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Ship Imaging, Detection and Recognition for High-Resolution SAR

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1600

Special Issue Editors


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Guest Editor
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Interests: system design and imaging processing for high-resolution and wide swath synthetic aperture radar
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: system analysis and imaging processing for synthetic aperture radar moving target

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Guest Editor
School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 510006, China
Interests: ocean microwave remote sensing; synthetic aperture radar (SAR) signal processing; radar processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: SAR/ISAR signal processing and applications

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Guest Editor
Computer Information Systems, University of the Fraser Valley, 33844 King Rd, Abbotsford, BC V2S 7M8, Canada
Interests: machine learning; deep learning; remote sensing image processing

Special Issue Information

Dear Colleagues,

In recent years, the continuous improvement in the spatial resolution of synthetic aperture radar (SAR) systems has necessitated longer synthetic aperture time, creating significant challenges for ship imaging. The long aperture time introduces two major complications. Firstly, micro-motion causes continuous variations in a ship's attitude relative to the SAR platform, including its backscattering characteristics and echo coherence. Secondly, micro-motion induces multi-cycle oscillations in the range history, generating sinusoidal-coupled phase components in the echoes and micro-Doppler effects. The combined influence of time-varying backscattering and micro-Doppler phenomena leads to complicated amplitude-phase modulation in the echoes, resulting in severe defocusing in the reconstructed imagery. Moreover, high resolution alters the statistical properties of both sea surfaces and ship targets, significantly degrading the performance of conventional detection and recognition algorithms. These limitations substantially constrain the operational effectiveness of SAR in maritime security and traffic management applications. Consequently, developing advanced theoretical frameworks and methodologies for the high-resolution SAR imaging of moving ships, along with optimized detection and classification techniques for both coastal and open-ocean scenarios, has emerged as a critical research priority. Such advancements are essential for unlocking the full operational potential of modern SAR systems in maritime remote sensing.

This Special Issue aims to explore advanced methods related to imaging, detection and recognition for ships with complicated motion in high-resolution spaceborne SAR. This subject covers the entire process of ship imaging, detection and recognition in SAR signal processing, aligning with this journal’s focus on remote sensing, signal processing and geospatial AI technologies for maritime monitoring.

This Special Issue covers broad topics, including but not limited to the following:

  • System Design: Advanced imaging system/model for maneuvering ship surveillance;
  • Imaging Enhancement: Motion estimation, ship imaging, clutter suppression and image despeckling;
  • Detection Techniques: Maneuvering ship detection and non-focused ship detection;
  • Recognition Methods: Maneuvering ship recognition and AI-based ship recognition;
  • Related advances: Wave-height inversion, marine information inversion and real-time processing.

Prof. Dr. Ze Yu
Dr. Jindong Yu 
Prof. Dr. Xiaoqing Wang
Prof. Dr. Lijia Huang
Dr. Frank Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • SAR
  • ship imaging
  • ship detection
  • ship recognition
  • motion estimation
  • clutter suppression
  • maritime surveillance

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Published Papers (2 papers)

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Research

24 pages, 15964 KB  
Article
LGNet: A Lightweight Ghost-Enhanced Network for Efficient SAR Ship Detection
by Jiawei Chen, Junyu Huang, Yuna Tan, Zhifeng Wu and Renbo Luo
Remote Sens. 2025, 17(23), 3800; https://doi.org/10.3390/rs17233800 - 23 Nov 2025
Viewed by 639
Abstract
Current SAR ship detection methods face a critical trade-off between accuracy and computational efficiency, severely limiting their deployment on resource-constrained edge devices that are essential for distributed maritime surveillance systems. This paper presents LGNet, a novel ultra-lightweight network specifically designed for edge deployment [...] Read more.
Current SAR ship detection methods face a critical trade-off between accuracy and computational efficiency, severely limiting their deployment on resource-constrained edge devices that are essential for distributed maritime surveillance systems. This paper presents LGNet, a novel ultra-lightweight network specifically designed for edge deployment that achieves extreme model compression while maintaining detection performance through two core innovations. First, we develop a SAR-adapted Ghost-enhanced architecture that exploits inherent feature redundancy in SAR imagery through systematic integration of Ghost convolutions and hierarchical GHBlock modules, reducing redundant computation while preserving discriminative capabilities. Second, we introduce Layer-wise Adaptive Magnitude-based Pruning (LAMP) that assigns layer-specific sparsity levels based on multi-scale detection contributions, enabling intelligent compression with minimal accuracy loss. LGNet achieves remarkable efficiency gains: 75.3% parameter reduction and 59.3% FLOPs reduction compared to YOLOv8n baseline (from 3.0 M/8.1 G to 0.74 M/3.3 G) while delivering superior accuracy on SSDD (mAP@50: 97.9%, mAP@95: 71.9%) and strong generalization on RSDD-SAR (mAP@50: 94.4%). Extensive edge deployment validation demonstrates genuine real-time capability with 135.39 FPS performance on Huawei Atlas AIpro-20T edge computing platform, confirming practical viability for autonomous maritime systems and remote surveillance applications where computational resources are critically constrained. This work establishes that extreme model compression and high detection accuracy can coexist through principled SAR-specific lightweight design, enabling new paradigms for edge-based maritime monitoring networks. Full article
(This article belongs to the Special Issue Ship Imaging, Detection and Recognition for High-Resolution SAR)
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27 pages, 19387 KB  
Article
GEO SAR Refocusing Algorithm of Ship Targets with Complex Motion via CFSFD-Based ISAR Technique
by Xinhang Zhu, Yicheng Jiang, Zitao Liu, Yun Zhang and Qinglong Hua
Remote Sens. 2025, 17(22), 3659; https://doi.org/10.3390/rs17223659 - 7 Nov 2025
Cited by 1 | Viewed by 581
Abstract
In geosynchronous orbit satellite synthetic aperture radar (GEO SAR) maritime surveillance, imaging of ship targets with complex motion is a hard task. The difficulty lies in how to process the received signal with an extremely low signal-to-noise ratio (SNR), long synthetic aperture time, [...] Read more.
In geosynchronous orbit satellite synthetic aperture radar (GEO SAR) maritime surveillance, imaging of ship targets with complex motion is a hard task. The difficulty lies in how to process the received signal with an extremely low signal-to-noise ratio (SNR), long synthetic aperture time, high-order phase term and range migration. To address the issue, this paper proposes a GEO SAR refocusing algorithm for ship targets with complex motion via ISAR technique, which is based on complex fast-time slow-time frequency distribution (CFSFD). First, the received signal of ship targets with complex motion is derived and modeled as a multicomponent 2-D joint sine-series-polynomial phase signal (2-D JSPS), where 2-D signal is used to describe the signal with range migration induced by complex motion. To deal with the signal, a joint envelope-phase estimation named CFSFD is proposed. Even under low SNR and long synthetic aperture time, CFSFD can achieve directly instantaneous frequency analysis for 2-D JSPS accurately. Finally, a hybrid SAR/ISAR refocusing algorithm is proposed, in which CFSFD-based ISAR technique replaces the range migration correction followed by time–frequency transform approach, yielding clear refocused results of ship targets with complex motion. Simulation and real data experiments validate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Ship Imaging, Detection and Recognition for High-Resolution SAR)
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