title: A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery.
abstract: Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Moreover, these methods usually produce multiple detection maps rather than a single anomaly distribution image. In this study, we exploit the concept of coskewness tensor and propose a new anomaly detection method, which is called COSD (coskewness detector). COSD does not need iteration and can produce single detection map. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.
authors.affiliation: Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Xiurui
authors.initials: X
authors.lastname: Geng
authors.affiliation: Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Kang
authors.initials: K
authors.lastname: Sun
authors.affiliation: Ministry of Education Key Laboratory for Earth System Modelling, Centre for Earth System Science, Tsinghua University, Beijing, China.
authors.forename: Luyan
authors.initials: L
authors.lastname: Ji
authors.affiliation: Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Yongchao
authors.initials: Y
authors.lastname: Zhao