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Remote Sensing
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SAR Images Processing and Analysis (3rd Edition)
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SAR Images Processing and Analysis (3rd Edition)

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1593

Special Issue Editors

Dr. Hongwei Dong

E-Mail Website
Guest Editor
The National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Interests: convolutional neural network; synthetic aperture radar; image classification; polarimetric synthetic aperture radar; deep learning
Dr. Lingyu Si

E-Mail Website
Guest Editor
The National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Interests: intelligent information processing; multi-modal data fusion; target perception
Special Issues, Collections and Topics in MDPI journals
Dr. Tong Gao

E-Mail Website
Guest Editor
College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
Interests: machine learning; small object detection; feature fusion; domain adaptation; remote sensing image interpretation and processing

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) is an indispensable active remote sensing technology with all-day and all-weather Earth observation capabilities, playing a pivotal role in both military and civilian fields such as target reconnaissance, environmental monitoring and geological exploration. However, SAR image processing and analysis face inherent challenges: coherent speckle noise degrades image quality, while limited annotated datasets and poor open-source ecosystems restrict algorithm innovation. Additionally, traditional methods suffer from high annotation dependence, weak generalization and inadaptability to complex scenarios. Addressing these issues is critical to unlocking SAR’s full potential in intelligent interpretation.

We are pleased to invite you to showcase cutting-edge advancements in SAR image processing and analysis, bridging gaps between theoretical innovation and practical application. Aligned with Remote Sensing’s scope—covering signal processing, data interpretation and remote sensing applications—it will integrate interdisciplinary breakthroughs to advance the field’s technological maturity and ecological construction.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: self-supervised learning for SAR noise suppression; foundation models for target recognition; small-sample/class-imbalanced SAR image classification; multi-modal SAR data fusion; and open datasets/evaluation benchmarks. We welcome original research articles presenting novel algorithms/models, reviews synthesizing domain progress and technical notes on open-source tools or datasets.

Dr. Hongwei Dong
Dr. Lingyu Si
Dr. Tong Gao
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 250 words) can be sent to the Editorial Office for assessment.

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

  • SAR image processing
  • target recognition
  • self-supervised learning
  • foundation model
  • small-sample learning
  • speckle noise suppression
  • remote sensing interpretation

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Related Special Issue

Published Papers (2 papers)

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Research

25 pages, 29036 KB  
Article
Task-Oriented Unsupervised SAR Image Enhancement with Semantic Preservation for Robust Target Recognition
by Chengyu Wan, Siqian Zhang, Lingjun Zhao, Tao Tang and Gangyao Kuang
Remote Sens. 2026, 18(6), 930; https://doi.org/10.3390/rs18060930 - 19 Mar 2026
Viewed by 253
Abstract
Synthetic aperture radar (SAR) images often suffer from coupled degradations such as speckle noise, background clutter, and system disturbances, which distort target structure and reduce feature discriminability for target recognition. Most existing enhancement methods typically optimize perceptual quality and may produce visually appealing [...] Read more.
Synthetic aperture radar (SAR) images often suffer from coupled degradations such as speckle noise, background clutter, and system disturbances, which distort target structure and reduce feature discriminability for target recognition. Most existing enhancement methods typically optimize perceptual quality and may produce visually appealing yet recognition-inconsistent results, especially when paired supervision is unavailable. To address this, an unsupervised SAR image quality enhancement framework is proposed in this study, formulating the degradation as a domain shift problem between low- and high-quality SAR data. A DualGAN-based architecture is adopted to learn bidirectional mappings with reconstruction regularization, enabling enhancement without paired samples. To explicitly preserve task-relevant features and enforce structural consistency, a segmentation-guided recognition-oriented constraint is introduced to embed task awareness into the enhancement process. Furthermore, to mitigate semantic drift during unpaired translation, a semantic preservation constraint based on contrastive learning is proposed to align the enhanced, original, and smoothed images, which can maintain semantic fidelity and reinforce structural cues. Experimental results demonstrate that the proposed framework effectively bridges the domain gap between low- and high-quality SAR images, producing semantically consistent enhancement and improving robustness in target recognition. Evaluations on the GMVT dataset show that the proposed method achieves an average recognition accuracy improvement of over 10% across six recognition networks and four imaging conditions. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (3rd Edition))
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24 pages, 11779 KB  
Article
Effective SAR Image Despeckling Using Noise-Guided Transformer and Multi-Scale Feature Fusion
by Linna Zhang, Le Zheng, Yuxin Wen, Fugui Zhang, Fuyu Bo and Yigang Cen
Remote Sens. 2025, 17(23), 3863; https://doi.org/10.3390/rs17233863 - 28 Nov 2025
Cited by 1 | Viewed by 835
Abstract
Speckle noise is a significant challenge in synthetic aperture radar (SAR) images, severely degrading the visual quality and compromising subsequent image interpretation tasks. While existing despeckling methods can reduce noise, they often fail to strike a appropriate balance between noise suppression and the [...] Read more.
Speckle noise is a significant challenge in synthetic aperture radar (SAR) images, severely degrading the visual quality and compromising subsequent image interpretation tasks. While existing despeckling methods can reduce noise, they often fail to strike a appropriate balance between noise suppression and the preservation of fine image details. To address this issue, in this paper, we propose a novel SAR image despeckling method that leverages both structural image priors and noise distribution characteristics in an end-to-end framework. Our approach consists of two key components: a dual-branch subnet for coarse despeckling and noise estimation, and a noise-guided Transformer-based subnet for final image refinement. The dual-branch subnet decouples the tasks of noise estimation and despeckling, improving both noise suppression accuracy and structural detail preservation. Furthermore, a combination of grouped pooling attention (GPA) and context-aware fusion (CAF) modules enables effective multi-scale feature fusion by jointly capturing local details and global contextual information. The noise estimation branch generates adaptive priors that guide the Transformer refinement, which incorporates deformable convolutions and a masked self-attention mechanism to selectively focus on relevant image regions. Extensive experiments conducted on both synthetic and real SAR datasets demonstrate that the proposed method consistently outperforms current state-of-the-art methods, achieving superior speckle suppression while preserving fine details more effectively. Full article
(This article belongs to the Special Issue SAR Images Processing and Analysis (3rd Edition))
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