A Moving Target Detection Method for GEO SAR Image in Maritime Areas

WU Yifan; HUANG Lijia; YAN Chaobao; ZHANG Bingchen

doi:10.11999/JEIT240906

Volume 47 Issue 6

Jun. 2025

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WU Yifan, HUANG Lijia, YAN Chaobao, ZHANG Bingchen. A Moving Target Detection Method for GEO SAR Image in Maritime Areas[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1723-1733. doi: 10.11999/JEIT240906

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WU Yifan, HUANG Lijia, YAN Chaobao, ZHANG Bingchen. A Moving Target Detection Method for GEO SAR Image in Maritime Areas[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1723-1733. doi: 10.11999/JEIT240906

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A Moving Target Detection Method for GEO SAR Image in Maritime Areas

doi: 10.11999/JEIT240906 cstr: 32379.14.JEIT240906

WU Yifan^{1, 2, 3, 4},
HUANG Lijia^{1, 2, 3
,
,},
YAN Chaobao^{1, 2, 3, 4},
ZHANG Bingchen^{1, 2, 3}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
4.
Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China

Funds: The Youth Innovation Promotion Association, Chinese Academy of Sciences (Y2023036)

Received Date: 2024-10-22
Rev Recd Date: 2025-05-14

Available Online: 2025-05-24

Publish Date: 2025-06-30

Abstract

Abstract

Objective Geosynchronous Synthetic Aperture Radar (GEO SAR) provides wide-area coverage and rapid revisit, supporting near real-time observation of large maritime regions. However, detecting moving targets in GEO SAR images remains challenging due to severe geometric shifts and defocusing effects induced by target motion. These issues are further compounded when deep learning-based detection algorithms are applied. Specifically, the extended synthetic aperture time of GEO SAR leads to significant motion-induced defocusing, degrading the structural clarity of moving targets. Moreover, the wide swath of GEO SAR results in sparsely distributed moving targets, substantially increasing computational demands. The long integration time also renders GEO SAR imagery more sensitive to sea clutter, which elevates false alarm rates and obscures target backscattering signatures, thereby compromising target contour visibility. To address these challenges, this study analyzes the displacement and phase errors introduced by moving targets under the non-linear squint-angle imaging geometry of GEO SAR. Based on this analysis, a diffusion model-based method is proposed to detect maritime moving targets in GEO SAR imagery. Methods To address the aforementioned challenges, this study analyzes azimuth displacement and phase errors induced by target motion under the non-planar squint-angle imaging geometry of GEO SAR. Based on this analysis, a diffusion-based approach is proposed for detecting maritime moving targets in GEO SAR imagery. The method comprises two primary components: a preprocessing stage and a conditional diffusion detection network. In the preprocessing stage, the full-scene image is divided into smaller sub-scenes to mitigate the difficulty of directly processing ultra-wide-swath GEO SAR data. These sub-scenes are subsequently downsampled to dimensions compatible with network input, which enhances the signal-to-noise ratio and strengthens the representation of motion-related features. In the detection stage, a conditional diffusion detection network tailored for GEO SAR is developed. This network accepts the preprocessed image as conditional input to guide the generation of detection results, thereby improving accuracy. To further refine performance, a dense interaction module is introduced to facilitate multi-scale feature fusion between the segmentation mask and original data in the latent space, enabling precise segmentation of moving targets. Results and Discussions To evaluate the effectiveness of the proposed detection method, the imaging characteristics of sea clutter and moving targets under long synthetic aperture time are simulated. The sea surface is modeled using the Jonswap spectrum, and clutter is generated via a backscattering coefficient model, resulting in simulated imagery of sea clutter and moving targets under extended integration time (Fig. 5). A quantitative analysis is performed to examine the effect of downsampling on detection performance by calculating the Signal-to-Clutter Ratio (SCR), as summarized in Table 4. The results show that 8× downsampling improves the SCR by 11.3 dB. After preprocessing, the downsampled sub-scenes are fed into the detection network to produce moving target detection results (Fig. 6). Compared to other methods, the proposed approach yields improvements of 1.1%, 2.2%, and 0.9% in Intersection over Union (IoU), Accuracy, and F1-score, respectively. Conclusions To address the challenges of detecting moving maritime targets in GEO SAR images—such as ultra-long synthetic aperture duration, extremely wide swath, and interference from sea clutter—this study first analyzes azimuth displacement and phase errors introduced by target motion under the non-linear squint-angle imaging geometry of GEO SAR. A detection method is then proposed, comprising two key components: preprocessing and a conditional diffusion detection network. During preprocessing, the full-scene image is divided into multiple sub-scenes to facilitate processing of ultra-wide-swath GEO SAR data. These sub-scenes are downsampled to dimensions compatible with the detection network, improving the SCR, enhancing motion features, and suppressing clutter. In the detection stage, a conditional diffusion detection network customized for GEO SAR imagery is employed. This network uses the preprocessed sub-scenes as conditional input to guide the generation of detection results, enhancing accuracy. A dense interaction module is further incorporated to enable multi-scale feature coupling between the segmentation mask and the original data in the latent space, achieving pixel-level segmentation of moving targets. Simulation experiments confirm the method’s effectiveness in detecting moving maritime targets under complex oceanic conditions. Although initial progress has been achieved, further work is required to improve parameter extraction and optimize network performance. Future efforts will focus on refining detection models for more comprehensive analysis of moving targets in GEO SAR imagery.
- Geosynchronous Synthetic Aperture Radar (GEO SAR),
- Moving Target Detection (MTD),
- Deep learning,
- Diffusion model

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References(21)

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