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CR-DEQ-SAR: A Deep Equilibrium Sparse SAR Imaging Method for Compound Regularization

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 4680-4695页

作者： Zhou, Guoru Zuo, Yixin Zhang, Zhe Zhang, Bingchen Wu, Yirong Chinese Acad Sci Key Lab Geospatial Informat Proc & Applicat Syst Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Res Dept Cyber Electromagnet Space Informat Techno Beijing 100190 Peoples R China Suzhou Aerosp Informat Res Inst Suzhou Key Lab Microwave Imaging Proc & Applicat T Suzhou 215128 Peoples R China

synthetic aperture radar (SAR) is a microwave remote sensing technology offering all-weather, high-resolution imaging. The rising demand for high precision and real-time processing under complex conditions in resource-constrained environments has spurred interest in deep network-based SAR imaging, which combines traditional sparse SAR imaging methods with deep learning to optimize parameters and scene features while retaining physical model interpretability and enabling fast inference. However, the single regularization cannot entirely capture the features of complex observation scenes, and network architectures based on iterative unfolding often face memory and numerical precision constraints during training. In this article, we propose a deep equilibrium sparse SAR Imaging method for compound regularization, integrating sparse and implicit regularizations to better capture complex scene features. The deep equilibrium model (DEQ) serves as a novel deep network framework that directly computes fixed points using analytical methods, theoretically allowing for infinite forward iterations while maintaining constant memory requirements. This is particularly advantageous in memory-intensive SAR imaging applications. Finally, we validate the effectiveness and superiority of the proposed method through experiments on real SAR scenes. The experimental results show that the proposed method outperforms existing deep learning-based SAR imaging methods regarding reconstruction performance and memory usage.

关键词： radar polarimetry Imaging Compounds Iterative methods radar imaging image reconstruction synthetic aperture radar Network architecture Microwave imaging Azimuth Compound regularization deep equilibrium model (DEQ) sparse microwave imaging synthetic aperture radar (SAR)

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Joint compression and despeckling by SAR representation learning

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2025年 220卷 524-534页

作者： Amao-Oliva, Joel Foix-Colonier, Nils Sica, Francescopaolo German Aerosp Ctr Microwaves & Radar Inst Munchener Str 20 D-82234 Wessling Germany Nantes Univ Ecole Cent Nantes CNRS UMR 6004LS2N 1 Rue Noe F-44000 Nantes France Univ Bundeswehr Munich Dept Aerosp Engn Werner Heisenberg Weg 39 D-85577 Neubiberg Germany

synthetic aperture radar (SAR) imagery is a powerful and widely used tool in a variety of remote sensing applications. The increasing number of SAR sensors makes it challenging to process and store such a large amount of data. In addition, as the flexibility and processing power of on-board electronics increases, the challenge of effectively transmitting large images to the ground becomes more tangible and pressing. In this paper, we present a method that uses self-supervised despeckling to learn a SAR image representation that is then used to perform image compression. The intuition that despeckling will additionally improve the compression task is based on the fact that the image representation used for despeckling forms an image prior that preserves the main image features while suppressing the spatially correlated noise component. The same learned image representation, which can already be seen as the output of a data reduction task, is further compressed in a lossless manner. While the two tasks can be solved separately, we propose to simultaneously training our model for despeckling and compression in a self-supervised and multi-objective fashion. The proposed network architecture avoids the use of skip connections by ensuring that the encoder and decoder share only the features generated at the lowest network level, namely the bridge, which is then further transformed into a bitstream. This differs from the usual network architectures used for despeckling, such as the commonly used Deep Residual U-Net. In this way, our network design allows compression and reconstruction to be performed at two different times and locations. The proposed method is trained and tested on real data from the TerraSAR-X sensor (downloaded from https://***/eogateway/catalog/terrasarx-esa-archive). The experiments show that joint optimization can achieve performance beyond the state-ofthe-art for both despeckling and compression, represented hereby the MERLIN and JPEG2000 a

关键词： synthetic aperture radar (SAR) Despeckling image compression Machine learning Self-supervised learning Representation learning

