FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

• Published in: Science China. Information sciences Vol. 63; no. 4; p. 140303
• Main Authors: Hou, Xiyue, Ao, Wei, Song, Qian, Lai, Jian, Wang, Haipeng, Xu, Feng
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.04.2020; Springer Nature B.V
• Subjects: Aircraft; Algorithms; Annotations; Artificial neural networks; C band; Classification; Clutter; Computer Science; Datasets; Deep learning; Design; False alarms; High resolution; Information Systems and Communication Service; Neural networks; Recognition; Remote sensing; Research Paper; Satellites; Synthetic aperture radar; Target detection; Target recognition; Taxonomy; multi-scale CFAR; deep learning; automatic target recognition; Gaofen-3; SAR-AIS matchup; FUSAR-Ship
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-019-2772-5

Gaofen-3 (GF-3) is China’s first civil C-band fully Polarimetric spaceborne synthetic aperture radar (SAR) primarily missioned for ocean remote sensing and marine monitoring. This paper proposes an automatic sea segmentation, ship detection, and SAR-AIS matchup procedure and an extensible marine target taxonomy of 15 primary ship categories, 98 sub-categories, and many non-ship targets. The FUSAR-Ship high-resolution GF-3 SAR dataset is constructed by running the procedure on a total of 126 GF-3 scenes covering a large variety of sea, land, coast, river and island scenarios. It includes more than 5000 ship chips with AIS messages as well as samples of strong scatterer, bridge, coastal land, islands, sea and land clutter. FUSAR-Ship is intended as an open benchmark dataset for ship and marine target detection and recognition. A preliminary 8-type ship classification experiment based on convolutional neural networks demonstrated that an average of 79% test accuracy can be achieved.