期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 11, 期 2, 页码 536-550出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2017.2787573
关键词
Eigenellipse discrimination; generalized Gamma distribution (G Gamma D); multiscale constant false alarm rate (CFAR); maximum-likelihood (ML) discrimination; synthetic aperture radar (SAR); ship detection
类别
资金
- National Key R&D Program of China [2017YFB0502700]
- National Natural Science Foundation of China Projects [61571132, 61571134]
This paper proposes a novelmethod for ship detection and discrimination in complex background from synthetic aperture radar (SAR) images. It first implements a pixel-level land-sea segmentation with the aid of a global 250-m water mask. Then, an efficient multiscale constant false alarm rate (CFAR) detector with generalized Gamma distribution clutter model is designed to detect candidate targets in the sea. At last, eigenellipse discrimination andmaximum-likelihood (ML) discrimination are designed to further exclude false alarm nonship objects in nearshore and harbor area. The proposed land-sea segmentation method is compared with multilevel Otsu method. The proposed multiscale ship detector is compared with conventional CFAR detectors. These contrast experiments show the good performance of our method. Finally, experiments undertaken on actual ALOS-2 SAR data show the efficacy of the proposed approach in detecting nearshore ship targets in a complex coastal environment.
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