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synthetic aperture radar Imaging Meets Deep Unfolded Learning: A comprehensive review

IEEE GEOSCIENCE AND remote sensing MAGAZINE 2025年第1期13卷 79-120页

作者： Wang, Mou Hu, Yifei Wei, Shunjun Shi, Jun Cui, Guolong Kong, Lingjiang Guo, Yongxin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China IEEE Piscataway NJ 08854 USA City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

synthetic aperture radar (SAR) can obtain highresolution images without being affected by environmental visibility. The compressed sensing (CS) technique is considered to be a strong candidate for simplifying SAR system complexity and improving imaging quality. With CS, SAR imaging is addressed by optimizing an augmented object function with data fidelity and feature-oriented priors, however, suffering from the time-consuming calculation and poor adaptability. Recently, an emerging technique dubbed deep unfolded/unrolled learning, or model-driven learning, offers promise in eliminating such issues by bridging the gap between learning framework and iterative algorithms. The increasing popularity of unfolded networks in SAR inverse problems also show their potential in developing efficient and accurate imaging algorithms. This paper surveys the SAR imaging algorithms based on deep unfolding techniques. We extensively cover different imaging regimes including conventional 2-D SAR, inverse SAR (ISAR), three-dimensional SAR (3-D SAR), and automotive radar imaging. On the algorithm side, the deep unfolding frameworks are mainly categorized according to the feature-oriented regularizers, wherein, their characteristics, principle, and feasibility in SAR inverse problems are discussed in detail. By reviewing the pioneer works, we discuss and reveal the current research stage in different tasks. Finally, the limitations, challenges, and opportunities of deep unfolding techniques are discussed in different radar imaging tasks.

关键词： Imaging radar imaging synthetic aperture radar radar polarimetry signal processing algorithms image resolution Three-dimensional displays signal resolution Data models apertures

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Collaborative Optimization for SAR image Despeckling With Structure Preservation

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Yu, Jindong Pan, Baojing Yu, Ze Li, Chunsheng Wu, Xiaojun Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

Speckle is a significant problem in synthetic aperture radar (SAR) image processing, as it deteriorates the texture details and thus limits the precision of its high-accuracy applications. The impact of speckle on SAR images is related to their structural characteristics, which usually classify the images into homogeneous regions, heterogeneous regions, and extremely heterogeneous regions. In the latter two regions, structures such as edges and strong point targets generate a more complex speckle distribution which differs from that in homogeneous regions. Despeckling filters based on multiplicative models perform well in homogeneous regions but exhibit poor performance in the other two types of regions. To solve this problem, a collaborative optimization for SAR image despeckling with structure preservation is proposed in this article, which leverages the coupling between the despeckling module and the structure extraction module. Guided by structure information, the proposed method achieves better preservation of edges and texture details while removing speckle. Experimental results based on real SAR images demonstrate that the proposed method effectively enhances the despeckling performance in all three types of regions.

关键词： Speckle radar polarimetry Optimization image edge detection Collaboration Feature extraction Data mining Noise synthetic aperture radar Optical sensors Collaborative optimization deep learning despeckling structure synthetic aperture radar (SAR)

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Integrated Physically Interpretable Model for SAR Target Recognition: Unified Fusion of Electromagnetic and Deep Features

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IEEE GEOSCIENCE AND remote sensing LETTERS 2025年 22卷

作者： Liao, Leiyao Hong, Zishuo Liu, Ziwei Zhang, Gengxin Nanjing Univ Posts & Telecommun Coll Commun & Informat Engn Nanjing 210003 Peoples R China

synthetic aperture radar (SAR) target recognition has been a prominent research topic in the field of remote sensing image processing. Traditional methods can extract physical features for SAR target recognition, but they are often time-consuming. Deep learning-based methods can learn representative features, but they often operate as black boxes and lack interpretability. This letter introduces an integrated physically interpretable model (IPIM) for SAR target recognition, which unifies electromagnetic and deep learning features. Our method is an integrative model built with an end-to-end mechanism, achieving promising performance and high time efficiency. Specifically, our method employs a deep-unfolding network to learn physical features by incorporating the generative process of complex SAR images. Additionally, a feature fusion module is designed to combine physical features, which reflect local target characteristics, with deep features that capture global information. Experimental results on a measured SAR image dataset demonstrate that our method effectively learns physical features and achieves high performance in target recognition.

