期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 19, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2022.3145790
关键词
Marine vehicles; Radar polarimetry; Synthetic aperture radar; Decoding; Feature extraction; Task analysis; Background noise; Encoder-decoder; multitask learning; ship detection in SAR image; synthetic aperture radar (SAR) image
类别
资金
- National Natural Science Foundation of China [61672158, 61972097]
- Natural Science Foundation of Fujian Province [2019J02006, 2020J01494, 2021H6022]
- National Key Research and Development Plan of China [2021YFB3600503]
In this study, an efficient encoder-decoder network with estimated ship direction was proposed for ship detection in SAR images. The network achieved state-of-the-art performance by extracting multiple-level features and addressing the challenge of overlapped annotations.
Synthetic aperture radar (SAR) image ship detection has important applications in marine surveillance. There are two limitations when applying advanced detection methods naively for SAR ship detection. First, most detectors construct the model as an encoder and rely on the feature pyramid network (FPN) head for accurate prediction, which may lead to high computational costs. Second, the background noises in the ground truth (annotated as rectangular bounding boxes) of angular ships bring difficulties for model training. To meet these challenges, we propose an efficient encoder-decoder network with estimated direction for ship detection in SAR images. First, we present an anchor-free encoder-decoder model that can efficiently extract multiple-level features. Second, we formulate ship detection as a multitask learning problem, including a bounding box prediction and a ship direction regression. The estimated ship direction can weakly supervise and benefit ship detection. Furthermore, we develop a center-weighted labeling method for overlapped annotations. Comprehensive experiments on SAR-Ship-Detection and SSDD datasets show that our method achieves state-of-the-art performance with a high running speed.
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