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Advances in AI-Driven Synthetic Aperture Radar (SAR): Data Processing to Automatic Interpretation

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

Deadline for manuscript submissions: 15 May 2026 | Viewed by 1508

Special Issue Editors

Prof. Dr. Fan Zhang

E-Mail Website
Guest Editor
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
Interests: synthetic aperture radar (SAR) signal processing; SAR image interpretation; high performance computing; SAR/optical remote sensing image processing; Artificial intelligence
Prof. Dr. Bing Han

E-Mail Website
Guest Editor
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: advanced spaceborne SAR signal processing; remote sensing information extraction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weixian Tan

E-Mail Website
Guest Editor
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Interests: two-dimensional (2D)/three-dimensional (3D) microwave imaging systems; image processing; signal processing and applications
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Yin

E-Mail Website
Guest Editor
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
Interests: synthetic aperture radar (SAR); polarimetric synthetic aperture radar

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) plays an indispensable role in modern remote sensing, providing robust imaging capabilities under diverse environmental and illumination conditions. Recent advances in artificial intelligence (AI), particularly in machine learning and data-driven modeling, have demonstrated significant promise in enhancing SAR data processing, facilitating automatic interpretation and broadening the scope of SAR applications.

This Special Issue invites contributions that explore how AI technologies are reshaping SAR data processing and interpretation, covering the full spectrum from foundational advances in signal and image processing to high-level semantic understanding and automated interpretation. Emphasis will be placed on approaches that not only advance AI methodology, but which also push the boundaries of remote sensing capabilities by leveraging the unique characteristics of SAR data. Topics may include, but are not limited to, the following:

  • AI-based SAR image enhancement and denoising;
  • Intelligent classification and segmentation of SAR imagery;
  • Target detection and recognition from SAR data;
  • Learning-based interferometric and polarimetric SAR processing;
  • AI-driven multi-sensor data fusion with SAR;
  • Domain adaptation and generalization across SAR scenes;
  • Large-scale SAR data analytics and automated mapping.

We look forward to receiving your contributions.

Prof. Dr. Fan Zhang
Prof. Dr. Bing Han
Prof. Dr. Weixian Tan
Dr. Qiang Yin
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

  • synthetic aperture radar (SAR)
  • AI-based synthetic aperture radar image processing
  • deep learning in remote sensing
  • automatic target recognition (ATR)
  • InSAR and PolSAR interpretation
  • synthetic aperture radar data fusion
  • semantic segmentation of synthetic aperture radar

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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26 pages, 12819 KB  
Article
Multiscale Attention-Enhanced Complex-Valued Graph U-Net for PolSAR Image Classification
by Wanying Song, Qian Liu, Kuncheng Pu, Yinyin Jiang and Yan Wu
Remote Sens. 2025, 17(24), 3943; https://doi.org/10.3390/rs17243943 - 5 Dec 2025
Viewed by 206
Abstract
The powerful graph convolutional network (GCN) for polarimetric synthetic aperture radar (PolSAR) image classification generally relies on real-valued features, ignoring the phase information and thus limiting the modeling of complex-valued (CV) polarization characteristics. To address this issue, this paper proposes a novel multiscale [...] Read more.
The powerful graph convolutional network (GCN) for polarimetric synthetic aperture radar (PolSAR) image classification generally relies on real-valued features, ignoring the phase information and thus limiting the modeling of complex-valued (CV) polarization characteristics. To address this issue, this paper proposes a novel multiscale attention-enhanced CV graph U-Net model, abbreviated as MAE-CV-GUNet, by embedding CV-GCN into a graph U-Net framework augmented with multiscale attention mechanisms. First, a CV-GCN is constructed based on the real-valued GCN, to effectively capture the intrinsic amplitude and phase information of the PolSAR data, along with the underlying correlations between them. This way can well lead to an improved feature representation for PolSAR images. Based on CV-GCN, a CV graph U-Net (CV-GUNet) architecture is constructed by integrating multiple CV-GCN components, aiming to extract multi-scale features and further enhance the ability to extract discriminative features in the complex domain. Then, a multiscale attention (MSA) mechanism is designed, enabling the proposed MAE-CV-GUNet to adaptively learn the importances of features at various scales, thereby dynamically fusing the multiscale information among them. The comparisons and ablation experiments on three PolSAR datasets show that MAE-CV-GUNet has excellent performance in PolSAR image classification. Full article
(This article belongs to the Special Issue Advances in AI-Driven Synthetic Aperture Radar (SAR): Data Processing to Automatic Interpretation)
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24 pages, 29785 KB  
Article
Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification
by Nana Jiang, Wenbo Zhao, Jiao Guo, Qiang Zhao and Jubo Zhu
Remote Sens. 2025, 17(15), 2663; https://doi.org/10.3390/rs17152663 - 1 Aug 2025
Cited by 2 | Viewed by 781
Abstract
Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based [...] Read more.
Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness. Full article
(This article belongs to the Special Issue Advances in AI-Driven Synthetic Aperture Radar (SAR): Data Processing to Automatic Interpretation)
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