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Proceedings Paper

Selecting neighbor sets for texture classification using multispectral images
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Paper Abstract

In this paper, we design a decision rule to select optimized neighbor sets for multispectral images. We assume that multispectral images can be modeled by parametric Gaussian Random Fields. From a class of such models with different neighbor sets, we choose the best representation employing bayesian methods. The chosen model accounts for interactions within each of the spectral bands as well as the interaction between different spectral bands in a multispectral image. We evaluate the performance of the neighbor sets for multispectral texture classification.

Paper Details

Date Published: 1 June 2005
PDF: 10 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.603938
Show Author Affiliations
Subhadip Sarkar, Univ. of California/Irvine (United States)
Glenn Healey, Univ. of California/Irvine (United States)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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