegetation extent showing density changes over time. Identifying dominant vegetation in these maps allowed for the application of the National Estuarine Reserve System (NERRS) Classification Codes to zones of interest. NDWI provided water extent at different water levels and when paired with LiDAR and bathymetric data yielded water volume and residence time estimates. The produced maps contribute to the overall understanding of habitats affected by water inundation variations.

While maintenance dredging of port access channels is often required to maintain navigability, it can result in increased turbidity, sediment plumes, and associated reductions in water quality. Unoccupied aircraft systems (UAS, or drones) are increasingly applied to study water quality due to their high spatial and temporal resolutions. In this study, we investigated the use of drone imagery to monitor turbidity in the Morehead City Harbor, North Carolina, USA, during channel maintenance by hopper dredge. Drone flights were conducted concurrently with in-situ sampling during active dredging and post-dredging. Multispectral drone images were radiometrically calibrated, converted to reflectance and then turbidity using two separate processing methods and a single-band (red; 620nm-700nm) generic turbidity retrieval algorithm, and then compared to in-situ measurements. The method of using average reflectance to retrieve a single turbidity measurement per drone image produced agreeable results when compared to the in-situ measurements (R² = 0.84). This method was then used to generate turbidity maps and extract surface plumes. While this could be considered a limited validation, the results indicate that realistic values can be obtained from drone imagery for low and high turbidity concentrations (1-72 FNU), making drones a viable option for monitoring surface turbidity associated with dredging.

In this paper, we dive into the exciting world of wireless communication, focusing on how millimeter-wave technology and Multiple-Input Multiple-Output phased array antennas are shaping the future of 5G and the upcoming 6G technologies. We cover the latest advancements in millimeter-wave and beam-forming technologies, emphasizing their role in enhancing network security and efficiency in automotive vehicles through dual radar communication. Our discussion spans the benefits, applications, challenges, and solutions of these technologies individually from millimeter-wave to beam-forming technology and joint radar communications, alongside a look at their theoretical and practical implementations. We emphasize the integration of beam-forming technology in joint radar communications for future automotive vehicles and its impact on automotive systems, smart cities, and the Internet of Things (IoT). Looking ahead, we discuss the potential of these technologies to transform future tech landscapes, while also addressing the security implications of merging communication and radar capabilities. This paper aims to provide a clear view of the advancements and prospects of millimeter-wave, beam-forming, and dual radar communication technologies.

Currently, electromagnetic and radar methods are the most common non-destructive tests for non-destructive rebar location in structures.. Both methods have some advantages, disadvantages and limitations. This article is an attempt to describe possibilities of particular methods with emphasizing their advantages and limitations. The first stage of tests included results from tests conducted on a lintel beam made of autoclaved aerated concrete (AAC) and reinforced with small-diameter rebars and rebars placed close to each other. The second stage consisted in testing nine lightweight concrete specimens, each with three bars having a diameter of 10, 16 and 20 mm at three different space arrangements, to determine the effect of bar diameter and spacing on the measurement accuracy. The reinforcement of lintels and specimens was tested with two different electromagnetic scanners and a ground-penetrating radar (GPR) device. Received results from direct tests were compared with results from non-destructive tests (NDT). NDT tests conducted in such an element were found to perform the correct assessment of the concrete cover, and at the same time to give ambiguous results with reference to shape and number of rebars.

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

A method of direction-of-arrival (DoA) estimation for FMCW radars is presented. In addition to MUSIC and ESPRIT, which are well-known high-resolution DoA estimation algorithms, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and field experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.

In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, a high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

Suicide and suicidal behavior are major public health concerns and affect 3-9% of the population worldwide. Despite growing evidence, there are still few effective interventions available to reduce suicide risk. In this article, we describe theoretical models of suicide ideation and behavior and propose to examine the possible effectiveness of a new and innovative preventive strategy. A model of suicidal intrusion (mental imagery related to suicide, also referred to as suicidal flash-forwards) is presented describing one of the assumed mechanisms in the etiology of suicide and the mechanism of therapeutic change. We provide a brief rationale for an Eye Movement Dual Task (EMDT) treatment for suicidal intrusions describing techniques that can be used to target these suicidal mental images and thoughts to reduce overall behavior. Based on the available empirical evidence for the mechanisms of suicidal intrusions, this approach appears to be a promising new treatment to prevent suicidal behavior as it potentially targets one of the linking pins between suicidal ideation and suicidal actions.

