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Proceedings Paper

K-means reclustering: an alternative approach to automatic target cueing in hyperspectral images
Author(s): Raymond S. Wong; Gary E. Ford; David W. Paglieroni
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Paper Abstract

An approach to automatic target cueing (ATC) in hyperspectral images, referred to as K-means reclustering, is introduced. The objective is to extract spatial clusters of spectrally related pixels having specified and distinctive spatial characteristics. K-means reclustering has three steps: spectral cluster initialization, spectral clustering and spatial re-clustering, plus an optional dimensionality reduction step. It provides an alternative to classical ATC algorithms based on anomaly detection, in which pixels are classified as type anomaly or background clutter. K-means reclustering is used to cue targets of various sizes in AVIRIS imagery. Statistical performance and computational complexity are evaluated experimentally as a function of the designated number of spectral classes (K) and the initially specified spectral cluster centers.

Paper Details

Date Published: 25 July 2002
PDF: 11 pages
Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); doi: 10.1117/12.477023
Show Author Affiliations
Raymond S. Wong, Univ. of California/Davis (United States)
Gary E. Ford, Univ. of California/Davis (United States)
David W. Paglieroni, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 4726:
Automatic Target Recognition XII
Firooz A. Sadjadi, Editor(s)

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