Search Results (34,215)

High-repetitive features in unstructured environments and frequent signal loss of the Global Navigation Satellite System (GNSS) severely limits the development of autonomous robot localization in orchard settings. To address this issue, we propose a LiDAR-based odometry pipeline GLIO, inspired by KISS-ICP and DLIO. GLIO is based on a nonlinear observer with strong global convergence, effectively fusing sensor data from GNSS, IMU, and LiDAR. This approach allows for many potentially interfering and inaccessible relative and absolute measurements, ensuring accurate and robust 6-degree-of-freedom motion estimation in orchard environments. In this framework, GNSS measurements are treated as absolute observation constraints. These measurements are tightly coupled in the prior optimization and scan-to-map stage. During the scan-to-map stage, a novel point-to-point ICP registration with no parameter adjustment is introduced to enhance the point cloud alignment accuracy and improve the robustness of the nonlinear observer. Furthermore, a GNSS health check mechanism, based on the robot’s moving distance, is employed to filter reliable GNSS measurements to prevent odometry crashed by sensor failure. Extensive experiments using multiple public benchmarks and self-collected datasets demonstrate that our approach is comparable to state-of-the-art algorithms and exhibits superior localization capabilities in unstructured environments, achieving an absolute translation error of 0.068 m and an absolute rotation error of 0.856°. Full article

In the field of object detection, small object detection in remote sensing images is an important and challenging task. Due to limitations in size and resolution, most existing methods often suffer from localization blurring. To address the above problem, this paper proposes a remote sensing small object detection network based on adaptive channel attention and large kernel adaptation. This approach aims to enhance multi-channel information mining and multi-scale feature extraction to alleviate the problem of localization blurring. To enhance the model’s focus on the features of small objects in remote sensing at varying scales, this paper introduces an adaptive channel attention block. This block applies adaptive attention weighting based on the input feature dimensions, guiding the model to better focus on local information. To mitigate the loss of local information by large kernel convolutions, a large kernel adaptive block is designed. The block dynamically adjusts the surrounding spatial receptive field based on the context around the detection area, improving the model’s ability to extract information around remote sensing small objects. To address the recognition confusion during the sample classification process, a layer batch normalization method is proposed. This method enhances the consistency analysis capabilities of adaptive learning, thereby reducing the decline in the model’s classification accuracy caused by sample misclassification. Experiments on the DOTA-v2.0, SODA-A and VisDrone datasets show that the proposed method achieves state-of-the-art performance. Full article

Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China’s diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km², 31.53 km², and 21.98 km², respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A^−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km², respectively, with a gully density of 1.65, 5.25, and 0.50 km km⁻², and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr⁻¹, significantly surpassing HL (12.24 m yr⁻¹) and ML (7.11 m yr⁻¹). Areal erosion rates were highest in HL (277.79 m² yr⁻¹) and lowest in YKS (105.22 m² yr⁻¹), with ML intermediate at 243.36 m² yr⁻¹. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr⁻¹) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A^−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China. Full article

Accurate predictions are crucial for addressing the challenges posed by climate change. Given South Korea’s location within the East Asian summer monsoon domain, characterized by high spatiotemporal variability, enhancing prediction accuracy for regions experiencing heavy rainfall during the summer monsoon is essential. This study aims to derive temporal weighting functions using hybrid surface rainfall radar-observation data as the target, with input from two forecast datasets: the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) and the KLAPS Forecast System. The results indicated that the variability in the optimized parameters closely mirrored the variability in the rainfall events, demonstrating a consistent pattern. Comparison with previous blending results, which employed event-type-based weighting functions, showed significant deviation in the average AUC (0.076) and the least deviation (0.029). The optimized temporal weighting function effectively mitigated the limitations associated with varying forecast lead times in individual datasets, with RMSE values of 0.884 for the 1 h lead time of KLFS and 2.295 for the 4–6 h lead time of MAPLE. This blending methodology, incorporating temporal weighting functions, considers the temporal patterns in various forecast datasets, markedly reducing computational cost while addressing the temporal challenges of existing forecast data. Full article

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R² = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R² = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R² = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. Full article

The accurate measurement of water vapor is essential for research about and the applications of meteorology, climatology, and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) can be retrieved with the precise point positioning (PPP) software (XTW-PPP version 0.0). The experiment was conducted in Beijing in January 2023. Three solutions were designed with PPP using the BeiDou system only, the GPS system only, and the BeiDou-GPS combined solution. Real-time PWVs for the three solutions were validated with the ERA5 reanalysis data. Between the PWV values from the single BeiDou and ERA5, there was a bias of 0.7 mm and an RMSE of 1.8 mm. For the GPS case, the bias was 0.73 mm and the RMSE was 1.97 mm. The biases were less than 1 mm and RMSEs were less than 2 mm. Both the BeiDou and the GPS processing performed very well. But little improvement was found for the BeiDou-GPS combined solution, compared with the BeiDou system-only and the GPS system-only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with a single system at the first step and then obtain the optimization by Bayesian model averaging. Full article

