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Proceedings Paper

Automatic clustering based on an information-theoretic approach with application to spectral anomaly detection
Author(s): Mark J. Carlotto
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Paper Abstract

An information-theoretic method is described for automatically determining the best number of clusters. It is motivated by Rissanen's minimum description length principle that states the best representation is the one with the fewest bits. The method is evaluated using two different clustering algorithms: a mode finder based on scale-space algorithm, and a vector quantizer (VQ). Synthetic, single- and multi-band image clustering examples are presented. Clusterings produced by the mode finder are shown to better correspond to distinguishable surface categories in the scene than those produced by the VQ algorithm. VQ clusterings are evaluated within an anomaly detector, which detects manmade object/changes as spectral outliers within a set of background clusters. It is shown that the optimal VQ clustering (the one with the fewest bits) produces the best detection performance.

Paper Details

Date Published: 17 May 2006
PDF: 9 pages
Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 623517 (17 May 2006); doi: 10.1117/12.668805
Show Author Affiliations
Mark J. Carlotto, General Dynamics Corp. (United States)


Published in SPIE Proceedings Vol. 6235:
Signal Processing, Sensor Fusion, and Target Recognition XV
Ivan Kadar, Editor(s)

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