关键词： Feature extraction Target recognition synthetic aperture radar Scattering radar polarimetry Point cloud compression Decoding Transformers image reconstruction Electromagnetics Complex networks feature fusion interpretability synthetic aperture radar (SAR) target recognition

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Difference-Complementary Learning and Label Reassignment for Multimodal Semi-Supervised Semantic Segmentation of remote sensing images

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IEEE TRANSACTIONS ON image processing 2025年 34卷 566-580页

作者： Han, Wenqi Jiang, Wen Geng, Jie Miao, Wang Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

The feature fusion of optical and synthetic aperture radar (SAR) images is widely used for semantic segmentation of multimodal remote sensing images. It leverages information from two different sensors to enhance the analytical capabilities of land cover. However, the imaging characteristics of optical and SAR data are vastly different, and noise interference makes the fusion of multimodal data information challenging. Furthermore, in practical remote sensing applications, there are typically only a limited number of labeled samples available, with most pixels needing to be labeled. Semi-supervised learning has the potential to improve model performance in scenarios with limited labeled data. However, in remote sensing applications, the quality of pseudo-labels is frequently compromised, particularly in challenging regions such as blurred edges and areas with class confusion. This degradation in label quality can have a detrimental effect on the model's overall performance. In this paper, we introduce the Difference-complementary Learning and Label Reassignment (DLLR) network for multimodal semi-supervised semantic segmentation of remote sensing images. Our proposed DLLR framework leverages asymmetric masking to create information discrepancies between the optical and SAR modalities, and employs a difference-guided complementary learning strategy to enable mutual learning. Subsequently, we introduce a multi-level label reassignment strategy, treating the label assignment problem as an optimal transport optimization task to allocate pixels to classes with higher precision for unlabeled pixels, thereby enhancing the quality of pseudo-label annotations. Finally, we introduce a multimodal consistency cross pseudo-supervision strategy to improve pseudo-label utilization. We evaluate our method on two multimodal remote sensing datasets, namely, the WHU-OPT-SAR and EErDS-OPT-SAR datasets. Experimental results demonstrate that our proposed DLLR model outperforms other relev

关键词： remote sensing Optical sensors Semantic segmentation Optical imaging radar polarimetry Adaptive optics synthetic aperture radar Accuracy Semantics Sensors Semi-supervised learning semantic segmentation multimodal fusion remote sensing

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Tomographic Inversion of Urban Area via Tikhonov Regularization and Bayesian Information Criterion

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2025年第2期9卷

作者： Bi, Hui Xu, Weihao Jin, Shuang Zhang, Jingjing Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Peoples R China

As an extension of synthetic aperture radar (SAR), SAR tomography (TomoSAR) technology can reduce the overlapping in 2-D SAR image and separate multiscatterer along the elevation direction, thereby achieving the high-precision 3-D reconstruction of the surveillance area. However, in practical spaceborne TomoSAR application, the quality of 3-D imaging is restricted by the limited number of baselines and their uneven distribution. Therefore, it is necessary to find advanced signal processing technology to achieve the target 3-D recovery when the amount of data is limited. In this letter, a novel Tikhonov regularization and Bayesian information criterion (BIC)-based nonparametric iterative adaptive approach (IAA), named RIAA-BIC, is proposed and introduced to the spaceborne data processing. Compared with conventional spectral estimation, compressed sensing-based, and IAA algorithms, the proposed method incorporates the Tikhonov regularization term to avoid the problem of solving nonlinear ill-posed equation in the elevation inversion. Furthermore, the BIC model selection tool can eliminate the false or weak scatterers, thereby improving the 3-D reconstruction accuracy of the surveillance area. Experimental results based on TerraSAR-X dataset verify the proposed method.