We describe an efficient and cost effective data access mechanism for Sentinel-1 TOPS 1 mode bursts. Our data access mechanism enables burst-based data access and processing, thereby 2 eliminating ESA’s Sentinel-1 SLC data packaging conventions as a bottleneck to large scale processing. 3 Pipeline throughput is now determined by available compute resources and efficiency of the analysis 4 algorithms. For targeted infrastructure monitoring studies, we are able to generate coregistered, 5 geocoded stacks of SLCs for any AOI in the world in a few minutes. In addition, we describe our 6 global scale radar backscatter and interferometric products and associated pipeline design decisions 7 that ensure geolocation consistency across the suite of derived products from Sentinel-1 data. Finally, 8 we discuss the benefits and limitations of working with geocoded SAR SLC data.

Spatial data conflation plays a fundamental role in in many aspects of modern Geographic Information Systems (GIS) research and development such as geospatial data visualization, incremental updating of databases and disaster evaluation. The primary objective of conflation is to derive valuable information based on the comparison of multiple spatial data sources of homogeneous or heterogeneous nature. This paper reviews the state of art of the concept of conflation, feature matching, the most important progresses of conflation between image and road network data, and the complexity of spatial conflation.

Byers Peninsula is considered one of the largest ice-free areas in Maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes in lives cycles, limnology and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical and morphological characteristics strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. To mapping and monitoring those areas with high biological occupation, could be very useful to have models of edaphic properties prepared from satellite images. In this approach, Deep Learning and Linear Regression models of soil properties and spectral indexes as explicative variables were performed. We training models of soil properties closely related to biological activity such as Dissolved Organic Carbon (DOC) and the iron fraction associated with organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe and pH by training models of Linear Regression and Deep Learning over Sentinel2 and WorldView2 images. The most robust models were used to track possible areas with ornithogenic soils as well as areas of the Byers Peninsula that could support the highest biological development.

The paper presents experimental studies on the geophysical survey of Almaty – Nur-Sultan highway sections, which located 100 km. from Almaty, Almaty region of the Republic of Kazakhstan. As the object of study selected highway sections in 3 places: on the highway section with a well-preserved coating to identify layers; on the highway section with obvious damage (with holes, potholes, horizontal and grid cracks); on the section of the highway with a drainage pipe. Since the formulation of the problem requires the use of a non-destructive, fast method that gives an idea of the layer structures with an accuracy of ± 0.1 m., experimental studies were conducted using the ground penetrating radar (GPR) «Loza B» using different antennas, with different profile steps for accuracy. The results of the geophysical survey of the structure of the underlying layers and asphalt for the detection of defects and their causes are presented. The results of this work can be used to develop compositions of asphalt concrete mixtures with high temperature stability, strength and durability, considering the climatic conditions in order to ensure the safety of road surfaces during operation.

Bello's stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth's surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date, no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a wideband generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Invasive species are significant threats to global agriculture and food security being the major causes of crop loss. An operative biosecurity policy requires full automation of detection and habitat identification of the potential pests and pathogens. Unmanned Aerial Vehicles (UAVs) mounted thermal imaging cameras can observe and detect pest animals and their habitats, and estimate their population size around the clock. However, their effectiveness becomes limited due to manual detection of cryptic species in hours of captured flight videos, failure in habitat disclosure and the requirement of expensive high-resolution cameras. Therefore, the cost and efficiency trade-off often restricts the use of these systems. In this paper, we present an invasive animal species detection system that uses cost-effectiveness of consumer-level cameras while harnessing the power of transfer learning and an optimised small object detection algorithm. Our proposed optimised object detection algorithm named Optimised YOLO (OYOLO) enhances YOLO (You Only Look Once) by improving its training and structure for remote detection of elusive targets. Our system, trained on the massive data collected from New South Wales and Western Australia, can detect invasive species (rabbits, Kangaroos and pigs) in real-time with a higher probability of detection (85–100 %), compared to the manual detection. This work will enhance the visual analysis of pest species while performing well on low, medium and high-resolution thermal imagery, and equally accessible to all stakeholders and end-users in Australia via a public cloud.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of the different reflection patterns. The proposed framework consists of four processes including signal processing, cloud points clustering, target tracking, and target recognition. Signal processing translates the raw signals into spare cloud points. These points are merged into several clusters, each representing a single target. Target tracking estimates the new locations of each detected target. A novel convolutional neural network model is developed for drone and/or non-drone targets feature extraction and recognition. For performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments is used for training and testing the convolutional neural network. The proposed recognition model achieves an accuracy of 98.4% for one target and 98.1% for two targets.

