Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images

He, Yishan; Gao, Fei; Wang, Jun; Hussain, Amir; Yang, Erfu; Zhou, Huiyu

Abstract

Common horizontal bounding box-based methods are not capable of accurately locating slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images. Therefore, in recent years, methods based on oriented bounding box (OBB) have gradually received attention from researchers. However, most of the recently proposed deep learning-based methods for OBB detection encounter the boundary discontinuity problem in angle or key point regression. In order to alleviate this problem, researchers propose to introduce some manually set parameters or extra network branches for distinguishing the boundary cases, which make training more difficult and lead to performance degradation. In this article, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings. The encoding scheme uses a group of vectors pointing from the center of the ship target to the boundary points to represent an OBB. The boundary discontinuity problem is avoided by training and inference directly according to the polar encodings. In addition, we propose an intersect over union (IOU)-weighted regression loss, which further guides the training of polar encodings through the IOU metric and improves the detection performance. Comparative experiments on the benchmark Rotating SAR Ship Detection Dataset (RSSDD) demonstrate the effectiveness of our proposed method in terms of enhanced detection performance over state-of-the-art algorithms and other OBB encoding schemes.

Citation

He, Y., Gao, F., Wang, J., Hussain, A., Yang, E., & Zhou, H. (2021). Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3846-3859. https://doi.org/10.1109/jstars.2021.3068530

• Journal Article Type: Article
• Acceptance Date: Mar 22, 2021
• Online Publication Date: Mar 24, 2021
• Publication Date: Mar 24, 2021
• Deposit Date: May 28, 2021
• Publicly Available Date: May 28, 2021
• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Print ISSN: 1939-1404
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Pages: 3846-3859
• DOI: https://doi.org/10.1109/jstars.2021.3068530
• Keywords: Marine vehicles, Encoding, Training, Feature extraction, Synthetic aperture radar, Radar polarimetry, Task analysis
• Public URL: http://researchrepository.napier.ac.uk/Output/2776044