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Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5902

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Special Issue Editors

Dr. Qihua Wu

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Guest Editor

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: (inverse) synthetic aperture radar imaging; radar anti-jamming; radar waveform design

Dr. Xiaobin Liu

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Guest Editor

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: radar anti-jamming; radar waveform design; radar image transformation

Dr. Zhiming Xu

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Guest Editor

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: characteristics and recognition of polarimetric radar targets

Prof. Dr. Siyuan He

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Guest Editor

School of Earth and Space Science and Technology, Wuhan University, Wuhan 430072, China
Interests: SAR/ISAR target feature modeling; computational electromagnetic; electromagnetic scattering

Special Issue Information

Dear Colleagues,

Imaging radars, including synthetic aperture radar (SAR) and inverse SAR (ISAR), play a significant role in remote sensing areas due to its all-day and all-weather imaging abilities. In recent years, imaging radar techniques have attracted great attention, especially signal processing, waveform design, anti-jamming, target feature extraction, and target recognition. Advances in the relevant aspects contribute greatly to the progress of microwave remote sensing techniques. Hence, it is of great necessity and significance to conduct this Special Issue entitled “Advances in imaging radar signal processing, target feature extraction and recognition”.

This Special Issue will include research covering recent advances related to imaging radar techniques in remote sensing. Topics may cover anything from signal processing, waveform design, and anti-jamming to target feature extraction, target recognition, and so on. Articles may address, but are not limited, to the following topics:

SAR/ISAR imaging techniques;
SAR/ISAR waveform design;
SAR/ISAR image feature transform;
SAR/ISAR anti-jamming;
SAR/ISAR target feature extraction;
SAR/ISAR target recognition;
Pol-SAR/ISAR information processing.

Submissions focused exclusively on radar signal processing and algorithms without demonstrated applications in remote sensing will not be accepted. Examples include the following:

Pure theoretical signal processing/radar array algorithms: studies lacking integration with remote sensing applications;
Studies focused on electronic engineering or hardware design, e.g., radar antennae and RF circuit improvements;
Studies focused on non-remote-sensing radar applications, e.g., industrial inspection or medical imaging;
Military-focused technical details.

Dr. Qihua Wu
Dr. Xiaobin Liu
Dr. Zhiming Xu
Prof. Dr. Siyuan He
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)
inverse synthetic aperture radar (ISAR)
radar imaging
radar waveform design
radar anti-jamming
target feature extraction
target recognition

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Published Papers (6 papers)

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Research

19 pages, 3571 KB

Open AccessArticle

Few-Shot Class-Incremental SAR Target Recognition Based on Dynamic Task-Adaptive Classifier

by Dan Li, Feng Zhao, Yong Li and Wei Cheng

Remote Sens. 2026, 18(3), 527; https://doi.org/10.3390/rs18030527 - 6 Feb 2026

Viewed by 505

Abstract

Current synthetic aperture radar automatic target recognition (SAR ATR) tasks face challenges including limited training samples and poor generalization capability to novel classes. To address these issues, few-shot class-incremental learning (FSCIL) has emerged as a promising research direction. Few-shot learning facilitates the expedited adaptation to novel tasks utilizing a limited number of labeled samples, whereas incremental learning concentrates on the continuous refinement of the model as new categories are incorporated without eradicating previously learned knowledge. Although both methodologies present potential resolutions to the challenges of sample scarcity and class evolution in SAR target recognition, they are not without their own set of difficulties. Fine-tuning with emerging classes can perturb the feature distribution of established classes, culminating in catastrophic forgetting, while training exclusively on a handful of new samples can induce bias towards older classes, leading to distribution collapse and overfitting. To surmount these limitations and satisfy practical application requirements, we propose a Few-Shot Class-Incremental SAR Target Recognition method based on a Dynamic Task-Adaptive Classifier (DTAC). This approach underscores task adaptability through a feature extraction module, a task information encoding module, and a classifier generation module. The feature extraction module discerns both target-specific and task-specific characteristics, while the task information encoding module modulates the network parameters of the classifier generation module based on pertinent task information, thereby improving adaptability. Our innovative classifier generation module, honed with task-specific insights, dynamically assembles classifiers tailored to the current task, effectively accommodating a variety of scenarios and novel class samples. Our extensive experiments on SAR datasets demonstrate that our proposed method generally outperforms the baselines in few-shot class incremental SAR target recognition. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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24 pages, 10940 KB

