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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (1) : 33-41     DOI: 10.6046/gtzyyg.2019.01.05
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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
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Abstract  

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.
Keywords hyperspectral      band similarity      linear prediction      learning dictionary      anomaly detection      low rank decomposition      sparse     
:  TP79  
Corresponding Authors: Kun TAN     E-mail: tankuncu@gmail.com
Issue Date: 15 March 2019
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Zengfu HOU
Rongyuan LIU
Bokun YAN
Kun TAN
Cite this article:   
Zengfu HOU,Rongyuan LIU,Bokun YAN, et al. Hyperspectral imagery anomaly detection based on band selection and learning dictionary[J]. Remote Sensing for Land & Resources, 2019, 31(1): 33-41.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.05     OR     https://www.gtzyyg.com/EN/Y2019/V31/I1/33
Fig.1  Framework of the proposed method
Fig.2  HyMap data set
Fig.3  Detection results and ROC curves of HyMap simulation data set
指标 GRX LRX UNRS-MD LRRaLD LPaBS-
LRRaLD
AUC 0.748 35 0.832 27 0.905 54 0.933 74 0.937 00
时间/s 0.538 100.000 165.900 60.300 49.180
Tab.1  Comparison of AUC and execution time using HyMap simulation data
Fig.4  HYDICE data set
Fig.5  Detection results and ROC curves of HYDICE data set
指标 GRX LRX UNRS-MD LRRaLD LPaBS-
LRRaLD
AUC 0.987 23 0.949 27 0.973 19 0.997 27 0.997 52
时间/s 0.178 1 61.440 0 98.380 0 85.370 0 56.140 0
Tab.2  Comparison of AUC and execution time using HYDICE data set
Fig.6  Hyperion data set
Fig.7  Detection results and ROC curves of Hyperion data set
指标 GRX LRX UNRS-MD LRRaLD LPaBS-
LRRaLD
AUC 0.997 82 0.730 53 0.999 62 0.999 60 0.999 83
时间/s 0.331 6 176.50 276.60 110.60 83.58
Tab.3  Comparison of AUC and execution time using Hyperion data set
Fig.8  Hyspex data set
Fig.9  Detection results and ROC curves of Hyspex data set
指标 GRX LRX UNRS-MD LRRaLD LPaBS-
LRRaLD
AUC 0.848 51 0.654 31 0.692 50 0.877 18 0.910 69
时间/s 5.338 4 494 5 500 430.5 109.5
Tab.4  Comparison of AUC and execution time using Hyspex data set
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