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Remote Sensing
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SAR Target Detection and Recognition with Intelligent Methods and Its...
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SAR Target Detection and Recognition with Intelligent Methods and Its Applications

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

Deadline for manuscript submissions: 28 March 2026 | Viewed by 1764

Special Issue Editors

Dr. Zongyong Cui

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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Sihang Dang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Interests: SAR automatic target recognition; target detection; incremental learning
Dr. Siqian Zhang

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Guest Editor
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Interests: remote sensing; SAR image processing; SAR signal processing; object detection; image classification; feature extraction; simulation modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zongjie Cao

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar signal processing; synthetic aperture radar imaging; target recognition

Special Issue Information

Dear Colleagues,

Synthetic aperture radar is a type of active aviation and aerospace ground detection system that achieves coherent imaging of targets by transmitting an electromagnetic pulse and receiving backscattered information from the ground surface. It has many advantages, such as its all-weather, all-day, multiband, multipolarization, and strong penetration capability.

For key targets in military and civilian fields, such as aircraft, ships, tanks, satellites, and vehicles, SAR target detection and recognition can extract important information from SAR images, such as location, class, and situation.

Although many researchers have conducted meaningful research on SAR target detection and recognition, there are still many challenges that need to be addressed in this field. In particular, introducing AI algorithms in SAR target detection and recognition can further enhance the level of intelligence in this field and promote the practical application of this technology.

This Special Issue aims to collect advanced methods and applications in SAR target detection and recognition. SAR is a very important remote sensing sensor; with this, we hope this Special Issue can be beneficial in promoting SAR’s application in remote sensing.

We are pleased to invite you to contribute your latest research results to this Special Issue. Original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  1. Target detection and recognition in spaceborne and airborne SAR;
  2. SAR target detection and recognition based on incremental learning, reinforcement learning, and transfer learning;
  3. SAR target detection and recognition working with few or zero samples;
  4. SAR target detection and recognition with large models;
  5. Ground, sea, and space target detection and recognition;
  6. SAR data simulation, enhancement, and generation;
  7. Deep network interpretability for SAR target detection and recognition.

Dr. Zongyong Cui
Dr. Sihang Dang
Dr. Siqian Zhang
Prof. Dr. Zongjie Cao
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 target detection
  • SAR target recognition
  • artificial intelligence
  • small sample
  • SAR image interpretation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 24793 KB  
Article
SAR-ESAE: Echo Signal-Guided Adversarial Example Generation Method for Synthetic Aperture Radar Target Detection
by Jiahao Cui, Jiale Duan, Wang Guo, Chengli Peng and Haifeng Li
Remote Sens. 2025, 17(17), 3080; https://doi.org/10.3390/rs17173080 - 4 Sep 2025
Viewed by 787
Abstract
Synthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness. Existing adversarial SAR target detection approaches mainly focus on the image domain and neglect the critical role of signal propagation, making it difficult [...] Read more.
Synthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness. Existing adversarial SAR target detection approaches mainly focus on the image domain and neglect the critical role of signal propagation, making it difficult to fully capture the connection between the physical space and the image domain. To address this limitation, we propose an Echo Signal-Guided Adversarial Example Generation method for SAR target detection (SAR-ESAE). The core idea is to embed adversarial perturbations into SAR echo signals and propagate them through the imaging and inverse scattering processes, thereby establishing a unified attack framework across the signal, image, and physical spaces. In this way, perturbations not only appear as pixel-level distortions in SAR images but also alter the scattering characteristics of 3D target models in the physical space. Simulation experiments in the Scenario-SAR dataset demonstrate that the SAR-ESAE method reduces the mean Average Precision of the YOLOv3 model by 23.5% and 8.6% compared to Dpatch and RaLP attacks, respectively. Additionally, it exhibits excellent attack effectiveness in both echo signal and target model attack experiments and exhibits evident adversarial transferability across detection models with different architectures, such as Faster-RCNN and FCOS. Full article
(This article belongs to the Special Issue SAR Target Detection and Recognition with Intelligent Methods and Its Applications)
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22 pages, 76137 KB  
Article
CS-FSDet: A Few-Shot SAR Target Detection Method for Cross-Sensor Scenarios
by Changzhi Liu, Yibin He, Xiuhua Zhang, Yanwei Wang, Zhenyu Dong and Hanyu Hong
Remote Sens. 2025, 17(16), 2841; https://doi.org/10.3390/rs17162841 - 15 Aug 2025
Viewed by 552
Abstract
Synthetic Aperture Radar (SAR) plays a pivotal role in remote-sensing target detection. However, domain shift caused by distribution discrepancies across sensors, coupled with the scarcity of target-domain samples, severely restricts the generalization and practical performance of SAR detectors. To address these challenges, this [...] Read more.
Synthetic Aperture Radar (SAR) plays a pivotal role in remote-sensing target detection. However, domain shift caused by distribution discrepancies across sensors, coupled with the scarcity of target-domain samples, severely restricts the generalization and practical performance of SAR detectors. To address these challenges, this paper proposes a few-shot SAR target-detection framework tailored for cross-sensor scenarios (CS-FSDet), enabling efficient transfer of source-domain knowledge to the target domain. First, to mitigate inter-domain feature-distribution mismatch, we introduce a Multi-scale Uncertainty-aware Bayesian Distribution Alignment (MUBDA) strategy. By modeling features as Gaussian distributions with uncertainty and performing dynamic weighting based on uncertainty, MUBDA achieves fine-grained distribution-level alignment of SAR features under different resolutions. Furthermore, we design an Adaptive Cross-domain Interactive Coordinate Attention (ACICA) module that computes cross-domain spatial-attention similarity and learns interaction weights adaptively, thereby suppressing domain-specific interference and enhancing the expressiveness of domain-shared target features. Extensive experiments on two cross-sensor few-shot detection tasks, HRSID→SSDD and SSDD→HRSID, demonstrate that the proposed method consistently surpasses state-of-the-art approaches in mean Average Precision (mAP) under 1-shot to 10-shot settings. Full article
(This article belongs to the Special Issue SAR Target Detection and Recognition with Intelligent Methods and Its Applications)
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