Hyperspectral imagery anomaly detection based on band selection and learning dictionary
Zengfu HOU1, Rongyuan LIU2, Bokun YAN2, Kun TAN1()
1.Key Laboratory for Land Environment and Disaster Monitoring of NASG,China University of Mining and Technology, Xuzhou 221116,China 2.China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
With the large quantities of redundant information in the hyperspectral imagery, the traditional anomaly detection algorithm using the overall hyperspectral spectrum should consume a larger amount of computing time. Based on the linear prediction and learning dictionary, the authors put forward a novel algorithm. Compared with other low rank representation methods, the linear prediction method with the similarity of the band is utilized to find the least similar band subsets, and then the learning dictionary is implemented to obtain the learning dictionary which can represent the background information of the imagery. In addition, the imagery is divided into low rank matrix and sparse matrix via the low rank and decomposition. Finally, the traditional RXD (Reed-X detector) detection algorithm is utilized to detect the sparse image anomaly. Compared with other methods, the proposed method performs better with lower computational cost. Experimental results demonstrate that the selection of some bands including original information can achieve a good performance without corrupting the original information. It is a fine technique to apply to the hyperspectral imagery anomaly detection.
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