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A sparse dictionary learning method for hyperspectral anomaly detection with capped norm
Ma, Dandan1; Yuan, Yuan1; Wang, Qi2; Wang, Qi (author:crabwq@nwpu.edu.cn)
2017-12-01
会议名称37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
会议录名称2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings
卷号2017-July
页码648-651
会议日期2017-07-23
会议地点Fort Worth, TX, United states
出版者Institute of Electrical and Electronics Engineers Inc.
产权排序1
摘要

Hyperspectral anomaly detection is playing an important role in remote sensing field. Most conventional detectors based on the Reed-Xiaoli (RX) method assume the background signature obeys a Gaussian distribution. However, it is definitely hard to be satisfied in practice. Moreover, background statistics is susceptible to contamination of anomalies in the processing windows, which may lead to many false alarms and sensitiveness to the size of windows. To solve these problems, a novel sparse dictionary learning hyperspectral anomaly detection method with capped norm constraint is proposed. Contributions are claimed in threefold: 1) requiring no assumptions on the background distribution makes the method more adaptive to different scenes; 2) benefiting from the capped norm our method has a stronger distinctiveness to anomalies; and 3) it also has better adaptability to detect different sizes of anomalies without using the sliding dual window. The extensive experimental results demonstrate the desirable performance of our method. © 2017 IEEE.

作者部门光学影像学习与分析中心
DOI10.1109/IGARSS.2017.8127037
收录类别EI ; ISTP
ISBN号9781509049516
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/29944
专题光学影像学习与分析中心
通讯作者Wang, Qi (author:crabwq@nwpu.edu.cn)
作者单位1.Xi'An Institute of Optics and Precision Mechanics of CAS, China
2.School of Computer Science, Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University, China
推荐引用方式
GB/T 7714
Ma, Dandan,Yuan, Yuan,Wang, Qi,et al. A sparse dictionary learning method for hyperspectral anomaly detection with capped norm[C]:Institute of Electrical and Electronics Engineers Inc.,2017:648-651.
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