In this study, an experiment was conducted on hoop pine (Araucaria cunninghamii), which is a common tree species in Taiwan. Holes with diameters of 4, 5, 7, 10, 12, and 14 cm were drilled in dry hoop pine timber with a diameter of 39 cm and a height of 57 cm, and an 800 MHz Ground Penetrating Radar (GPR) antenna were used to determine the minimum image resolution and the size of the holes in the wood. GPR images of the timber were subjected to a filtering process to obtain characteristic reflection curves of the damage to the timber. Curves with clear waveforms were observed in the GPR profiles of eccentric holes with a diameter of 4 cm. Profiles of the damage were obtained and profile images with the trunk radius at the centre were transformed into polar coordinate images. From these polar coordinate images, the location, size and diameter of the eccentric holes in the wood were identified, and the reflection curves were characterised by waveforms and irregular fluctuations. The risk levels of tree trunks were determined on the basis of their t/R (minimum normal wall thickness of the cross-section of the trunk/radius of the trunk) values and hole area ratios. A comparison between the GPR-based results and the actual diameters of the holes revealed that the filtered GPR profiles could be used for the efficient and accurate assessment of damage to hoop pine timber (average error rate ≤ 4%).

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. Leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms and extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

We describe an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We use simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We quantify the loss of precision due to application of static flattening factors, and show that these are well below stated requirements of change detection algorithms. Finally, we discuss the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth Observation Satellites (CEOS) Analysis Ready Dataset (ARD) families - Geocoded Single Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework.

During the few years, various algorithms have been developed to extract features from high-resolution satellite imagery. For the classification of these extracted features, several complex algorithms have been developed. But these algorithms do not possess critical refining stages of processing the data at the preliminary phase. Various satellite sensors have been launched such as LISS3, IKONOS, QUICKBIRD, and WORLDVIEW etc. Before classification and extraction of semantic data, imagery of the high resolution must be refined. The whole refinement process involves several steps of interaction with the data. These steps are pre-processing algorithms that are presented in this paper. Pre-processing steps involves Geometric correction, radiometric correction, Noise removal, Image enhancement etc. Due to these pre-processing algorithms, the accuracy of the data is increased. Various applications of these pre-processing of the data are in meteorology, hydrology, soil science, forest, physical planning etc. This paper also provides a brief description of the local maximum likelihood method, fuzzy method, stretch method and pre-processing methods, which are used before classifying and extracting features from the image.

Uganda is one of the poorest nations in the world. To address the developmental challenges and understand social and economic status, it is important to obtain accurate data in a timely manner. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, methods developed from these studies (1) suffer from coarse resolutions, (2) fail to capture the nonlinearity and multi-scale variability of geospatial data, and (3) perform poorly for agriculture-dependent regions. This study proposes a new enhanced light intensity model (ELIM) to estimate the Gross Domestic Product (GDP) at sub-national scales for Uganda. This model is developed by combining the NTL data from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the population data from the Global Human Settlement Layer (GHSL), and information on agricultural production and market prices across several commodity types. This resulted in a gridded dataset for GDP and GDP per capita for Uganda at 1 km spatial resolution and district level to capture the spatial heterogeneity in economic activity.

Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, street furniture and traffic signs) makes it the most economical complement to the cameras in the visible spectrum, in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithm will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its aver-age error in distance, the maximum errors and the number of echoes per second it can provide. In this work we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in-situ sampling, and HF radar. The aim is to deepen our understanding of surface currents' impact on contaminant dispersion, thereby improving predictive models for environmental incident management, including pollutant releases. Rhodamine WT dye, chosen for its significant fluorescent properties and detectability, along with drifter data, allowed us to investigate the dynamics of near-surface physical phenomena such as the Ekman current, Stokes drift, and wind-driven currents. Our research emphasizes the importance of integrating scalar tracers and Lagrangian markers in experimental designs, revealing differential dispersion behaviors due to near-surface vertical shear caused by the Ekman current and Stokes drift. The elongation direction of the dye patch aligns with the Ekman current direction during slow current conditions. Analytical calculations of vertical shear, based on the Ekman current and Stokes drift, closely matched those derived from tracer observations. Over a 7-hour experiment, the vertical diffusivity of near-surface water was observed at 2×10-4 m2/s, and the horizontal eddy diffusivity of the dye patch and drifters reached the order of 1 m2/s and at a 1000m length scale. Particle tracking models demonstrate that while HF radar currents can effectively predict the trajectories of tracers near the surface, incorporating near-surface currents, including Ekman current, Stokes drift, and windage, is essential for a more accurate prediction of the fate of surface floats.

