DocumentCode :
144197
Title :
Robust anomaly detection in Hyperspectral Imaging
Author :
Frontera-Pons, J. ; Veganzones, M.A. ; Velasco-Forero, S. ; Pascal, F. ; Ovarlez, J.P. ; Chanussot, J.
Author_Institution :
SONDRA Res. Alliance, Supelec, Gif-sur-Yvette, France
fYear :
2014
fDate :
13-18 July 2014
Firstpage :
4604
Lastpage :
4607
Abstract :
Anomaly Detection methods are used when there is not enough information about the target to detect. These methods search for pixels in the image with spectral characteristics that differ from the background. The most widespread detection test, the RX-detector, is based on the Mahalanobis distance and on the background statistical characterization through the mean vector and the covariance matrix. Although non-Gaussian distributions have already been introduced for background modeling in Hyperspectral Imaging, the parameters estimation is still performed using the Maximum Likelihood Estimates for Gaussian distribution. This paper describes robust estimation procedures more suitable for non-Gaussian environment. Therefore, they can be used as plug-in estimators for the RX-detector leading to some great improvement in the detection process. This theoretical improvement has been evidenced over two real hyperspectral images.
Keywords :
covariance matrices; geophysical image processing; geophysical techniques; hyperspectral imaging; object detection; parameter estimation; spectral analysis; statistical analysis; statistical distributions; Mahalanobis distance; RX-detector; background modeling; background statistical characterization; covariance matrix; detection test; hyperspectral imaging; image pixels; mean vector; nonGaussian distribution; parameters estimation; plug-in estimators; robust anomaly detection method; spectral characteristics; target detection; Covariance matrices; Detectors; Gaussian distribution; Hyperspectral imaging; Parameter estimation; Robustness; Vectors; M-estimators; anomaly detection; elliptical distributions; hypespectral imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
Type :
conf
DOI :
10.1109/IGARSS.2014.6947518
Filename :
6947518
Link To Document :
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