A Frequency-Aware and Spatially Constrained Network for Ship Instance Segmentation in SAR Images

ZHANG Boya; WANG Yong

doi:10.11999/JEIT250938

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ZHANG Boya, WANG Yong. A Frequency-Aware and Spatially Constrained Network for Ship Instance Segmentation in SAR Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250938

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ZHANG Boya, WANG Yong. A Frequency-Aware and Spatially Constrained Network for Ship Instance Segmentation in SAR Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250938

ZHANG Boya, WANG Yong. A Frequency-Aware and Spatially Constrained Network for Ship Instance Segmentation in SAR Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250938

Citation:

ZHANG Boya, WANG Yong. A Frequency-Aware and Spatially Constrained Network for Ship Instance Segmentation in SAR Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250938

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A Frequency-Aware and Spatially Constrained Network for Ship Instance Segmentation in SAR Images

doi: 10.11999/JEIT250938 cstr: 32379.14.JEIT250938

ZHANG Boya,
WANG Yong^,

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Funds: The National Key Research and Development Program of China (2024YFB3909800), The National Science Fund for Distinguished Young Scholars of China (62325104)

Received Date: 2025-09-19
Accepted Date: 2026-01-04
Rev Recd Date: 2026-01-04

Available Online: 2026-01-08

Abstract

Abstract

Objective With the development of Synthetic Aperture Radar (SAR) imaging technology, ship instance segmentation in SAR images has become an important research direction in radar signal processing. Unlike traditional optical image segmentation tasks, SAR images reflect target backscatter intensity and usually contain objects with diverse scales and irregular spatial distributions, which poses significant challenges for ship instance segmentation. Although recent studies have achieved notable progress, existing networks do not fully exploit frequency features and spatial information of targets, resulting in classification and localization errors. To address this limitation, a frequency-aware and spatially constrained network is proposed to extract frequency features and spatial information from multiscale representations, thereby improving feature representation and instance segmentation accuracy in SAR images. Methods For input SAR images, a frequency-aware backbone network is first applied to extract frequency features at different scales. Features from the first four stages of the backbone network are then processed by a selective feature pyramid network to guide the model to focus on the most informative regions and to fuse multiscale features effectively. After enhanced multiscale features are obtained, a region proposal network is employed to generate candidate target proposals. These features and proposals are subsequently fed into a segmentation head with spatial information constraints to produce final instance segmentation results. The frequency-aware backbone network encodes multiscale features in the frequency domain, which strengthens feature extraction for ship targets. Based on image semantic information, the selective feature pyramid network enables effective attention to informative regions and integration of features across scales. In addition, a spatially constrained mask loss function is designed to update model parameters under constraints of centroid distance and directional deviation between predicted masks and ground-truth targets. Results and Discussions The effectiveness and robustness of the proposed network are validated on two public datasets, SSDD and HRSID. For the SSDD dataset, P, R, F1, AP0.5, AP0.75, and AP0.5–0.95 metrics are used for evaluation. Quantitative and qualitative comparisons (Figures 6 and 7, Table 1) indicate that the proposed network improves feature extraction and feature integration for SAR images, which enables more accurate segmentation of ships with different scales in complex backgrounds. For the HRSID dataset, AP0.5, AP0.75, and AP0.5–0.95 are reported for quantitative comparison. The results (Table 3) demonstrate strong adaptability and generalization capability across different datasets and application scenarios in ship instance segmentation tasks. Additionally, ablation experiments (Table 2) confirm the contribution of each module of the proposed network to segmentation performance improvement in SAR images. Conclusions A frequency-aware and spatially constrained network for ship instance segmentation in SAR images is proposed. The frequency-aware backbone network enhances feature perception for SAR imagery, whereas the selective feature pyramid network guides attention toward informative regions and improves segmentation of ship targets at different scales. The segmentation head incorporates spatial information constraints into the mask loss function, which yields more accurate instance segmentation results. Experimental results on the SSDD and HRSID datasets show that the proposed method outperforms existing approaches and achieves improved effectiveness and generalization capability for ship instance segmentation in SAR images.
- Synthetic Aperture Radar (SAR),
- Ship instance segmentation,
- Frequency-aware,
- Feature extraction,
- Spatial information constraints

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References(31)

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