Environmental effects and climate change are lately representing an increasing strain of the coastal areas which topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. A continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two-years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relation with the precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.

Weather radar plays a crucial role in the monitoring of severe convective weather. However, due to its limited detection range, it cannot conduct effective monitoring in remote offshore areas. Therefore, this paper utilized the Unet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibit significant diurnal cycles, but there are notable differences in their variation characteristics. To address this, we established 4 comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results show that adding the distribution map of the minimum cloud-top brightness temperature at the corresponding time in the model can effectively reduce the root mean square error (RMSE) of the model and enhance the accuracy of severe convective weather monitoring. This conclusion can be applied in the future to enhance and improve satellite quantitative precipitation estimation.

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

Natural vegetation restoration can take decades to achieve and requires information on species composition and trend to inform management strategies. Here we demonstrate the potential and portability of drone, dove and Sentinel-2 imagery for species level classification and trajectory monitoring using three study sites with different revegetation histories. Drone imagery (4 cm) was classified using random forests. Dove (3 m) and Sentinel-2 (10 m) images were acquired close to the anniversary of the drone flights and converted into fractional cover maps showing the proportion of exotics, natives and other land covers using ordinary least squares (OLS) and geographically weighted regression (GWR). The time series of fractional cover maps were converted into positive and negative slope and second-order curvature and summarised as one RGB composite to illustrate trend and observe exotic expansion. Drone classification accuracies ranged from 73%-76% when applied to the full plant species list and between 93% and 97% when grouped into natives, exotics, and other land covers. GWR outperformed OLS for all fractional cover mapping and GWR with dove imagery was more accurate than with Sentinel imagery. The combination of very high-resolution drone acquired imagery with the versatility of dove acquired imagery arms land managers with highly accurate species composition and their spatio-temporal dynamics to promote timely intervention.

Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's Disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted CDR presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4,139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine Learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Unmanned aerial vehicles (UAV) thermal imagery offers several advantages in environmental monitoring, as it can provide a low-cost, high-resolution, and flexible solution to measure the temperature of the surface of the land. Limitations related to the maximum load of the drone lead to use of lightweight uncooled thermal cameras whose internal components are not stabilized to a constant temperature. Such cameras suffer from several unwanted effects that contribute to the increase in temperature measurement error from ±0.5 °C in laboratory conditions, to ±5 °C in unstable flight conditions. This article describes a post processing procedure, that reduces the above unwanted effects. It consists of following steps: i) devignetting using single image vignette correction algorithm, ii) georeferencing of images using EXIF data, scale-invariant feature transform (SIFT) stitching, and gradient descent optimisation, and iii) temperature calibration by minimisation of bias between overlapping thermal images using gradient descent optimisation. The solution was tested in several case studies of river areas, where natural water bodies were used as a reference temperature benchmark. In all tests, the precision of the measurements was increased. The root of the mean of the Square of Errors RMSE on average was reduced by 39.0% and Mean of the absolute value of Errors MAE by 40.5%. The proposed algorithm can be called self-calibrating, as in contrast to other known solutions is fully automatic, uses only field data and does not require any calibration equipment or additional operator effort. A Python implementation of the solution is available on GitHub.

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

Recent advances in open data initiatives allow us to free access to a vast amount of open LiDAR data in many cities. However, most of these open LiDAR data over cities are acquired by airborne scanning, where the points on façades are sparse or even completely missing due to the viewpoint and object occlusions in the urban environment. Integrating other sources of data, such as ground images, to complete the missing parts is an effective and practical solution. This paper presents an approach for improving open LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two different sources of data. Firstly, the façade point cloud generated from terrestrial images is initially geolocated by matching the SFM camera positions to their GPS meta-information. Next, an improved Coherent Point Drift algorithm with normal consistency is proposed to accurately align building façades to open LiDAR data. The significance of the work resides in the use of 2D overlapping points on the outline of buildings instead of limited 3D overlap between the two point clouds and the achievement to a reliable and precise registration under possible incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façades details of buildings in open LiDAR data and improving registration accuracy from up to 10 meters to less than half a meter compared to classic registration methods.

