Share Email Print
cover

Proceedings Paper

Improved outlier identification in hyperspectral imaging via nonlinear dimensionality reduction
Author(s): C. C. Olson; J. M. Nichols; J. V. Michalowicz; F. Bucholtz
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

We use a nonlinear dimensionality reduction technique to improve anomaly detection in a hyperspectral imaging application. A nonlinear transformation, diffusion map, is used to map pixels from the high-dimensional spectral space to a (possibly) lower-dimensional manifold. The transformation is designed to retain a measure of distance between the selected pixels. This lower-dimensional manifold represents the background of the scene with high probability and selecting a subset of points reduces the computational overhead associated with diffusion map. The remaining pixels are mapped to the manifold by means of a Nystr¨om extension. A distance measure is computed for each new pixel and those that do not reside near the background manifold, as determined by a threshold, are identified as anomalous. We compare our results with the RX and subspace RX methods of anomaly detection.

Paper Details

Date Published: 13 May 2010
PDF: 5 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769507 (13 May 2010); doi: 10.1117/12.851811
Show Author Affiliations
C. C. Olson, U.S. Naval Research Lab. (United States)
J. M. Nichols, U.S. Naval Research Lab. (United States)
J. V. Michalowicz, U.S. Naval Research Lab. (United States)
F. Bucholtz, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top