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

doi:10.1109/access.2018.2881479

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	Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks
	Yu, Y.; Ai, H.; He, X. J.; Yu, S. H.; Zhong, X.; Lu, M.
	2018
发表期刊	Ieee Access
ISSN	2169-3536
卷号	6 页码:71122-71131
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.
关键词	Object detection feature extraction CNN AdaBoost classifier object detection tracks shape Computer Science Engineering Telecommunications
DOI	10.1109/access.2018.2881479
收录类别	SCI ; EI
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	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.

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