Hyperspectral anomaly detection based on machine learning and building selection graph | |
Tang, Yehui1; Qin, Hanlin1; Liang, Ying1; Leng, Hanbing2; Ju, Zezhao1 | |
2017 | |
会议日期 | 2017-06-04 |
会议地点 | Beijing, China |
卷号 | 10462 |
DOI | 10.1117/12.2285780 |
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. |
产权排序 | 2 |
会议录 | AOPC 2017: Optical Sensing and Imaging Technology and Applications |
会议录出版者 | SPIE |
语种 | 英语 |
ISSN号 | 0277786X |
ISBN号 | 9781510614055 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29917] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
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]. 见:. Beijing, China. 2017-06-04. |
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