Addressing the challenge of radar detection of small targets under sea clutter, target detection methods based on a three-dimensional feature space have shown effectiveness. However, their application has revealed several problems, including high dependency on linear relationships between features for dimensionality reduction, unclear reduction objectives, and spatial divergence of target samples, which limit detection performance. To mitigate these challenges, we constructed a feature density distance metric employing copula functions to quantitatively describe the classification capability of multidimensional features to distinguish targets from sea clutter. On the basis of this, a lightweight nonlinear dimensionality reduction network utilizing a self-attention mechanism was developed, optimally re-expressing multidimensional features into a three-dimensional feature space. Additionally, a concave hull classifier using feature sample distance was proposed to mitigate the negative impact of target sample divergence in the feature space. Furthermore, multivariate autoregressive prediction was used to optimize features, reducing erroneous decisions caused by anomalous feature samples. Experimental results using the measured data from the SDRDSP public dataset demonstrated that the proposed detection method achieved a detection probability more than 4% higher than comparative methods under Sea State 5, was less affected by false alarm rates, and exhibited superior detection performance under different false alarm probabilities from 10⁻³ to 10⁻¹. Full article

Brazil, a leading rice producer globally, faces challenges in systematically mapping its diverse rice fields due to varying cropping systems, climates, and planting calendars. Existing rice mapping methods often rely on complex techniques like deep learning or microwave imagery, posing limitations for large-scale mapping. This study proposes a novel approach utilizing Sentinel-2 spectral–temporal metrics (STMs) in conjunction with a random forest classifier for rice paddy mapping. By extracting diverse STMs and training both regional and global classifiers, we validated the method across independent areas. While regional models tended to overestimate rice areas, the global model effectively reduced discrepancies between our data and the reference maps, achieving an overall classifier accuracy exceeding 80%. Despite the need for further refinement to address confusion with other crops, STM exhibits promise for national-scale rice paddy mapping in Brazil. Full article

Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256 × 256 pixels and a mono-channel panchromatic image of 1024 × 1024 pixels. To avoid irreversible loss of spectral information and achieve a high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for pansharpening. Multispectral images are progressively upsampled while panchromatic images are downsampled. Corresponding multispectral features and panchromatic features at the same scale are then fused in a cascaded manner to obtain more robust features. Extensive experiments validate the effectiveness of CMFNet. Full article

Monitoring air pollution is important for human health and the environment. Previous studies on the prediction of air pollutants from satellite images have employed machine learning, yet there are few enhancements to the constructure of model. Moreover, the existing models have been successful in predicting pollutants like PM_2.5, PM₁₀, and O₃. They have not been as effective in predicting other primary air pollutants. To improve the overall prediction performance of the existing model, a novel residual learning backpropagation model, abs. as BresNet, has been proposed in this research. This model has revealed the availability to precisely predict the ground-surface concentration of the six primary air pollutants, PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂, based on the satellite imagery of MODIS AOD. Two of the most commonly used machine learning models so far, viz. the multilayer backpropagation neural network (MLBPN) and random forest (RF), were employed as the control. In the conducted experiments, the proposed BresNet model demonstrated significant improvements of 18.75%/31.94%, 33.82%/85.71%, 15.00%/35.29%, 39.06%/134.21%, 23.23%/68.00%, and 137.14%/260.87% in terms of R² for the six primary air pollutants, compared to the RF/MLBPN model. Moreover, the convergence speed and loss function of the BresNet model compared to that of the MLBPN decreased by 55.15%, revealing superior convergence speed with the lower loss function. Full article

The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, due to the complexity of training in tracking tasks, existing models strive to improve tracking performance within limited scales, making it challenging to directly apply lightweight designs. To address these challenges, we introduce an efficient attention mechanism known as Macaron Attention, which we integrate into the existing UAV tracking framework to enhance the model’s discriminative ability within these constraints. Specifically, our attention mechanism comprises three components, fixed window attention (FWA), local squeezing global attention (LSGA), and conventional global attention (CGA), collectively forming a Macaron-style attention implementation. Firstly, the FWA module addresses the multi-scale issue in UAVs by cropping tokens within a fixed window scale in the spatial domain. Secondly, in LSGA, to adapt to the scale variation, we employ an adaptive clustering-based token aggregation strategy and design a “window-to-window” fusion attention model to integrate global attention with local attention. Finally, the CGA module is applied to prevent matrix rank collapse and improve tracking performance. By using the FWA, LSGA, and CGA modules, we propose a brand-new tracking model named MATrack. The UAV123 benchmark is the major evaluation dataset of MATrack with 0.710 and 0.911 on success and precision, individually. Full article

