Fast Hyperspectral Anomaly Detection via SVDD | IEEE Conference Publication | IEEE Xplore

Fast Hyperspectral Anomaly Detection via SVDD


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, n...Show More

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.
Date of Conference: 16 September 2007 - 19 October 2007
Date Added to IEEE Xplore: 12 November 2007
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Conference Location: San Antonio, TX, USA

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