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Hyperspectral anomaly detection based on machine learning and building selection graph
Tang, Yehui1; Qin, Hanlin1; Liang, Ying1; Leng, Hanbing2; Ju, Zezhao1
2017
会议名称Applied Optics and Photonics China: Optical Sensing and Imaging Technology and Applications, AOPC 2017
会议录名称AOPC 2017: Optical Sensing and Imaging Technology and Applications
卷号10462
会议日期2017-06-04
会议地点Beijing, China
出版者SPIE
产权排序2
摘要

In hyperspectral images, anomaly detection without prior information develops rapidly. Most of the existing methods are based on restrictive assumptions of the background distribution. However, the complexity of the environment makes it hard to meet the assumptions, and it is difficult for a pre-set data model to adapt to a variety of environments. To solve the problem, this paper proposes an anomaly detection method on the foundation of machine learning and graph theory. First, the attributes of vertexes in the graph are set by the reconstruct errors. And then, robust background endmember dictionary and abundance matrix are received by structured sparse representation algorithm. Second, the Euler distances between pixels in lower-dimension are regarded as edge weights in the graph, after the analysis of the low dimensional manifold structure among the hyperspectral data, which is in virtue of manifold learning method. Finally, anomaly pixels are picked up by both vertex attributes and edge weights. The proposed method has higher probability of detection and lower probability of false alarm, which is verified by experiments on real images. © 2017 SPIE.

作者部门光谱成像技术实验室
DOI10.1117/12.2285780
收录类别EI ; ISTP
ISBN号9781510614055
语种英语
ISSN号0277786X
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/29917
专题光谱成像技术实验室
通讯作者Qin, Hanlin
作者单位1.School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, 710071, China
2.Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an, 710119, China
推荐引用方式
GB/T 7714
Tang, Yehui,Qin, Hanlin,Liang, Ying,et al. Hyperspectral anomaly detection based on machine learning and building selection graph[C]:SPIE,2017.
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