Open AccessArticle

A Few-Shot Object Detection Framework for Remote Sensing Images Based on Adaptive Decision Boundary and Multi-Scale Feature Enhancement

by Lijiale Yang, Bangjie Li, Dongdong Guan and Deliang Xiang

Remote Sens. 2026, 18(3), 388; https://doi.org/10.3390/rs18030388 - 23 Jan 2026

Viewed by 812

Abstract

Given the high cost of acquiring large-scale annotated datasets, few-shot object detection (FSOD) has emerged as an increasingly important research direction. However, existing FSOD methods face two critical challenges in remote sensing images (RSIs): (1) features of small targets within remote sensing images are incompletely represented due to extremely small-scale and cluttered backgrounds, which weakens discriminability and leads to significant detection degradation; (2) unified classification boundaries fail to handle the distinct confidence distributions between well-sampled base classes and sparsely sampled novel classes, leading to ineffective knowledge transfer. To address these issues, we propose TS-FSOD, a Transfer-Stable FSOD framework with two key innovations. First, the proposed detector integrates a Feature Enhancement Module (FEM) leveraging hierarchical attention mechanisms to alleviate small target feature attenuation, and an Adaptive Fusion Unit (AFU) utilizing spatial-channel selection to strengthen target feature representations while mitigating background interference. Second, Dynamic Temperature-scaling Learnable Classifier (DTLC) employs separate learnable temperature parameters for base and novel classes, combined with difficulty-aware weighting and dynamic adjustment, to adaptively calibrate decision boundaries for stable knowledge transfer. Experiments on DIOR and NWPU VHR-10 datasets show that TS-FSOD achieves competitive or superior performance compared to state-of-the-art methods, with improvements up to 4.30% mAP, particularly excelling in 3-shot and 5-shot scenarios. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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36 pages, 35595 KB

Open AccessArticle

Robust ISAR Autofocus for Maneuvering Ships Using Centerline-Driven Adaptive Partitioning and Resampling

by Wenao Ruan, Chang Liu and Dahu Wang

Remote Sens. 2026, 18(1), 105; https://doi.org/10.3390/rs18010105 - 27 Dec 2025

Viewed by 597

Abstract

Synthetic aperture radar (SAR) is a critical enabling technology for maritime surveillance. However, maneuvering ships often appear defocused in SAR images, posing significant challenges for subsequent ship detection and recognition. To address this problem, this study proposes an improved iteration phase gradient resampling autofocus (IIPGRA) method. First, we extract the defocused ships from SAR images, followed by azimuth decompression and translational motion compensation. Subsequently, a centerline-driven adaptive azimuth partitioning strategy is proposed: the geometric centerline of the vessel is extracted from coarsely focused images using an enhanced RANSAC algorithm, and the target is partitioned into upper and lower sub-blocks along the azimuth direction to maximize the separation of rotational centers between sub-blocks, establishing a foundation for the accurate estimation of spatially variant phase errors. Next, phase gradient autofocus (PGA) is employed to estimate the phase errors of each sub-block and compute their differential. Then, resampling the original echoes based on this differential phase error linearizes non-uniform rotational motion. Furthermore, this study introduces the Rotational Uniformity Coefficient (β) as the convergence criterion. This coefficient can stably and reliably quantify the linearity of the rotational phase, thereby ensuring robust termination of the iterative process. Simulation and real airborne SAR data validate the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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21 pages, 4796 KB

Open AccessArticle

Deep Bayesian Optimization of Sparse Aperture for Compressed Sensing 3D ISAR Imaging

