23 September 2003 A new approach to anomaly detection in hyperspectral images
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Anomaly detection in hyperspectral imagery is a potentially powerful approach for detecting objects of military interest because it does not require atmospheric compensation or target signature libraries. A number of methods have been proposed in the literature, most of these require a parametric model of the background probability distribution to be estimated from the data. There are two potential difficulties with this. First a parametric model must be postulated which is capable of describing the background statistics to an adequate approximation. Most work has made use of the multivariate normal distribution. Secondly the parameters must be estimated sufficiently accurately - this can be problematic for the covariance matrix of high dimensional hyperspectral data. In this paper we present an alternative view and investigate the capabilities of anomaly detection algorithms starting from a minimal set of assumptions. In particular we only require the background pixels to be samples from an independent and identically distributed (iid) process, but do not require the construction of a model for this distribution. We investigate a number of simple measures of the 'strangeness' of a given pixel spectra with respect to the observed background. An algorithm is proposed for detecting anomalies in a self-consistent way. The effectiveness of the algorithms is compared with a well-known anomaly detection algorithm from the literature on real hyperspectral data sets.
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Philip E. Clare, Philip E. Clare, Mark Bernhardt, Mark Bernhardt, William J. Oxford, William J. Oxford, Sean Murphy, Sean Murphy, Peter Godfree, Peter Godfree, Vicky Wilkinson, Vicky Wilkinson, } "A new approach to anomaly detection in hyperspectral images", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.487030; https://doi.org/10.1117/12.487030

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