19 July 2017 A study of anomaly detection performance as a function of relative spectral abundances for graph- and statistics-based detection algorithms
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Abstract
We investigate an anomaly detection framework that leverages manifold learning techniques to learn a background model. A manifold is learned from a small, uniformly sampled subset under the assumption that any anomalous samples will have little effect on the learned model. The remaining data are then projected into the manifold space and their projection errors used as detection statistics. We study detection performance as a function of the interplay between sub-sampling percentage and the abundance of anomalous spectra relative to background class abundances using synthetic data derived from field collects. Results are compared against both graph-based and traditional statistical models.
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C. C. Olson, C. C. Olson, M. Coyle, M. Coyle, T. Doster, T. Doster, } "A study of anomaly detection performance as a function of relative spectral abundances for graph- and statistics-based detection algorithms", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980X (19 July 2017); doi: 10.1117/12.2264160; https://doi.org/10.1117/12.2264160
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