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

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 591

Special Issue Editors

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

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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

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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

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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.  

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 (1 paper)

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Research

27 pages, 9566 KiB  
Article
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 271
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 [...] Read more.
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
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