Share Email Print

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
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top