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An Improved Parametric Polar Format Algorithm for Missile-Borne SAR Imaging With Large Squint Angles and Dive Trajectories

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IEEE GEOSCIENCE AND remote sensing LETTERS 2025年 22卷

作者： Xi, Zirui Wang, Guanyong Zhang, Lei Meng, Zhichao Wang, Xinshuo Wan, Bo Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China North China Univ Technol Sch Informat & Sci Beijing 100144 Peoples R China

Due to the complexity of the range model and the severe range-azimuth coupling in the signal echoes during the diving flight of missile-borne synthetic aperture radar (SAR), the traditional frequency-domain algorithms have the limitation of accuracy in the processing of missile-borne SAR imaging, and the complexity of the algorithm is relatively high. To solve the problem of mismatch between the algorithm and the range model in the diving state, an improved parametric polar format algorithm (PPFA) based on equivalent range model is proposed. First, this letter transforms the diving trajectory model of the missile-borne into an equivalent range model applicable to horizontal straight flight. Then, based on the equivalent range model, and considering the spatial variability of the equivalent velocity and squint angle, we improve the azimuth-focusing operation of PPFA. These enhancements resolve the issue of poor imaging effect of edge points by using traditional PPFA, significantly improving the edge point focusing performance. The effectiveness and feasibility of the proposed algorithm are verified by the experimental simulation results and various indexes.

关键词： Trajectory Missiles radar imaging Interpolation Vectors radar polarimetry Couplings image edge detection Focusing Approximation algorithms Diving state equivalent range model parametric polar format algorithm (PPFA) synthetic aperture radar (SAR)

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A Lightweight Network for Ship Detection in SAR images Based on Edge Feature Aware and Fusion

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 3782-3796页

作者： Li, Yuming Liu, Jin Li, Xingye Zhang, Xiliang Wu, Zhongdai Han, Bing Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Ship & Shipping Res Inst Co Ltd Shanghai 200135 Peoples R China

Recently, with the increasing adoption of synthetic aperture radar (SAR) ship detection methods on mobile platforms, the lightweighting of detection methods has become a research focus. Despite certain achievements, there are still several limitations: 1) Existing studies have mainly focused on reducing model complexity through shallow network structures. However, this approach frequently results in performance degradation, as they neglected a thorough investigation into achieving a better balance point between inference speed and detection accuracy. 2) Under the lightweight network structure, the rich edge features contained in SAR images, which are crucial for distinguishing ship targets from complex backgrounds, are often underutilized. To address these issues, we propose a novel lightweight detection method based on edge feature aware and fusion. Specifically, to effectively extract edge feature, we introduce an Edge Feature-Aware (EFA) network that incorporates a multiscale channel attention module. Furthermore, a lightweight feature fusion network, Filter-Pruned Bi-directional Feature Pyramid Network (FP-BiFPN), is carefully designed, which can not only suppresses background information, but also accentuates ship targets. Finally, we propose a selective quantization algorithm based on a bit-width selection mechanism to reduce model memory usage without compromising performance. To validate the superiority of our proposed method, we conduct extensive experiments on multiple public datasets, achieving average accuracy scores of 94.2%, 97.6%, and 97.7% on the HRSID, SAR-Ship-Dataset, and SSDD, respectively, with a model parameter size of only 3.36 M, and the fastest processing time for a single frame is 7.2 ms.

关键词： Marine vehicles Feature extraction image edge detection radar polarimetry Accuracy Quantization (signal) synthetic aperture radar Computational complexity Adaptation models Inference algorithms Deep learning lightweight network ship detection synthetic aperture radar (SAR)

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A Joint Suppression Method of Scalloping and Inter-Scan Banding for SAR images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 6681-6693页

作者： An, Xinwei Yang, Wei Shen, Haijun Zeng, Hongcheng Zou, Fei Deng, Jiadong Su, Can Liu, Wei Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Shanghai Inst Satellite Engn Shanghai 200240 Peoples R China Beijing Inst Remote Sensing Informat Beijing 100192 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China

