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SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement

Gao, Fei; Han, Xu; Wang, Jun; Sun, Jinping; Hussain, Amir; Zhou, Huiyu

Authors

Fei Gao

Xu Han

Jun Wang

Jinping Sun

Huiyu Zhou



Abstract

There are several unresolved issues in the field of ship instance segmentation in synthetic aperture radar (SAR) images. First, in inshore dense ship area, the problems of missed detections and mask overlap frequently occur. Second, in inshore scenes, false alarms occur due to strong clutter interference. In order to address these issues, we propose a novel ship instance segmentation network based on dynamic key points information enhancement. In the detection branch of the network, a dynamic key points module is designed to incorporate the target's geometric information into the parameters of the dynamic mask head using an implicit encoding technique. In addition, we introduce a dynamic key points encoding branch, which encodes the target's strong scattering regions as dynamic key points. It strengthens the network's ability to learn the correspondence between local regions with strong scattering and overall ship targets, effectively mitigating mask overlap issues. Moreover, it enhances the discriminative ability of network between ship targets and clutter interference, leading to a reduction in false alarm rates. To further enhance the dynamic key points information, an instancewise attention map module is designed, which decodes the key points during the mask prediction period, generating instancewise attention maps based on 2-D Gaussian distribution. This module further enhances the sensibility of network to specific instances. Simulation experiments conducted on the Polygon Segmentation SAR Ship Detection Dataset and High-Resolution SAR Images Dataset demonstrate the superiority of our proposed method over other state-of-the-art methods in inshore and offshore scenes.

Citation

Gao, F., Han, X., Wang, J., Sun, J., Hussain, A., & Zhou, H. (2024). SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 11365-11385. https://doi.org/10.1109/jstars.2024.3383779

Journal Article Type Article
Acceptance Date Mar 20, 2024
Online Publication Date Apr 16, 2024
Publication Date 2024
Deposit Date Jun 24, 2024
Publicly Available Date Jun 24, 2024
Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Print ISSN 1939-1404
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 17
Pages 11365-11385
DOI https://doi.org/10.1109/jstars.2024.3383779
Keywords Implicit encoding, key points detection, ship instance segmentation, synthetic aperture radar (SAR)

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