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25 July 2002 K-means reclustering: an alternative approach to automatic target cueing in hyperspectral images
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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.
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Raymond S. Wong, Gary E. Ford, and David W. Paglieroni "K-means reclustering: an alternative approach to automatic target cueing in hyperspectral images", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); https://doi.org/10.1117/12.477023
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