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

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Special Issue Editors

Prof. Dr. Fan Zhang

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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

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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

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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

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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

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24 pages, 29785 KiB

Open AccessArticle

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

Viewed by 287

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 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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