Journal
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
Volume -, Issue -, Pages 9486-9489Publisher
IEEE
DOI: 10.1109/igarss.2019.8900619
Keywords
synthetic aperture radar; ship detection; ship recognition; CNN; dataset; GaoFen-3
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Funding
- National Key RD Program [2017YFB0502703]
- Natural Science Foundation of China [61822107]
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A framework of end-to-end ship detection and recognition for high-resolution Gaofen-3 (GF3) SAR images is proposed. The framework includes three consecutive stages, namely sea-land segmentation, ship detection and discrimination. First, Otsu-based segmentation is used to exclude land areas. Then, adaptive multi-scale constant false alarm rate (CFAR) algorithm is employed to detect candidate ship target pixels. Subsequently, a convolutional neural network (CNN) is designed to filter out false alarms. The CNN is trained by a in-house built GF3 ship dataset, GF3-FUSAR Ships, which is a matchup dataset of SAR and Automatic identification system (AIS).
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