A heat transfer model, has determined millimeter crude oil thicknesses on saltwater at night. Model inputs are calibrated thermographic imagery, weather station data, metrological observations, and crude oil thermal conductivity. Outdoor field-testing was performed at the National Oil Spill Response & Renewable Energy Test Facility (Ohmsett) to determine model accuracy. Alaskan North Slope (ANS), Hoover Offshore Oil Pipeline System Blend (HOOPS), and ROCK crude oils were placed at varying mm depths. A roof-top mounted thermal camera measured the average oil surface temperature for each target and converted to oil spill thickness. The fidelity of the thickness measurements is dependent on the accurate measurement of the atmospheric and weather parameters, sea state, heat transfer constants, crude oil evaporation rates, and calibration and stability of the thermal camera.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

Rapid increase in digital data coupled with advances in deep learning algorithms is opening unprecedented opportunities for incorporating multiple data sources for modeling spatial dynamics of human infectious diseases. We used Convolutional Neural Networks (CNN) in conjunction with satellite imagery-based urban housing and socio-economic data to predict disease density in a developing country setting. We explored both single (uni) and multiple input (multimodality) network architectures for this purpose. We achieved maximum test set accuracy of 81.6 per cent using a single input CNN model built with one convolutional layer and trained using housing image data. However, this fairly good performance was biased in favor of specific disease density classes due to an unbalanced data set despite our use of methods to address the problem. These results suggest CNN are promising for modeling spatial dynamics of human infectious diseases, especially in a developing country setting. Urban housing signals extracted from satellite imagery seem suitable for this purpose, under the same context.

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

Digital-Radio-Frequency-Memory (DRFM) has emerged as an advanced technique to achieve diverse jamming signals, due to its capability of intercepting waveforms within a short time. Multiple-Input Multiple-Output (MIMO) radars can transmit agile orthogonal waveform sets for different pulses to combat the DRFM-based jamming, where any two groups of waveform sets are also orthogonal. In order to design multiple groups of orthogonal waveform sets at the same time, this article formulates a group orthogonal waveforms optimal design model, whose objec-tive function is the weighted sum of the intra- and inter-group orthogonal performances metrics. To solve this optimization problem, this article proposes a group orthogonal waveforms design algorithm. Based on a primal-dual type method and proper relaxations, the proposed algorithm transforms the original problem into a series of simple sub-problems. Numerical results demonstrate that the proposed algorithm has the ability to balance the intra- and inter-group orthogonal performances by adjusting the weighting factors, which is not available using other state-of-the-art orthogonal waveform design algorithms.

In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for that are its simplified transmitter architecture and lower linearity requirements, e.g., compared to orthogonal-frequency division multiplexing radars, as well as its high velocity unambiguity and multiple-input multiple-output operation capability that are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.

This paper considers the detection of fluctuating target in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter received from high-resolution radars and radars working at small grazing angle. Swerling type 1 is considered to describe the target fluctuation between scans. Conventional TBD techniques suffer from significant performance loss in heavy-tailed environments due to the more frequent occurrences of target-like outliers. In this paper, we resort to DP-TBD algorithm based on prior information, which can enhance the detection performance by using the environment and target fluctuating information during the integration process of TBD. Under non-Gaussian background, the expressions of the likelihood ratio merit function for Swerling type 1 target are derived first. However, the closed analytical form of the merit function is difficult to be obtained. In order to reduce the complexity of evaluating the merit function and the computational load, an efficient approximation method as well as a two-stage detection approach is proposed and used in the integration process. Finally, several numerical simulations of the new strategy and the comparisons are presented to verify that the proposed algorithm can improve the detection performance, especially for fluctuating target in heavy-tailed clutter.

