Collaborative representation with background purification and saliency weight for hyperspectral anomaly detection  

作　　者：Zengfu HOU Wei LI Ran TAO Pengge MA Weihua SHI 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China [2]School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450015,China [3]Urban-Rural Planning Administration Center,Ministry of Housing and Urban-Rural Development of the People's Republic of China,Beijing 100081,China

出　　处：《Science China(Information Sciences)》2022年第1期243-254,共12页中国科学（信息科学）（英文版）

基　　金：supported by National Key Research and Development Project of China(Grant No.017YFB-0503903);National Natural Science Foundation of China(Grant Nos.61922013,61421001,U1833203);Key Scientific and Technological Projects in Henan Province(Grant No.192102210106);Beijing Natural Science Foundation(Grant No.L191004)。

摘　　要：Collaborative representation-based detection(CRD)has been developed in hyperspectral anomaly detection tasks and testified to be very effective;however,heterogeneous pixels in the background may affect the accuracy of linear representation and make its performance suboptimal.To address this issue,a background purification framework based on linear representation is proposed,in which an automatic outlier removal strategy based on initial coefficients is designed to purify the background.In the proposed method,the classic least squares technique is firstly adopted to obtain preliminary linear representation coefficients,which are positively correlated with its contribution to a central testing pixel.Then,using statistical analysis of the representation coefficients,purified background pixels are obtained.Furthermore,a saliency weight is applied to fully utilize the spatial information of inner window pixels.Extensive experiments with three real hyperspectral datasets show that the proposed method outperforms state-of-the-art CRD and other traditional detectors.

关 键 词：HYPERSPECTRAL anomaly detection background purification collaborative representation saliency weight 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]