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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 17, Iss. 20 — Sep. 28, 2009
  • pp: 17391–17411

Decision Boundaries in Two Dimensions for Target Detection in Hyperspectral Imagery

Bernard R. Foy, James Theiler, and Andrew M. Fraser  »View Author Affiliations


Optics Express, Vol. 17, Issue 20, pp. 17391-17411 (2009)
http://dx.doi.org/10.1364/OE.17.017391


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Abstract

We present an approach to the problems of weak plume detection and sub-pixel target detection in hyperspectral imagery that operates in a two-dimensional space. In this space, one axis is a matched-filter projection of the data and the other axis is the magnitude of the residual after matched-filter subtraction. Although it is only two-dimensional, this space is rich enough to include several well-known signal detection algorithms, including the adaptive matched filter, the adaptive coherence estimator, and the finite-target matched filter. Because this space is only two-dimensional, adaptive machine learning methods can produce new plume detectors without being stymied by the curse of dimensionality. We investigate, in particular, the utility of the support vector machine for learning boundaries in this matched-filter-residual space, and compare the performance of the resulting nonlinearly adaptive detector to well-known alternatives.

© 2009 Optical Society of America

OCIS Codes
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.1545) Remote sensing and sensors : Chemical analysis
(110.4234) Imaging systems : Multispectral and hyperspectral imaging
(280.4991) Remote sensing and sensors : Passive remote sensing

ToC Category:
Remote Sensing and Sensors

History
Original Manuscript: June 1, 2009
Revised Manuscript: September 4, 2009
Manuscript Accepted: September 5, 2009
Published: September 15, 2009

Citation
Bernard R. Foy, James Theiler, and Andrew M. Fraser, "Decision boundaries in two dimensions for target detection in hyperspectral imagery," Opt. Express 17, 17391-17411 (2009)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-17-20-17391


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