关键词： Three-dimensional displays Imaging Tomography Mathematical models Estimation Accuracy synthetic aperture radar apertures Surveillance Reflectivity Sensor signal processing Bayesian information criterion (BIC) iterative adaptive approach (IAA) synthetic aperture radar tomography (TomoSAR) Tikhonov regularization

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AlignMixup-based ship classification in SAR imagery

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signal image AND VIDEO processing 2025年第3期19卷 1-10页

作者： Chen, Yongjin An, Wentao Zou, Bin Ren, Peng China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Natl Satellite Ocean Applicat Serv Key Lab Space Ocean Remote Sensing & Applicat Beijing 100081 Peoples R China

This study improves ship classification in synthetic aperture radar (SAR) imagery, focusing on few-shot datasets. We propose a data augmentation strategy combining the AlignMixup method and a detail enhancement module to optimize convolutional neural network performance. AlignMixup integrates features at intermediate layers, capturing structural information, while the detail enhancement module highlights high-frequency details to improve ship feature recognition in SAR images. Experiments on small sample datasets show that our method increases classification accuracy by a significant margin and remains practical under data-limited conditions.

关键词： Data enhancement AlignMixup Ship classification SAR

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Deep Learning-Based Methods for Lithology Classification and Identification in remote sensing images

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IEEE ACCESS 2025年 13卷 3038-3050页

作者： Zhang, Zhijun Wang, Ming Qi, Yueji Su, Xiaoqin Kong, Di China Univ Geosci Wuhan Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Hubei Peoples R China China Geol Survey Langfang Integrated Nat Resources Survey Ctr Langfang 065000 Hebei Peoples R China China Geol Survey Xining Ctr Integrated Nat Resources Survey Xining 810000 Qinghai Peoples R China China Geol Survey Geophys Survey Ctr Langfang 065000 Hebei Peoples R China

This study presents a deep learning model that integrates Vision Transformers (ViT) with Fourier spectral filtering for remote sensing lithology classification. The model automates the process of identifying and classifying various rock types in remote sensing images, addressing a multi-class classification challenge. It utilizes ViT for feature extraction, enhanced by pretrained weights for improved efficiency and accuracy in recognizing geographical features. Fourier spectral filtering further augments the model by leveraging frequency domain information for accurate classification. The model preprocesses images, extracts spatial features, applies spectral filtering, and employs a classification head to predict rock types. Optimization of parameters through backpropagation and gradient descent methods, coupled with regularization strategies, aims to prevent overfitting and ensure generalizability. This approach combines deep learning's capability for feature extraction with the analytical power of signal processing, offering a significant advancement for automatic rock type classification in remote sensing.

关键词： Rocks remote sensing Feature extraction Geology Accuracy radar polarimetry Deep learning Filtering Data mining Surveys Lithography Classification algorithms synthetic aperture radar Speckle Noise measurement remote sensing image lithology classification deep learning technology SAR image speckle noise

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Sparse SAR Imaging and Doppler Rate Estimation for Azimuth Downsampled Echo Data via Complex Approximated Message Passing

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Zhu, Ziyi Bi, Hui Zhang, Jingjing Yu, Deshui Hong, Wen Zhang, Bingchen Wu, Yirong Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China

Airborne synthetic aperture radar (SAR) systems are commonly susceptible to trajectory deviations, resulting in distinct azimuth phase errors in the collected echo. SAR autofocus technology can compensate for phase error and produce a well-focused image based on echo data. However, due to the influence of unfavorable factors such as radar interruption and electromagnetic interference, the echo data may be downsampled in azimuth, which reduces the phase error estimation accuracy of traditional autofocus methods. By introducing compressed sensing (CS) to SAR data processing, sparse SAR imaging shows outstanding performance in acquiring high-resolution images utilizing downsampled echo. However, the phase error existing in the azimuth direction will reduce the sparsity of the observed scene, and the precision of sparse reconstruction is consequently decreased. This article aims to enhance the Doppler rate error estimation precision when echo is downsampled in azimuth and proposes a novel sparse imaging method combined with Doppler rate estimation. During each iteration of the complex approximated message passing (CAMP) algorithm, the Doppler rate error is estimated according to the nonsparse solution by the fractional Fourier transform (FrFT). Then, the phase error is compensated to the nonsparse solution, and the azimuth matched filtering (MF) operator is upgraded. The aforementioned steps are performed iteratively until a well-focused sparse SAR image is generated. It should be noted that the Armijo rule and the random sample consensus algorithm (RANSAC) are introduced to guarantee the fast and precise reconstruction of the observed scene. Experiments from simulated and airborne data prove the enhancement in Doppler rate estimation precision by the proposed method than traditional estimator when used data are azimuth downsampled.

