skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Change detection for hyperspectral sensing in a transformed low-dimensional space

Abstract

We present an approach to the problem of change in hyperspectral imagery that operates in a two-dimensional space. The coordinates in the space are related to Mahalanobis distances for the combined ('stacked') data and the individual hyperspectral scenes. Although it is only two-dimensional, this space is rich enough to include several well-known change detection algorithms, including the hyperbolic anomalous change detector, based on Gaussian scene clutter, and the EC-uncorrelated detector based on heavy-tailed (elliptically contoured) clutter. Because this space is only two-dimensional, adaptive machine learning methods can produce new change 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 2-D space, and compare the performance of the resulting nonlinearly adaptjve detector to change detectors that have themselves shown good performance.

Authors:
 [1];  [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
984854
Report Number(s):
LA-UR-10-00455; LA-UR-10-455
TRN: US201016%%1642
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: Military Sensing Symposium on Battlefield Survivability & Discrimination ; February 22, 2010 ; Orlando, FL
Country of Publication:
United States
Language:
English
Subject:
99; ALGORITHMS; DETECTION; LEARNING; PERFORMANCE; VECTORS

Citation Formats

Foy, Bernard R, and Theiler, James. Change detection for hyperspectral sensing in a transformed low-dimensional space. United States: N. p., 2010. Web.
Foy, Bernard R, & Theiler, James. Change detection for hyperspectral sensing in a transformed low-dimensional space. United States.
Foy, Bernard R, and Theiler, James. Fri . "Change detection for hyperspectral sensing in a transformed low-dimensional space". United States. https://www.osti.gov/servlets/purl/984854.
@article{osti_984854,
title = {Change detection for hyperspectral sensing in a transformed low-dimensional space},
author = {Foy, Bernard R and Theiler, James},
abstractNote = {We present an approach to the problem of change in hyperspectral imagery that operates in a two-dimensional space. The coordinates in the space are related to Mahalanobis distances for the combined ('stacked') data and the individual hyperspectral scenes. Although it is only two-dimensional, this space is rich enough to include several well-known change detection algorithms, including the hyperbolic anomalous change detector, based on Gaussian scene clutter, and the EC-uncorrelated detector based on heavy-tailed (elliptically contoured) clutter. Because this space is only two-dimensional, adaptive machine learning methods can produce new change 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 2-D space, and compare the performance of the resulting nonlinearly adaptjve detector to change detectors that have themselves shown good performance.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: