A High-Order Statistical Tensor Based Algorithm for Anomaly Detection in Hyperspectral Imagery

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.

[1]  赵永超,et al.  A small target detection method for the hyperspectral image based on higher order singular value decomposition (HOSVD) , 2013 .

[2]  Hsuan Ren,et al.  A Parallel Approach for Initialization of High-Order Statistics Anomaly Detection in Hyperspectral Imagery , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[3]  D. Rosario.  A nonparametric F-distribution anomaly detector for hyperspectral imagery , 2005, 2005 IEEE Aerospace Conference.

[4]  Phillip A. Regalia,et al.  On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors , 2001, SIAM J. Matrix Anal. Appl..

[5]  Chein-I Chang,et al.  Anomaly detection and classification for hyperspectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  Qian Du,et al.  Hyperspectral Image Classification Using Band Selection and Morphological Profiles , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[8]  Chein-I Chang.  Hyperspectral Measures for Spectral Characterization , 2003 .

[9]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[10]  Chein-I Chang,et al.  Adaptive causal anomaly detection for hyperspectral imagery , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Yongchao Zhao,et al.  A Small Target Detection Method for the Hyperspectral Image Based on Higher Order Singular Value Decomposition (HOSVD) , 2013, IEEE Geoscience and Remote Sensing Letters.

[12]  Chein-I Chang,et al.  Unsupervised target detection in hyperspectral images using projection pursuit , 2001, IEEE Trans. Geosci. Remote. Sens..

[13]  Chein-I Chang,et al.  A nested spatial window-based approach to target detection for hyperspectral imagery , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Ying Liu,et al.  A Selective KPCA Algorithm Based on High-Order Statistics for Anomaly Detection in Hyperspectral Imagery , 2008, IEEE Geoscience and Remote Sensing Letters.

[15]  Xiaoli Yu,et al.  Comparative performance analysis of adaptive multispectral detectors , 1993, IEEE Trans. Signal Process..

[16]  Xiaoli Yu,et al.  Automatic target detection and recognition in multiband imagery: a unified ML detection and estimation approach , 1997, IEEE Trans. Image Process..

[17]  Heesung Kwon,et al.  Adaptive anomaly detection using subspace separation for hyperspectral imagery , 2003 .

[18]  Liqun Qi,et al.  Eigenvalues of a real supersymmetric tensor , 2005, J. Symb. Comput..

[19]  Felix Hueber,et al.  Hyperspectral Imaging Techniques For Spectral Detection And Classification , 2016 .

[20]  José M. F. Moura,et al.  Efficient detection in hyperspectral imagery , 2001, IEEE Trans. Image Process..

[21]  Yonghua Fang,et al.  Anomaly Detection Based on High-order Statistics in Hyperspectral Imagery , 2006, 2006 6th World Congress on Intelligent Control and Automation.