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
相关论文
共 50 条
  • [31] Inshore Ship Detection via Saliency and Context Information in High-Resolution SAR Images
    Zhai, Liang
    Li, Yu
    Su, Yi
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (12) : 1870 - 1874
  • [32] Ship Target Detection in High-Resolution SAR Images Based on Information Theory and Harris Corner Detection
    Wang, Haijiang
    Ran, Yuanbo
    Liu, Shuo
    Deng, Yangyang
    Su, Debin
    COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL II: SIGNAL PROCESSING, 2020, 516 : 685 - 694
  • [33] A Novel Method of Ship Detection in High-Resolution SAR Images Based on GAN and HMRF Models
    Yang, Meng
    Yi, Chenchen
    PROGRESS IN ELECTROMAGNETICS RESEARCH LETTERS, 2019, 83 : 77 - 82
  • [34] Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery
    Lang, Haitao
    Zhang, Jie
    Zhang, Ting
    Zhao, Di
    Meng, Junmin
    JOURNAL OF APPLIED REMOTE SENSING, 2014, 8
  • [35] Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images
    Feng, Yingchao
    Diao, Wenhui
    Sun, Xian
    Yan, Menglong
    Gao, Xin
    REMOTE SENSING, 2019, 11 (16)
  • [36] Inshore Ship Detection with High-Resolution SAR Data Using Salience Map and Kernel Density
    Liu, Wei
    Zhen, Yong
    Huang, Jie
    Zhao, Yongjun
    EIGHTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2016), 2016, 10033
  • [37] Ship Detection via Superpixel-Random Forest Method in High-Resolution SAR Images
    Tan, Xiulan
    Cui, Zongyong
    Cao, Zongjie
    Min, Rui
    COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL II: SIGNAL PROCESSING, 2020, 516 : 702 - 707
  • [38] Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images
    Kong, Weimin
    Liu, Shanwei
    Xu, Mingming
    Yasir, Muhammad
    Wang, Dawei
    Liu, Wantao
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2023, 44 (04) : 1390 - 1415
  • [39] Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet
    Wei, Shunjun
    Su, Hao
    Ming, Jing
    Wang, Chen
    Yan, Min
    Kumar, Durga
    Shi, Jun
    Zhang, Xiaoling
    REMOTE SENSING, 2020, 12 (01)
  • [40] Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection
    Yangyang Deng
    Haijiang Wang
    Shuo Liu
    Min Sun
    Xiaohong Li
    EURASIP Journal on Wireless Communications and Networking, 2018