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SAR Imaging and Deep Learning for Sea Target Detection and Maritime Surveillance

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 10279

Editors

Dr. Ding Nie

E-Mail Website
Guest Editor
School of Physics, Xidian University, Xi’an 710071, China
Interests: Remote sensing; maritime target detection; deep learning
Prof. Dr. Vassilis Anastassopoulos

E-Mail Website
Guest Editor
Department of Physics, University of Patras, 26504 Rio, Greece
Interests: signal and image processing; pattern recognition; remote sensing; information fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Chen

E-Mail Website
Guest Editor Assistant
Southeast University，School of Information Science and Engineering , Nanjing 210096, China
Interests: computational electromagnetics (CEM); information metamaterials and systems

Special Issue Information

Dear Colleagues,

With the rapid development of marine economic and related activities, maritime target detection and surveillance has become a core requirement for safeguarding marine security, maintaining ecological balance, and promoting resource development. Synthetic Aperture Radar (SAR), with its all-weather, all-day, high-resolution imaging capability, has shown irreplaceable advantages in complex marine environments. However, traditional SAR target detection methods are limited by the limitations of artificial feature design, and it is difficult to cope with the problems of target multi-scale changes, motion behavior, complex background interference, etc. In recent years, breakthroughs in deep learning technology have injected new momentum into SAR image interpretation, significantly improving the detection accuracy and adaptability of maritime targets through treatments such as multi-dimensional domain feature fusion, rotational invariance modeling, and real-time detection architecture optimization. The research in this cross-cutting field not only promotes the intelligent transformation of remote sensing technology but also has far-reaching scientific significance and application value for marine situational awareness, disaster emergency response, and marine safety and security.

This Special Issue aims to report the state of the art on the deep integration of SAR imaging technology and deep learning, which fits the direction of Remote Sensing's focus on cutting-edge technological innovation and interdisciplinary applications, such as SAR imaging mechanism optimization, deep learning model innovation, and multimodal data synergy.

The potential themes include, but are not limited to, the following:

  • SAR imaging and signal processing in marine environments;
  • Electromagnetic scattering modeling in marine environments;
  • Maritime target detection;
  • Deep learning models and algorithms;
  • Marine situational awareness;
  • Multi-source data fusion;
  • Performance evaluation criteria of detection algorithms in complex scenarios.

Dr. Ding Nie
Prof. Dr. Vassilis Anastassopoulos
Guest Editors

Dr. Hui Chen
Guest Editor Assistant

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-anonymized 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

  • SAR imaging
  • maritime target detection
  • deep learning
  • electromagnetic scattering
  • multi-source data fusion
  • marine situational awareness

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Published Papers (4 papers)