Due to a wide swath at the order of hundreds of kilometres, interscan banding (ISB) may occur in both ScanSAR and TOPS modes of a synthetic aperture radar (SAR), leading to degraded image quality. In this aticle, the causes of ISB are analyzed and an ISB suppression method based on Retinex theory and global weighting is proposed. First, the image with ISB is considered as an image with uneven lighting, and ISB is considered as a lighting component. The ISB component is calculated using a global weighting method. Based on previous studies, the coupling relationship between ISB and scalloping (another issue commonly found in ScanSAR) is identified, and a joint procedure for suppressing ISB and scalloping is established. The proposed method is a post-processing technique that does not require any other additional information, such as the noise equivalent sigma zero. Experimental results based on real SAR images from Gaofen-3 and Sentinel-1 indicate that the proposed method can significantly reduce the effect of ISB and improve the image quality, and it is more efficient and robust compared to existing methods.

关键词： radar polarimetry Lighting Couplings Sentinel-1 Satellites remote sensing radar imaging Phased arrays Kalman filters Electronic mail Gaofen-3 (GF-3) interscan banding (ISB) suppression ScanSAR TOPSAR

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Quantum Optimization for Phase Unwrapping in SAR Interferometry

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 3488-3500页

作者： Glatting, Kay Meyer, Jan Huber, Sigurd Krieger, Gerhard German Aerosp Ctr DLR D-82234 Wessling Germany

Phase unwrapping is the reconstruction of a phase given its values mod 2 pi. Phase unwrapping is an important image processing technique used in synthetic aperture radar interferometry, in the context of height estimation and ground deformation. By formulating the phase unwrapping problem as a global minimization problem, L(p )norm techniques form the backbone of many established phase unwrapping algorithms. The L-0 norm is commonly agreed to produce the best unwrapping solutions, but the necessary brute-force calculations are infeasible for the phase unwrapping of SAR interferograms. Developments in variational quantum algorithms suggest computational advantages, when compared to classical approaches, for a multitude of applications. We therefore investigate the application of quantum algorithms to the phase unwrapping problem and introduce and derive an approximate L-0 norm optimization solver utilizing the quantum approximate optimisation algorithm. This hybrid algorithm uses classical nonconvex parameter optimization to produce optimal parameters for a quantum circuit encoding the L-0 phase unwrapping. Subsequent execution of this quantum circuit allows for an estimate of the solution of the phase unwrapping problem with advantages compared to brute force approaches. We validate our approach for small topographies, by demonstrating improved results when compared to established L-1 optimization.

关键词： Optimization Quantum computing Quantum circuit Approximation algorithms synthetic aperture radar Qubit Quantum algorithm Phase measurement Geophysical measurements Deformation Interferometric synthetic aperture radar (InSAR) phase unwrapping (PU) quantum approximate optimization algorithm (QAOA) quantum computing

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Fine classification of multi-frequency PolSAR images using an adaptive fusion network

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remote sensing LETTERS 2025年第5期16卷 461-471页

作者： Wang, Zehua Wang, Zezhong Qiu, Xiaolan Zhang, Zhe Harbin Inst Technol Shenzhen Shenzhen Guangdong Peoples R China Suzhou Key Lab Microwave Imaging Proc & Applicat T 158 Dushuhu Ave Suzhou 215123 Jiangsu Peoples R China Suzhou Aerosp Informat Res Inst Suzhou Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Natl Key Lab Microwave Imaging Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China

Multi-frequency polarimetric synthetic aperture radar (MF-PolSAR) possesses the observational capabilities of traditional radar while also allowing for the acquisition of multi-frequency electromagnetic scattering information from observed targets, making it highly promising for various applications. To efficiently harness the wealth of multi-frequency information, this paper first introduces a scattering matrix extension method. Compared to other polarization data processing techniques, this method excels in preserving the original information, thereby facilitating the fusion of multi-frequency data. Furthermore, to address the issue of inter-band interference, an adaptive fusion model is proposed. Experimental results demonstrate that this method effectively leverages the information gain from different frequency bands, resulting in higher classification accuracy.