Bistatic radar experiments have been used to study surface characteristics of extra-terrestrial bodies in the Solar System, including the Moon, Venus, Mars, and Titan. This paper proposes a 3D model to characterize the scattered field of a Gaussian rough surface on an extra-terrestrial body for an orbital bistatic radar configuration. Specifically, this model will investigate how the variability of surface roughness impacts the spectral broadening of the received signal using physical optics approximations and ray tracing on a surface model using a facet approach with Gaussian properties. A linear relationship between spectral broadening of the signal and surface roughness was found. This relationship is in line with results obtained by commonly used analytical models for bistatic radar on planetary surfaces.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

Background: 3D virtual reality (VR) Motionless Imagery Exercise through Avatar (MIEA) was developed to improve elderly’s psychological (cognitive and emotional) and physical health. This study observed the impact of this program on the cognitive functions and emotional well-being and the electrophysiological changes in older adults. Methods: This study was conducted using a randomized controlled trial design. The participants included 38 adults, 65 to 84 years of age, who were physically mobile and neurologically healthy. Among them, 19 participants were assigned to the experimental group, who underwent exercises using elderly avatars in a VR virtual exercise program. The other 19 participants were assigned to the control group who underwent the VR program. Both groups engaged in the VR intervention for 20 minutes three times a week, over six weeks. Results: The cognitive functions, including attention restraint, working memory, and phonemic fluency, of the experimental group showed significant improvements compared to the control group. Regarding emotional well-being, the experimental group showed greater self-efficacy. The electrophysiological changes revealed similar patterns to exercise-induced reactions in electromyography of the experimental group after intervention. Conclusions: These findings confirm that participation in 3D VR MIEA enhances cognitive functions and physical self-efficacy among the elderly. Therefore, 3D VR MIEA can be utilized in the rehabilitation therapy of patients or elderly with limited physical activity.

With the continuous improvement of people's quality of life and aesthetics, furniture products have higher requirements. Excellent solid wood chairs are the most representative products in furniture to enhance space and taste and whose design may significantly impact consumers' emo-tional experience and purchase decisions. This study aims to evaluate how the modeling imagery of solid wood chairs affects user preferences and emotional experiences. The development of the current era is inseparable from the analysis of big data. Firstly, a representative sample was ob-tained by multidimensional scaling (MDS), and the sample was analyzed and evaluated by fac-tor analysis. Moreover, five groups of adjective vocabulary were selected to describe the model-ing imagery of solid wood chairs, such as "balanced and coordinated", "unique and novel", "prac-tical and simple", "quality and detailed" and "traditional and plain". Further, the triangular fuzzy theory was applied to analyze and discuss the 12 types of solid wood chairs in the five groups of adjective vocabulary. Then the study verified that the differences in the evaluations of the 12 sam-ples in the "unique and novel" and "quality and detailed" groups were significant, but more minor differences in the groups of "traditional and plain", "balanced and coordinated" and "practical and simple". Through comprehensive comparison, five groups with similar modeling imagery were created, and solid wood chairs with different modeling imagery should be placed in suita-ble spaces. According to the results of this study, the evaluation of the modeling imagery of solid wood chairs can not only rely on subjective judgments but can be reasonably described through data analysis and mathematical algorithms. It can scientifically and effectively reflect the poten-tial perception needs of consumers on the modeling imagery of solid wood chairs, as well as help to improve the design efficiency of the furniture product development stage.

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having direct impact on current residents and future generations. A key problem in relation to slums is slum mapping. Without delineations of where all slum settlements are, informed decisions cannot be made by policymakers in order to benefit the most in need. Satellite images have been used in combination with machine learning models to try and fill the gap in data availability of slum locations. Deep learning has been used on RGB images with some success but since labeled satellite images of slums are relatively low quality and the physical/visual manifestation of slums significantly varies within and across countries, it is important to quantify the uncertainty of predictions for reliable application in downstream tasks. Our solution is to train Monte Carlo dropout U-Net models on multispectral 13-band Sentinel-2 images from which we can calculate pixelwise epistemic (model) and aleatoric (data) uncertainty in our predictions. We trained our model on labelled images of Mumbai and verified our epistemic and aleatoric uncertainty quantification approach using altered models trained on modified datasets. We also used SHAP values to investigate how the different features contribute towards the model’s predictions and this showed that certain short-wave infrared and red-edge image bands are powerful features for determining the locations of slums within images. Having created our model with uncertainty quantification, in the future it can be applied to downstream tasks and decision-makers will know where predictions have been made with low uncertainty, giving them greater confidence in its deployment.

Road surface monitoring more specifically crack detection on the surface of the road pavement is a complicated task which is found vital due to critical nature of roads as elements of transportation infrastructure. Cracks on the road pavement is detectable using remotely sensed imagery or car mounted platforms. UAV’s are also considered as useful tools for acquiring reliable information about the pavement of the road. In This paper, an automatic method for crack detection on the road pavement is proposed using acquired videos from UAV platform. Selecting key frames and generating Ortho-image, violating non road regions in the scene are removed. Then through an edge based approach hypothesis crack elements are extracted. Afterwards, through SVM based classification true cracks are detected. Developing the proposed method, the generated results show 75% accuracy in crack detection while less than 10% of cracks are omitted.

