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Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks

文献类型：期刊论文

作者Yu, Y.; Ai, H.; He, X. J.; Yu, S. H.; Zhong, X.; Lu, M.
刊名Ieee Access
出版日期2018
卷号6页码:71122-71131
关键词Object detection feature extraction CNN AdaBoost classifier object detection tracks shape Computer Science Engineering Telecommunications
ISSN号2169-3536
DOI10.1109/access.2018.2881479
英文摘要The ship detection field faces many challenges due to the large-scale and high complexity of optical remote sensing images. Therefore, an innovative ship detection method that is simple, accurate, and stable is proposed in this paper. The algorithm consists of the following two steps: 1) the AdaBoost classifier, combined with Haar-like features, is used to rapidly extract candidate area slices, and 2) according to the characteristics of ships, a periphery-cropped network is designed for ship verification. Furthermore, we analyze the characteristics of ocean images to improve the contrast between the target and the background. Thus, an RGB spectrum-stretching method is proposed. Finally, we evaluate our method using spaceborne optical images from the Jilin-1 satellite, Google satellites, and the public dataset NWPU VHR-10. Our experimental results indicate that the proposed algorithm achieves a high detection rate.
源URL[http://ir.ciomp.ac.cn/handle/181722/61147]  
专题中国科学院长春光学精密机械与物理研究所
推荐引用方式
GB/T 7714		 Yu, Y.,Ai, H.,He, X. J.,et al. Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks[J]. Ieee Access,2018,6:71122-71131.
APA Yu, Y.,Ai, H.,He, X. J.,Yu, S. H.,Zhong, X.,&Lu, M..(2018).Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks.Ieee Access,6,71122-71131.
MLA Yu, Y.,et al."Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks".Ieee Access 6(2018):71122-71131.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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