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

被引:14
|
作者
Xiyue HOU [1 ]
Wei AO [1 ]
Qian SONG [1 ]
Jian LAI [2 ]
Haipeng WANG [1 ]
Feng XU [1 ]
机构
[1] Key Lab for Information Science of Electromagnetic Waves (MoE), Fudan University
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
FUSAR-Ship; Gaofen-3; SAR-AIS matchup; automatic target recognition; multi-scale CFAR; deep learning;
D O I
暂无
中图分类号
U675.79 [新技术在航海上的应用]; TN957.52 [数据、图像处理及录取]; TP18 [人工智能理论];
学科分类号
080904 ; 0810 ; 081001 ; 081002 ; 081104 ; 081105 ; 0812 ; 0825 ; 0835 ; 1405 ;
摘要
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 FUSARShip high-resolution GF-3SAR 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.
引用
收藏
页码:40 / 58
页数:19
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