We apply deep convolutional neural networks (CNNs) to estimate wave breaking type from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. Whereas for a plunging breaker, the crest and back face of the wave are most important. This suggests that CNN-based models utilize the distinctive `streak' temperature patterns observed on the back face of plunging breakers for classification.

In recent years, using multispectral cameras on UAVs has provided an opportunity to capture separate bands that offer the extraction of spectral features used for early detection of diseased plants. One of the main steps in disease detection is radiometric calibration that converts digital numbers to reflectance values commonly using white reference panels. This paper focused on the necessity of radiometric calibration to distinguish disease trees in orchards based on aerial multi-spectral images. For this purpose, two study sites with various climate conditions and tree species as well as different disease types were selected where multispectral images were taken using a multirotor UAV. The impact of radiometric correction on plant disease detection was assessed in two ways: 1) comparison of separability between the healthy and diseased classes using T-test and entropy distances; 2) radiometric calibration effect on the accuracy of classification. The experimental result showed the insignificant effect of radiometric calibration on separability criteria. Furthermore, based on T-test and entropy distances criteria, NIR and R spectral features made highest distances between healthy and Greening infected citrus trees, respectively, at the first study site while NDRE and BNDVI spectral features made highest distances between healthy and peach leaf curl infected trees, respectively, at the other study site. In the second strategy, the experimental result showed that radiometric calibration had no effect on the accuracy of classification. As a result, the overall accuracy and kappa values for both un-calibrated and calibrated orthomosaic classifications of the citrus orchard were 96.6% and 0.94%, respectively, using five spectral bands as well as DVI, NDRE, NDVI and GNDVI vegetation indices using a random forest classifier. The experimental results were also similar at the other study site. Therefore, the overall accuracy and kappa values for both the un-calibrated and calibrated orthomosaic classifications were 96.1%, 0.92, respectively, using five spectral bands as well as NDRE, BNDVI, GNDVI, DVI, and NDVI vegetation indices.

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks whose automation was analyzed consisted in estimating changes in the roughness of a mountain streambed and the extent of floods from images. The Segment Anything Model (SAM) developed this year by Meta was used for this purpose. Images from many years from the Wielka Puszcza mountain stream located in the Polish Carpathians were used as the only input data. The model was not additionally trained for the described tasks. The model can be run in several modes, but the two most appropriate were used in this study. The first one is available in the form of a web application, while the second one is available in the form of a Jupyter notebook run in the Google Colab environment. Both methods do not require specialized knowledge and can be used by virtually any hydrologist. In the roughness estimation task, the average Intersection over Union (IoU) ranges from 0.55 for grass to 0.82 for shrubs/trees. Ultimately, it was possible to estimate the roughness coefficient of the mountain streambed between 0.027 and 0.059 based solely on image data. In the task of estimation of the flood extent, when selecting appropriate images, one can expect IoU at the level of at least 0.94, which seems to be an excellent result considering that SAM is a general-purpose segmentation model. It can therefore be concluded that SAM model can be a useful tool for a hydrologist.

The present study illustrates a brain-computer interface designed and developed to be wearable, portable, and user-friendly. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: the "neurofeedback" group, which performed motor imagery while receiving feedback, and the "control" group, which performed motor imagery with no feedback. Several questionnaires were administrated to participants aiming to investigate the usability of the proposed system and individual’s ability to imagine movements. The highest mean classification accuracy across subjects of control group was about 62 % with 3 % associated type A uncertainty, and 69 % with 3 % uncertainty for neurofeedback group. Moreover, in some cases the results were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study highlights the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, the results and the perceived usability pave the way for the employment of the proposed system in tele-rehabilitation.

Mapping and prediction of inundated areas is increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observed flooding patterns for training, calibration, validation and benchmarking. At regional to continental scale, satellite earth observation is the established method for surface water extent (SWE) mapping and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared to low-resolution SWE maps derived from satellite earth observation (EO). We conclude that UAS have significant potential for SWE mapping at intermediate scales, can bridge the scale gap between ground observations and satellite EO and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation.