Advanced waveform design schemes have been widely employed for radar and sonar remote sensing analysis such as target detection and separation, where significant range sidelobe is a main factor that limits the improvement of analysis performance. As an extensional type of Golay complementary waveforms, complementary sets are a waveform design scenario of concern that shows more diversity in the design of transmission order, and results in a different distribution of range sidelobes. This work proposes an oversampled generalized Prouhet–Thue–Morse (OGPTM) method for the transmitted signal design of complementary sets, with comprehensive analysis to the influence on the sidelobe distribution. Based on this idea and our previous work, we further put forward a pointwise multiplication processor (PMuP) to integrate two delay-Doppler maps of oversampled complementary sets, which achieve much better sidelobe suppression performance on high-speed target detection with range migration. Full article

Oases play a crucial role in arid regions within the human–environmental system, holding significant ecological and biological importance. The Oasis Cold Island Effect (OCIE) represents a distinct climatic feature of oases and serves as a vital metric for assessing oasis ecosystems. Previous studies have overlooked the spatial extent of the Oasis Cold Island Effect (OCIE), specifically the boundary delineating areas influenced and unaffected by oases. This boundary is defined as the Oasis Cold Island Footprint (OCI FP). Utilizing Logistic modeling and MODIS data products, OCI FPs were calculated for the Ejina Oasis from 2000 to 2019. The assessment results underscore the accuracy and feasibility of the methodology, indicating its potential applicability to other oases. Spatial and temporal distributions of OCI FPs and the intensity of the Oasis Cold Island Effect Intensity (OCIEI) in the Ejina Oasis were analyzed, yielding the following findings: (1) OCI FP area and complexity were smallest in summer and largest in autumn. (2) Over the period 2000–2019, OCI FPs exhibited a pattern of increase, decrease, and subsequent increase. (3) OCIEI peaks in summer and reaches its lowest point in winter. Lastly, the study addresses current limitations and outlines future research objectives. Full article

Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between climate change and human activity has hindered comprehensive assessments of future ecological sustainability, leaving it inadequately investigated thus far. This study aimed to assess future ecological sustainability shaped by the Shared Socioeconomic Pathways (SSPs) using remote sensing data from a typical arid farming–pastoral zone located at the northern foot of Yinshan Mountain (NFYM), Inner Mongolia, China. Five machine learning models were employed to evaluate the relationship between ecological sustainability and its driving factors. The results indicate that (1) overall ecological sustainability initially decreased and then increased during 2003–2022; (2) the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4) mode and random forest model demonstrated the best performance in climate and ecological sustainability simulations; and (3) the annual change rates of ecological sustainability from 2023 to 2099 are projected to be +0.45%, −0.05%, and −0.46% per year under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, suggesting that stringent environmental policies can effectively enhance ecological sustainability. The proposed framework can assist decision-makers in understanding ecological changes under different SSPs and calls for strategies to enhance ecosystem resilience in the NFYM and similar regions. Full article

The vision transformer (ViT) has demonstrated performance comparable to that of convolutional neural networks (CNN) in the hyperspectral image classification domain. This is achieved by transforming images into sequence data and mining global spectral-spatial information to establish remote dependencies. Nevertheless, both the ViT and CNNs have their own limitations. For instance, a CNN is constrained by the extent of its receptive field, which prevents it from fully exploiting global spatial-spectral features. Conversely, the ViT is prone to excessive distraction during the feature extraction process. To be able to overcome the problem of insufficient feature information extraction caused using by a single paradigm, this paper proposes an MLP-mixer and a graph convolutional enhanced transformer (MGCET), whose network consists of a spatial-spectral extraction block (SSEB), an MLP-mixer, and a graph convolutional enhanced transformer (GCET). First, spatial-spectral features are extracted using SSEB, and then local spatial-spectral features are fused with global spatial-spectral features by the MLP-mixer. Finally, graph convolution is embedded in multi-head self-attention (MHSA) to mine spatial relationships and similarity between pixels, which further improves the modeling capability of the model. Correlation experiments were conducted on four different HSI datasets. The MGEET algorithm achieved overall accuracies (OAs) of 95.45%, 97.57%, 98.05%, and 98.52% on these datasets. Full article