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A review of ship detection and recognition based on optical remote sensing image
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Science & Technology Review | 2017, 35(20) : 77 - 85
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Science & Technology Review | 2017, 35(20): 77-85
• Spescial Issues •
A review of ship detection and recognition based on optical remote sensing image
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CHEN Liang1, WANG Zhiru1, HAN Zhong1, WANG Guanqun1, ZHOU Haotian1, SHI Hao1,2, HU Cheng1, LONG Teng1
Affiliations
    1. Beijing Key Laboratory of Embedded Real-time Information Processing Technology;Lab of Radar Research, Schoool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;
    2. Department of Electronics, Tsinghua University, Beijing 100084, China
Published: 2017-10-28 doi: 10.3981/j.issn.1000-7857.2017.20.008
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Ship detection based on optical remote sense images is an important application direction in the marine information perception. Its primary tasks include the fast detection of ship targets in a large view field and the further extraction and classification of the targets based on the ship detection. It is of great significance both in civilian and military applications. This paper reviews the main achievements in that field, focusing on the difficulties involved. Finally, the existing problems and the future development are discussed.
remote sensing image  /  ship detection  /  target recognition  /  feature extraction  /  deep learning
陈亮, 王志茹, 韩仲, 王冠群, 周浩天, 师皓, 胡程, 龙腾. 基于可见光遥感图像的船只目标检测识别方法. 科技导报, 2017 , 35 (20) : 77 -85 . DOI: 10.3981/j.issn.1000-7857.2017.20.008
CHEN Liang, WANG Zhiru, HAN Zhong, WANG Guanqun, ZHOU Haotian, SHI Hao, HU Cheng, LONG Teng. A review of ship detection and recognition based on optical remote sensing image[J]. Science & Technology Review, 2017 , 35 (20) : 77 -85 . DOI: 10.3981/j.issn.1000-7857.2017.20.008
Year 2017 volume 35 Issue 20
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doi: 10.3981/j.issn.1000-7857.2017.20.008
  • Receive Date:2017-09-25
  • Online Date:2017-10-31
  • Published:2017-10-28
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  • Received:2017-09-25
  • Revised:2017-10-10
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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Genus
种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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