关键词： Azimuth downsampled data compressed sensing (CS) Doppler rate estimation sparse synthetic aperture radar (SAR) imaging Azimuth downsampled data compressed sensing (CS) Doppler rate estimation sparse synthetic aperture radar (SAR) imaging

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L1-and F-norm joint edge preservation optimization algorithm for complex image sparse SAR reconstruction imaging model

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INTERNATIONAL JOURNAL OF remote sensing 2025年第7期46卷 2846-2865页

作者： Wang, Fengyun Liu, Guolin Tao, Qiuxiang Wang, Luyao Ren, Yaoyao Zhou, Yiming Shandong Univ Sci & Technol Coll Geodesy & Geomatics 579 Qianwangang Rd Qingdao 266590 Peoples R China Beijing Xingyao Data Technol Co Ltd Remote Sensing Div Beijing Peoples R China Shandong Inst Geol Surveying & Mapping Jinan Peoples R China

synthetic aperture radar (SAR) is widely used for ground observations owing to its all-weather and all-time capabilities. Sparse SAR reconstruction imaging, which can enhance SAR imaging performance, has emerged as a significant research area. The traditional sparse SAR reconstruction imaging method was successfully applied to raw SAR data processing. However, the computational complexity makes sparse reconstruction imaging difficult for large scenes. Meanwhile, because raw data are usually kept confidential, only limited data are available. To address the issue of how to use publicly available complex image data for sparse reconstruction and effectively improve image quality, this study proposes a joint L1- and F-norm edge-preservation-based sparse SAR reconstruction algorithm for complex images. The algorithm uses the gradient descent (GD) technique to optimize the L1-norm regularization and reconstruct the overall image information;the iterative soft thresholding algorithm (ISTA) to optimize the F-norm regularization and reconstruct the high-frequency image information;and fuses the two sets of reconstructed information to obtain SAR reconstructed images with characteristics such as edge preservation, clear texture, and high quality. The results show that, compared with traditional matched filtering (MF) imaging results, the joint L1- and F-norm results obtained for complex images have lower sidelobes, higher signal-to-noise ratio, and better target resolution ability;compared with the F-norm-based sparse SAR reconstruction imaging results, the joint L1- and F-norm method provides images with better edge texture clarity.

关键词： Gaofen-3 regularization sparse imaging synthetic aperture radar (SAR)

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A quadrilateral filtering algorithm for video SAR noise reduction

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remote sensing LETTERS 2025年第2期16卷 146-155页

作者： Wang, Gang Xue, Weibao Jia, Lu Ai, Jiaqiu Wang, Wei Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230601 Peoples R China Hefei Univ Technol Intelligent Interconnected Syst Lab Anhui Prov Hefei Peoples R China Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China

A quadrilateral filtering algorithm is proposed in this letter to reduce noise in video synthetic aperture radar (video SAR). The novel filtering approach draws inspiration from the traditional bilateral filter and effectively exploits the similarities in both greyscale and geometric levels. Moreover, the proposed quadrilateral filter incorporates the similarity of time domain and rank-ordered absolute difference (ROAD) statistic for detecting the abundant speckle and impulse noise in video SAR frames. An impulse detector, which is related to the distribution of ROAD, is added in the filtering process to remove the outliers in the intensity domain after time redundancy processing with the target frame image. The proposed quadrilateral filter can obviously smooth various sources of noise, including strong speckle noise and impulse noise, while considering the details of each image frame of the video SAR. The new algorithm has been verified to achieve superior filtering performance with equivalent number of looks (ENL) values increased by 25% on average comparing with other widely used algorithms.

关键词： Video synthetic aperture radar (video SAR) quadrilateral filter time redundancy processing rank-ordered absolute difference (ROAD) statistic impulse detector

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