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题名:
Hyperspectral anomaly detection based on machine learning and building selection graph
作者: Tang, Yehui1; Qin, Hanlin1; Liang, Ying1; Leng, Hanbing2; Ju, Zezhao1
出版日期: 2017
会议名称: Applied Optics and Photonics China: Optical Sensing and Imaging Technology and Applications, AOPC 2017
会议日期: 2017-06-04
会议地点: Beijing, China
DOI: 10.1117/12.2285780
通讯作者: Qin, Hanlin
英文摘要:

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.

收录类别: EI ; ISTP
会议录: AOPC 2017: Optical Sensing and Imaging Technology and Applications
会议录出版者: SPIE
语种: 英语
作者部门: 光谱成像技术实验室
卷号: 10462
产权排序: 2
ISBN号: 9781510614055
ISSN号: 0277786X
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.opt.ac.cn/handle/181661/29917
Appears in Collections:光谱成像技术实验室_会议论文

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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

Recommended Citation:
Tang, Yehui,Qin, Hanlin,Liang, Ying,et al. Hyperspectral anomaly detection based on machine learning and building selection graph[C]. 见:Applied Optics and Photonics China: Optical Sensing and Imaging Technology and Applications, AOPC 2017. Beijing, China. 2017-06-04.
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