Abstract
We present a method for fast anomaly detection in hyperspectral imagery (HSI) based on the support vector data description (SVDD) algorithm. The SVDD is a single class, non-parametric approach for modeling the support of a distribution. A global SVDD anomaly detector is developed that utilizes the SVDD to model the distribution of the spectra of pixels randomly selected from the entire image. Experiments on wide area airborne mine detection (WAAMD) hyperspectral data show improved receiver operating characteristic (ROC) detection performance when compared to the local SVDD detector and other standard anomaly detectors (including RX and GMRF). Furthermore, one-second processing time using desktop computers on several 256 times 256 times 145 datacubes is achieved.
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