Robust anomaly detection in hyperspectral imaging

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.
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Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014), Jul 2014, Québec, Canada. pp.n/c, 2014, 〈10.1109/IGARSS.2014.6947518〉
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Contributeur : Miguel Angel Veganzones <>
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Joana Frontera-Pons, Miguel Angel Veganzones, Santiago Velasco-Forero, Frédéric Pascal, Jean-Philippe Ovarlez, et al.. Robust anomaly detection in hyperspectral imaging. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014), Jul 2014, Québec, Canada. pp.n/c, 2014, 〈10.1109/IGARSS.2014.6947518〉. 〈hal-01010418〉

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