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Proceedings Paper

K-means reclustering: algorithmic options with quantifiable performance comparisons
Author(s): Alan W. Meyer; David W. Paglieroni; Cyrus Astaneh
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Paper Abstract

This paper presents various architectural options for implementing a K-Means Re-Clustering algorithm suitable for unsupervised segmentation of hyperspectral images. Performance metrics are developed based upon quantitative comparisons of convergence rates and segmentation quality. A methodology for making these comparisons is developed and used to establish K values that produce the best segmentations with minimal processing requirements. Convergence rates depend on the initial choice of cluster centers. Consequently, this same methodology may be used to evaluate the effectiveness of different initialization techniques.

Paper Details

Date Published: 18 June 2003
PDF: 9 pages
Proc. SPIE 5001, Optical Engineering at the Lawrence Livermore National Laboratory, (18 June 2003); doi: 10.1117/12.500371
Show Author Affiliations
Alan W. Meyer, Lawrence Livermore National Lab. (United States)
David W. Paglieroni, Lawrence Livermore National Lab. (United States)
Cyrus Astaneh, Lawrence Livermore National Lab. (United States)

Published in SPIE Proceedings Vol. 5001:
Optical Engineering at the Lawrence Livermore National Laboratory
Theodore T. Saito; Monya A. Lane, Editor(s)

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