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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
Conference NameApplied Optics and Photonics China: Optical Sensing and Imaging Technology and Applications, AOPC 2017
Source PublicationAOPC 2017: Optical Sensing and Imaging Technology and Applications
Volume10462
Conference Date2017-06-04
Conference PlaceBeijing, China
PublisherSPIE
Contribution Rank2
Abstract

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.

Department光谱成像技术实验室
DOI10.1117/12.2285780
Indexed ByEI ; ISTP
ISBN9781510614055
Language英语
ISSN0277786X
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/29917
Collection光谱成像技术实验室
Corresponding AuthorQin, Hanlin
Affiliation1.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
Recommended Citation
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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