by Zongkai Yang, Jingcheng Zhao, Mengyu Zhang, Changyu Lou and Xin Zhao

Remote Sens. 2025, 17(19), 3380; https://doi.org/10.3390/rs17193380 - 7 Oct 2025

Viewed by 1051

Abstract

High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling the aperture can decrease acquisition time; however, traditional reconstruction algorithms that utilize matched filtering exhibit significantly impaired imaging performance, often characterized by a high peak side-lobe ratio. A methodology is proposed that integrates compressed sensing(CS) theory with sparse-aperture optimization to achieve high-fidelity 3D imaging from sparsely sampled data. An optimized sparse sampling aperture is introduced to systematically balance the engineering requirement for efficient, continuous turntable motion with the low mutual coherence desired for the CS matrix. A deep Bayesian optimization framework was developed to automatically identify physically realizable optimal sampling trajectories, ensuring that the sensing matrix retains the necessary properties for accurate signal recovery. This method effectively addresses the high-sidelobe problem associated with traditional sparse techniques, significantly decreasing measurement duration while maintaining image quality. Quantitative experimental results indicate the method’s efficacy: the optimized sparse aperture decreases the number of angular sampling points by roughly 84% compared to a full acquisition, while reconstructing images with a high correlation coefficient of 0.98 to the fully sampled reference. The methodology provides an effective solution for rapid, high-performance 3D ISAR imaging, achieving an optimal balance between data acquisition efficiency and reconstruction fidelity. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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24 pages, 14264 KB

Open AccessArticle

Convex-Decomposition-Based Evaluation of SAR Scene Deception Jamming Oriented to Detection

by Hai Zhu, Sinong Quan, Shiqi Xing and Haoyu Zhang

Remote Sens. 2025, 17(18), 3178; https://doi.org/10.3390/rs17183178 - 13 Sep 2025

Viewed by 890

Abstract

The evaluation of synthetic aperture radar (SAR) jamming effectiveness is a primary means to measure the reliability of jamming effects, and it can provide important guidance for the selection of jamming strategies and application of jamming styles. To address problems in traditional evaluation methods for SAR scene deception jamming, namely the simple adoption of native feature parameters, incomprehensive integration for evaluation indicator design, and the inconsideration of resulting jamming detection effects, this paper proposes a SAR scene deceptive jamming evaluation method oriented to jamming detection. First, four profound feature parameters including the brightness change gradient, texture direction contrast degree, edge matching degree, and noise suppression difference index are extracted in terms of visual and non-visual manners, which accurately highlight the differences between jamming and the background. Subsequently, through nonlinear iterative optimization and loss function design, a comprehensive evaluation indicator, i.e., with a convex decomposition is proposed, which can effectively quantify the contribution of each feature parameter and distinguish the differences in jamming concealment under different scenes. Finally, based on the measured and simulated MiniSAR datasets of urban, mountainous, and other complex scenes, a mapping correlation between the SDD and jamming detection rate is established. The evaluation results show that when the SDD is less than 0.4, the jamming is undetectable; when the SDD is greater than 0.4, for every 0.1 increase in the SDD, the jamming detection rate decreases by approximately 0.1. This provides support for the quantification of jamming effects in terms of detection rate in real applications. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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27 pages, 9566 KB

Open AccessArticle

CSBBNet: A Specialized Detection Method for Corner Reflector Targets via a Cross-Shaped Bounding Box Network

by Wangshuo Tang, Yuexin Gao, Mengdao Xing, Min Xue, Huitao Liu and Guangcai Sun

Remote Sens. 2025, 17(16), 2760; https://doi.org/10.3390/rs17162760 - 8 Aug 2025

Viewed by 1188

Abstract

In synthetic aperture radar (SAR) maritime target detection tasks, corner reflector targets (CRTs) and their arrays can easily interfere with the accurate detection of ship targets, significantly increasing the misdetection rate and false alarm rate of detectors. Current deep learning-based research on SAR maritime target detection primarily focuses on ship targets, while dedicated detection methods addressing corner reflector interference have not yet established a comprehensive research framework. There remains a lack of theoretical innovation in detection principles for such targets. To address these issues, utilizing the prior knowledge of cross-shaped structures exhibited by marine CRTs in SAR images, we propose an innovative cross-shaped bounding box (CSBB) annotation strategy and design a novel dedicated detection network CSBBNet. The proposed method is constructed through three innovative component modules, namely the cross-shaped spatial feature perception (CSSFP) module, the wavelet cross-shaped attention downsampling (WCSAD) module, and the cross-shaped attention detection head (CSAD-Head). Additionally, to ensure effective training, we propose a cross-shaped intersection over union (CS-IoU) loss function. Comparative experiments with state-of-the-art methods demonstrate that our approach exhibits efficient detection capabilities for CRTs. Ablation experiment results validate the effectiveness of the proposed component architectures. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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