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Research

29 pages, 7577 KB  
Article
Confusing and Challenging Negative Proposals Mining for Few-Shot SAR Ship Detection Based on Uncertainty Estimation
by Fengjun Zhong, Fei Gao, Xiaoyu He, Jun Wang, Jinping Sun and Amir Hussain
Remote Sens. 2026, 18(16), 2737; https://doi.org/10.3390/rs18162737 - 14 Aug 2026
Viewed by 136
Abstract
Deep-learning-based few-shot synthetic aperture radar (SAR) ship detection has demonstrated considerable potential in scenarios with limited annotated samples and dynamically emerging ship categories, closely matching the practical requirements of maritime surveillance and intelligent SAR image interpretation. However, existing methods often overlook challenging negative [...] Read more.
Deep-learning-based few-shot synthetic aperture radar (SAR) ship detection has demonstrated considerable potential in scenarios with limited annotated samples and dynamically emerging ship categories, closely matching the practical requirements of maritime surveillance and intelligent SAR image interpretation. However, existing methods often overlook challenging negative proposals and the incomplete annotation problem inherent in few-shot learning. In complex offshore and inshore scenes, negative proposals contain either difficult background regions caused by sea clutter, port facilities, and strong scattering interference, or unlabeled ship targets resulting from missing annotations. Existing methods struggle to distinguish between these two types of proposals. Ignoring challenging negatives prevents the detector from learning precise decision boundaries between ships and complex backgrounds, whereas treating unlabeled ships as background introduces erroneous gradients during backpropagation and degrades detection performance. To address these issues, we introduce uncertainty as a measure of proposal reliability and propose two complementary components: uncertainty-guided proposal separation (UGPS) and uncertainty-aware discriminative gradient refocusing (UADGR). UGPS jointly exploits proposal uncertainty and intersection-over-union (IoU) to separate challenging negatives and confusing negatives from the negative proposal set, thereby preserving informative hard backgrounds while identifying potential unlabeled ships. Subsequently, UADGR combines proposal uncertainty with feature dissimilarity to a background prototype to adaptively regulate their training gradients. Specifically, higher weights are assigned to challenging negatives to improve discrimination between ships and complex background interference, whereas lower weights are assigned to confusing negatives to suppress erroneous supervision introduced by missing ship annotations. Extensive experiments on SRSDD-v1.0 demonstrate consistent improvements over existing few-shot detection approaches across different data splits and shot settings, while additional results on SAR-AIRcraft-1.0 further confirm the generalization of the proposed method. Full article
(This article belongs to the Special Issue SAR Imaging and Deep Learning for Sea Target Detection and Maritime Surveillance)
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24 pages, 6696 KB  
Article
Prototype-Driven Semantic Tree with Bimodal Embedding Space for Zero-Shot SAR Ship Recognition
by Rui Zhu and Tianwen Zhang
Remote Sens. 2026, 18(16), 2671; https://doi.org/10.3390/rs18162671 - 9 Aug 2026
Viewed by 192
Abstract
Zero-shot SAR ship recognition aims to identify unseen ship categories without annotated SAR samples, offering a promising solution for open-category maritime remote sensing. Although synthetic aperture radar (SAR) provides all-weather and day-and-night observation capability, practical maritime surveillance often encounters newly emerging or rarely [...] Read more.
Zero-shot SAR ship recognition aims to identify unseen ship categories without annotated SAR samples, offering a promising solution for open-category maritime remote sensing. Although synthetic aperture radar (SAR) provides all-weather and day-and-night observation capability, practical maritime surveillance often encounters newly emerging or rarely observed ship types with limited labeled data. Existing zero-shot recognition methods mainly rely on direct visual–semantic mapping, while overlooking the scattering-driven structural characteristics of SAR imagery and the semantic relationships among ship categories. This leads to two key challenges: weak alignment between SAR visual features and textual attributes, and semantic isolation of unseen categories in the embedding space. To address these issues, we propose a prototype-driven semantic tree framework with a bimodal embedding space for zero-shot SAR ship recognition. First, a Bimodal Embedding Space Construction (BESC) module is designed to learn structure-aware visual embeddings from SAR images and dependency-aware semantic embeddings from textual attributes, and align them within a unified embedding space. Second, a Prototype-Driven Semantic Tree (PDST) module organizes class prototypes into a hierarchical structure and refines them through tree-guided propagation, enabling structured knowledge transfer among related ship types. During inference, an unseen SAR image is classified by matching its visual embedding with the refined semantic prototypes. Experiments on the FUSAR and SRSDD datasets show that BESC-PDST improves the harmonic mean from 0.424 to 0.448 and from 0.509 to 0.541, respectively, outperforming representative zero-shot recognition methods. These results demonstrate the effectiveness of the proposed framework for knowledge-driven open-category SAR ship understanding. Full article
(This article belongs to the Special Issue SAR Imaging and Deep Learning for Sea Target Detection and Maritime Surveillance)
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26 pages, 4244 KB  
Article
Fine-Grained Spaceborne SAR Ship Classification into Nine Categories via AIS Association
by Xinyang Chen, Yi Zhang, Lizhen Hu, Hongyi Zhang, Liangsheng Li and Xupu Geng
Remote Sens. 2026, 18(13), 2223; https://doi.org/10.3390/rs18132223 - 6 Jul 2026
Viewed by 586
Abstract
Spaceborne Synthetic Aperture Radar (SAR) provides all-weather, day and night and wide-area imaging capability, and plays a critical role in maritime surveillance. While substantial progress has been achieved in SAR ship detection, SAR ship classification remains relatively underexplored, mainly due to the scarcity [...] Read more.
Spaceborne Synthetic Aperture Radar (SAR) provides all-weather, day and night and wide-area imaging capability, and plays a critical role in maritime surveillance. While substantial progress has been achieved in SAR ship detection, SAR ship classification remains relatively underexplored, mainly due to the scarcity of reliable category labels. Automatic Identification System (AIS) provides vessel identity, type, and dynamic trajectory information, and thus offers vessel type information that is difficult to obtain directly from SAR imagery. This paper proposes a fine-grained nine-category SAR ship classification method based on AIS association, which reorganizes the original AIS vessel types into nine fine-grained categories of SAR ship, transfers AIS vessel type information to SAR detection through a global optimal matching strategy, and supports SAR-only vessel category recognition. By retaining only high-confidence SAR and AIS matched pairs and cropping the corresponding SAR ship chips, an SAR ship classification dataset containing 4472 ship chips across the nine categories is constructed. In Monte Carlo experiments based on real AIS records, the proposed association strategy achieves more reliable high-confidence label generation than the compared association methods under close ship ambiguity, spatial perturbation, distractor AIS candidates, and AIS static size errors. In the benchmark experiment on the constructed classification dataset, ConvNeXt-Tiny achieves the best performance among the compared mainstream classifiers. These results demonstrate that AIS association can provide reliable category supervision for SAR ship classification, and the trained classifier can perform ship classification using SAR imagery alone. Full article
(This article belongs to the Special Issue SAR Imaging and Deep Learning for Sea Target Detection and Maritime Surveillance)
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26 pages, 6668 KB  
Article
Dark Ship Detection via Optical and SAR Collaboration: An Improved Multi-Feature Association Method Between Remote Sensing Images and AIS Data
by Fan Li, Kun Yu, Chao Yuan, Yichen Tian, Guang Yang, Kai Yin and Youguang Li
Remote Sens. 2025, 17(13), 2201; https://doi.org/10.3390/rs17132201 - 26 Jun 2025
Cited by 17 | Viewed by 8462
Abstract
Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote [...] Read more.
Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote sensing and AIS data, with a focus on oriented bounding box course estimation, to improve the detection of dark ships and enhance maritime surveillance. Firstly, the oriented bounding box object detection model (YOLOv11n-OBB) is trained to break through the limitations of horizontal bounding box orientation representation. Secondly, by integrating position, dimensions (length and width), and course characteristics, we devise a joint cost function to evaluate the combined significance of multiple features. Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. Finally, by integrating data from Gaofen-6 (optical) and Gaofen-3B (SAR) satellites, a day-and-night collaborative monitoring framework is constructed to address the blind spots of single-sensor monitoring during night-time or adverse weather conditions. Our results indicate that the detection model demonstrates a high average precision (AP50) of 0.986 on the optical dataset and 0.903 on the SAR dataset. The association accuracy of the multi-feature association algorithm is 91.74% in optical image and AIS data matching, and 91.33% in SAR image and AIS data matching. The association rate reaches 96.03% (optical) and 74.24% (SAR), respectively. This study provides an efficient technical tool for maritime safety regulation through multi-source data fusion and algorithm innovation. Full article
(This article belongs to the Special Issue SAR Imaging and Deep Learning for Sea Target Detection and Maritime Surveillance)
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