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1 June 2005 Selecting neighbor sets for texture classification using multispectral images
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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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Subhadip Sarkar and Glenn Healey "Selecting neighbor sets for texture classification using multispectral images", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.603938
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