Fei Gao
SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement
Gao, Fei; Han, Xu; Wang, Jun; Sun, Jinping; Hussain, Amir; Zhou, Huiyu
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) |
Files
SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement
(12.4 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Guided Policy Search for Sequential Multitask Learning
(2018)
Journal Article
Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization
(2018)
Journal Article
Cross-modality interactive attention network for multispectral pedestrian detection
(2018)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search