41 Scopus citations

Abstract

This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. As the kernel size of the moving box is the most important factor for the computation of GLCM-based texture descriptors, a three-dimensional semi-variance analysis algorithm was also developed to determine appropriate moving box sizes for 3D computation of GLCM from different data sets. The developed algorithms were applied to a series of classifications of two remote sensing hyperspectral image cubes and comparing their performance with conventional GLCM textural classifications. Evaluations of the classification results indicated that the developed semi-variance analysis was effective in determining the best kernel size for computing GLCM. It was also demonstrated that textures derived from 3D computation of GLCM produced better classification results than 2D textures.

Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings
PublisherSpringer Verlag
Pages429-440
Number of pages12
ISBN (Print)9783540741954
DOIs
StatePublished - 2007
Event6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007 - Ezhou, China
Duration: 27 Aug 200729 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4679 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007
Country/TerritoryChina
CityEzhou
Period27/08/0729/08/07

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