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.

Date Published: 19 July 2017
PDF: 12 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980X (19 July 2017); doi: 10.1117/12.2264160

Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)