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Remote Sensing
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Signal Processing Theory and Methods in Remote Sensing (Second Edition)
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Signal Processing Theory and Methods in Remote Sensing (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1536

Special Issue Editors

Dr. Shaohui Mei

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
Interests: remote sensing image processing; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Junhui Hou

E-Mail Website
Guest Editor
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong 999077, China
Interests: image/video representations and analysis; semi-supervised/unsupervised data modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Hu

E-Mail Website
Guest Editor
School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: multimedia computing; remote sensing applications; graph learning
Dr. Mingyang Ma

E-Mail
Guest Editor
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: remote sensing image processing; video understanding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of signal processing is closely intertwined with the advancement of remote sensing. The continual evolution of signal processing techniques provides essential tools for the processing and interpretation of remote sensing data, while the development of remote sensing technology offers rich data sources and practical application scenarios for signal processing. With the progress of remote sensing technology, the volume and variety of data acquired have steadily increased. The development of signal processing techniques provides robust tools and methods for handling these diverse data, including image processing, time-series analysis, feature extraction, pattern recognition, and more. Thus, the development of signal processing and remote sensing mutually reinforce each other, collectively driving the widespread application of remote sensing technology in fields such as earth science, environmental monitoring, and resource management.

This Special Issue aims to explore the latest advancements and innovative applications of signal processing theory and methods in remote sensing. We invite contributions focusing on innovative signal processing techniques for the enhancement, analysis, and interpretation of remote sensing data across different domains. Topics of interest include, but are not limited to, the following:

  • Feature extraction for remote sensing;
  • Time-series analysis for remote sensing observations;
  • Fusion techniques for multi-source remote sensing data;
  • Real-time signal processing for remote sensing;
  • Large-scale image processing for remote sensing;
  • Compressed sensing for remote sensing;
  • Deep learning approaches for remote sensing;
  • Signal processing platforms for remote sensing;
  • Signal processing in remote sensing applications.

Dr. Shaohui Mei
Dr. Junhui Hou
Dr. Kun Hu
Dr. Mingyang Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal processing
  • remote sensing
  • image processing
  • deep learning
  • information fusion
  • time-series analysis

Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 5550 KiB  
Article
GNSS/LiDAR/IMU Fusion Odometry Based on Tightly-Coupled Nonlinear Observer in Orchard
by Na Sun, Quan Qiu, Tao Li, Mengfei Ru, Chao Ji, Qingchun Feng and Chunjiang Zhao
Remote Sens. 2024, 16(16), 2907; https://doi.org/10.3390/rs16162907 - 8 Aug 2024
Viewed by 173
Abstract
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. [...] Read more.
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
(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))
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27 pages, 11527 KiB  
Article
Unpaired Remote Sensing Image Dehazing Using Enhanced Skip Attention-Based Generative Adversarial Networks with Rotation Invariance
by Yitong Zheng, Jia Su, Shun Zhang, Mingliang Tao and Ling Wang
Remote Sens. 2024, 16(15), 2707; https://doi.org/10.3390/rs16152707 - 24 Jul 2024
Viewed by 331
Abstract
Remote sensing image dehazing aims to enhance the visibility of hazy images and improve the quality of remote sensing imagery, which is essential for various applications such as object detection and classification. However, the lack of paired data in remote sensing image dehazing [...] Read more.
Remote sensing image dehazing aims to enhance the visibility of hazy images and improve the quality of remote sensing imagery, which is essential for various applications such as object detection and classification. However, the lack of paired data in remote sensing image dehazing enhances the applications of unpaired image-to-image translation methods. Nonetheless, the considerable parameter size of such methods often leads to prolonged training times and substantial resource consumption. In this work, we propose SPRGAN, a novel approach leveraging Enhanced Perlin Noise-Based Generative Adversarial Networks (GANs) with Rotation Invariance to address these challenges. Firstly, we introduce a Spatial-Spectrum Attention (SSA) mechanism with Skip-Attention (SKIPAT) to enhance the model’s ability to interpret and process spectral information in hazy images. Additionally, we have significantly reduced computational overhead to streamline processing. Secondly, our approach combines Perlin Noise Masks in pre-training to simulate real foggy conditions, thereby accelerating convergence and enhancing performance. Then, we introduce a Rotation Loss (RT Loss) to ensure the model’s ability to dehaze images from different angles uniformly, thus enhancing its robustness and adaptability to diverse scenarios. At last, experimental results demonstrate the effectiveness of SPRGAN in remote sensing image dehazing, achieving better performance compared to state-of-the-art methods. Full article
(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))
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21 pages, 4506 KiB  
Article
SREDet: Semantic-Driven Rotational Feature Enhancement for Oriented Object Detection in Remote Sensing Images
by Zehao Zhang, Chenhan Wang, Huayu Zhang, Dacheng Qi, Qingyi Liu, Yufeng Wang and Wenrui Ding
Remote Sens. 2024, 16(13), 2317; https://doi.org/10.3390/rs16132317 - 25 Jun 2024
Viewed by 724
Abstract
Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature [...] Read more.
Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature extraction in OOD becomes more challenging due to the diversity of object orientations. In this paper, we propose a semantic-driven rotational feature enhancement method, termed SREDet, to fully leverage the joint semantic and spatial information of oriented objects in the remote sensing images. We first construct a multi-rotation feature pyramid network (MRFPN), which leverages a fusion of multi-angle and multiscale feature maps to enhance the capability to extract features from different orientations. Then, considering feature confusion and contamination caused by the dense arrangement of objects and background interference, we present a semantic-driven feature enhancement module (SFEM), which decouples features in the spatial domain to separately enhance the features of objects and weaken those of backgrounds. Furthermore, we introduce an error source evaluation metric for rotated object detection to further analyze detection errors and indicate the effectiveness of our method. Extensive experiments demonstrate that our SREDet method achieves superior performance on two commonly used remote sensing object detection datasets (i.e., DOTA and HRSC2016). Full article
(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))
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