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 |
DOI | 10.1117/12.2285780 |
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 | 光谱成像技术研究室 |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Hyperspectral anomal(645KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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