Considering spatial information to improve anomaly detection in heterogeneous hyperspectral images

The aim of this paper is to assess the gain of accuracy obtained by taking into account spatial information for anomaly detection in hyperspectral imaging. A mixture of conditional vector autoregressive model, MixCVAR, is introduced for background pixels. It is exploited to construct an anomaly detector (AD) based on generalized likelihood ratio test (GLRT). In the considered detection task, this detector outperforms the SEM-RX detector [1].

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

[2]  Mireille Guillaume,et al.  Comparisonof local anomaly detection algorithms based on statistical hypothesis tests , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[3]  Josiane Zerubia,et al.  Texture feature analysis using a gauss-Markov model in hyperspectral image classification , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  S Matteoli,et al.  A tutorial overview of anomaly detection in hyperspectral images , 2010, IEEE Aerospace and Electronic Systems Magazine.

[5]  Gérard Govaert,et al.  Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library , 2015 .

[6]  Dirk Borghys,et al.  Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity , 2012, J. Electr. Comput. Eng..

[7]  Heesung Kwon,et al.  Kernel-Based Anomaly Detection in Hyperspectral Imagery , 2006 .

[8]  Qian Du,et al.  Collaborative Representation for Hyperspectral Anomaly Detection , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Yuliya Tarabalka,et al.  Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing , 2009, Journal of Real-Time Image Processing.

[10]  Kenneth W. Bauer,et al.  A Locally Adaptable Iterative RX Detector , 2010, EURASIP J. Adv. Signal Process..

[11]  Stefania Matteoli,et al.  An Overview of Background Modeling for Detection of Targets and Anomalies in Hyperspectral Remotely Sensed Imagery , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[13]  Marco Diani,et al.  Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images , 2005, SPIE Remote Sensing.

[14]  Jocelyn Chanussot,et al.  Robust anomaly detection in Hyperspectral Imaging , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.