关键词： Multi-frequency polarimetric synthetic aperture radar image classification deep learning adaptive fusion model

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Recent Advances in Deep-Learning-Based SAR image Target Detection and Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 6884-6915页

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yang, Huizhang Yin, Junjun Yang, Jian Martorella, Marco Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Pisa Dept Informat Engn I-56127 Pisa Italy

synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

关键词： radar polarimetry Target recognition synthetic aperture radar Marine vehicles Feature extraction Object detection Aircraft Surveys Accuracy Deep learning Automatic target recognition change detection (CD) deep learning (DL) SAR image interpretation target detection

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Autofocus Algorithm for Real-Time Correction of Residual RCM Based on ANCPS

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Xie, Yi Chen, Longyong Zhang, Fubo Xu, Yihao Yang, Ling Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

With synthetic aperture radar (SAR) systems being developed with miniaturization, lightweight, low-cost, high-resolution, and real-time capabilities, the scope of their application has expanded to some lightweight platforms that cannot carry a high-precision position and orientation system (POS) and whose trajectories are susceptible to interference. This often results in residual range cell migration (RCM) exceeding several or even dozens of range resolution cells, which notably degrades the performance of traditional autofocus algorithms and seriously affects the quality of real-time imaging. Considering this, we propose a real-time residual RCM correction scheme based on the autocorrelated normalized cross-power spectrum (ANCPS). First, the range-compressed image is segmented into blocks based on the azimuth and range. The ANCPS is then used to estimate the optimal residual RCM for each subblock. Second, the optimal segment is selected from the residual RCM curve fragments estimated by various subblocks. Finally, the residual RCM fragments are spliced and filtered to correct the complete data. When the algorithm is deployed on a GPU platform, it only takes 1.39 s to process an 8 x 40 K high-resolution original SAR image, and the calculation time is reduced by 94.6% compared with only using a CPU for calculation. The experimental results demonstrate that the proposed residual RCM correction scheme has good convergence, can achieve subpixel residual RCM compensation without iteration and interpolation, has a simple calculation process, is efficient, and has strong parallelism. It is thus suitable for deployment in GPUs and is conducive to realizing real-time SAR autofocus at a low cost and with a high resolution.

关键词： image segmentation Imaging Graphics processing units radar imaging apertures Real-time systems Robustness Trajectory synthetic aperture radar Field programmable gate arrays Autocorrelated normalized cross-power spectrum (ANCPS) correction of range cell migration (RCM) parallel computing synthetic aperture radar (SAR) real-time autofocus

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Screening and Artifact Detection of RFI in Sentinel-1A Time-Series images Combining Change Detection Techniques With Structural Similarity Index

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 10864-10881页

作者： Zhang, Zhizheng Shu, Gaofeng Huang, Yabo Wu, Lin Li, Ning Henan Univ Coll Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Prov Engn Res Ctr Spatial Informat Proc Kaifeng 475004 Peoples R China

As a wideband radar system, spaceborne synthetic aperture radar (SAR) is susceptible from other high-power radiation sources, which can cause radio frequency interference (RFI) artifacts in the acquired images. Given that RFI significantly impacts data processing and image interpretation, screening and artifact detection of RFI have become top priorities for spaceborne SAR systems, which generate massive data daily. However, current image-level methods mostly rely on the acquisition of relevant prior knowledge, which makes efficient screening of large-scale products difficult when the scenario changes. In this article, a novel screening and detection method based on change detection techniques is proposed. Due to the time-varying feature of RFI artifacts in time-series SAR images, local areas affected by RFI can be masked through change detection operator. The screening step is performed by quantifying and analyzing the feature differences between RFI artifacts and ground-truth information in local areas. Therefore, an RFI-free background is constructed based on the results, enabling effective artifacts detection of RFI-containing images. Experimental results of Sentinel-1A Level-2 products verify the effectiveness and robustness of the proposed method.

关键词： radar polarimetry synthetic aperture radar Wideband Narrowband Spaceborne radar Robustness Radiofrequency interference radar imaging Indexes Accuracy image change detection radio frequency interference (RFI) structural similarity synthetic aperture radar (SAR) time series

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