Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images.
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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 discont.....
JournalIEEE Journal of Selected Topics In Applied Earth Observations And Remote Sensing
Page Rangepp.3846 - 3859
Sustainable Development Goals (SDG)14.a
Maturity LevelPilot or Demonstrated
Spatial CoveragePolar Regions
CitationHe, Y., Gao, F., Wang, J., Hussain, A., Yang, E. and 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, 14:9385869, pp.3846–3859. DOI: https://doi.org/10.1109/JSTARS.2021.3068530
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