Sun glint, i.e., direct solar radiation reflected from a water surface, negatively affects the accuracy of ocean color retrieval schemes if entering the field-of-view of the observing instrument. Herein, a simple and robust method to quantify the sun glint contribution to top-of-atmosphere (TOA) reflectances in the visible (VIS) and near-infrared (NIR) is proposed, exploiting concomitant observations of the sun glint’s morphology in the shortwave infrared (SWIR) characterized by reflectance contrasts typically higher than those resulting from other in-water or atmospheric processes. The proposed method, termed Glint Removal through Contrast Minimization (GRCM), requires high spatial resolution (ca. 10–50 m) imagery to resolve the sun glint’s characteristic morphology, meeting additional criteria on radiometric resolution and temporal delay between the individual band’s acquisitions. It has been applied with good success to a selection of Landsat 8 (L8) Operational Land Imager (OLI) scenes encompassing a wide range of environmental conditions in terms of observation geometry and glint intensity, as well as aerosol and Rayleigh optical depth. The method proposed herein is entirely image based and does not require ancillary information on the sea surface roughness or related parameters (e.g., surface wind), neither the presence of clear water areas in the image under consideration. Limitations of the proposed method are discussed, and its potential for sensors other than OLI and applications beyond glint removal is sketched.

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin-tungsten mine excavation faces using machine-learning classification. We compiled a set of hand samples of minerals of interest from a tin-tungsten mine and analyzed two types of hyperspectral images: 1) images acquired with a laboratory set-up under close-to-optimal conditions; and 2) scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450 – 950 nm and 950 – 1780 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten-tin mine faces.

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture used solves limitations of previous research and can be used at scale in commercial breeding programs.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, bio-mass estimation, etc. Deep Neural Networks (DNN) have shown superior results when comparing with conventional machine learning methods such as Multi-Layer Perceptron (MLP) in cases of huge input data. The objective of this research was to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, RGB channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Organisms are adapted to each other and the environment, not as tradition would have it, because natural selection made them that way, but because there is inbuilt striving towards stability and equilibrium. A General Theory of Behaviour connects imagery, affect and action with the central executive system we call Consciousness. The General Theory is founded on the assumption that the primary motivation of all of Consciousness and intentional behaviour is Psychological Homeostasis. Psychological homeostasis is as important to the organisation of mind and behaviour as Physiological Homeostasis is to the organisation of bodily systems. Consciousness processes quasi-perceptual images independently of the input to the retina and sensorium. Consciousness is the “I-Am” control centre for integration and regulation of (my) thoughts, (my) feelings and (my) actions with (my) conscious mental imagery as foundation stones. Imagery vividness, a combination of clarity and liveliness, is essential to imagining, remembering, thinking, predicting, planning and acting. Assessment of vividness using introspective report is validated by objective means such as fMRI. A significant body of work shows that vividness of visual imagery is determined by the similarity of neural responses in imagery to those occurring in perception of actual objects and performance of activities. I am Conscious, therefore I am.

Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e. tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m², and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV based approach for larger orchards. The different flying heights (i.e. 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.

This study proposed a spatially and temporally improving methodology adopting the Regional Frequency Analysis with L-moments approach to estimate rainfall quantiles from 22787 grids of radar rainfall in Taiwan for a 24-hour duration. Due to limited radar coverage in the eastern region, significant discordant grids were found in the coastal area of the eastern region. A K-means cluster analysis using scaled at-site characteristics was used to group the QPESUMS grids in Taiwan into 22 clusters/sub-regions based on their characteristics. Spatially, homogeneous subregions with QPESUMS data produce more detailed homogeneous subregions with clear and continuous boundaries, especially in the mountain range area where the number of rain stations is still very limited. According to the results of z-values and L-moment ratio diagrams, the Wakeby (WAK), Generalized Extreme Value (GEV), and Generalized Pareto (GPA) distributions of rainfall extremes fitted well for the majority of subregions. The Wakeby distribution was the dominant best-fitted distribution, especially in the central and eastern regions. The east of the northern part and southern part of Taiwan had the highest extreme rainfall for each return period. Both areas were frequently struck by typhoons. By using grid-based (at-site) as the basis for assessing regional frequency analysis, the results show that the regional approach in determining extreme rainfall is very suitable for large-scale applications and even better for smaller scales such as watershed areas. The spatial investigation was performed by establishing regions of interest in small subregions across the northern part. It showed that regionalization was correct and consistent.