Learning_Polar_Encodings_for_Arbitrary-Oriented_Ship_Detection_in_SAR_Images.pdf (8.44 MB)
Download file

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

Download (8.44 MB)
journal contribution
posted on 07.10.2021, 12:18 by Y He, F Gao, J Wang, A Hussain, E Yang, Huiyu Zhou
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.

History

Citation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 3846-3859, 2021, doi: 10.1109/JSTARS.2021.3068530.

Author affiliation

School of Informatics

Version

VoR (Version of Record)

Published in

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

14

Pagination

3846-3859

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

eissn

2151-1535

Acceptance date

22/03/2021

Copyright date

2021

Available date

16/04/2021

Language

en