3D computation of gray level co-occurrence in hyperspectral image cubes

Fuan Tsai, Chun Kai Chang, Jiann-Yeou Rau, Tang Huang Lin, Gin Ron Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)

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
Pages429-440
Number of pages12
Volume4679 LNCS
Publication statusPublished - 2007
Event6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007 - Ezhou, China
Duration: 2007 Aug 272007 Aug 29

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

Other

Other6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007
CountryChina
CityEzhou
Period07-08-2707-08-29

Fingerprint

Gray Level Co-occurrence Matrix
Hyperspectral Image
Regular hexahedron
Textures
Texture
kernel
Matrix Computation
Three-dimensional
Algorithm Analysis
Texture Feature
Remote Sensing Image
Discriminant
Descriptors
Remote sensing
Series
Computing
Evaluation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tsai, F., Chang, C. K., Rau, J-Y., Lin, T. H., & Liu, G. R. (2007). 3D computation of gray level co-occurrence in hyperspectral image cubes. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings (Vol. 4679 LNCS, pp. 429-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4679 LNCS).
Tsai, Fuan ; Chang, Chun Kai ; Rau, Jiann-Yeou ; Lin, Tang Huang ; Liu, Gin Ron. / 3D computation of gray level co-occurrence in hyperspectral image cubes. Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. Vol. 4679 LNCS 2007. pp. 429-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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.",
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Tsai, F, Chang, CK, Rau, J-Y, Lin, TH & Liu, GR 2007, 3D computation of gray level co-occurrence in hyperspectral image cubes. in Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. vol. 4679 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4679 LNCS, pp. 429-440, 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007, Ezhou, China, 07-08-27.

3D computation of gray level co-occurrence in hyperspectral image cubes. / Tsai, Fuan; Chang, Chun Kai; Rau, Jiann-Yeou; Lin, Tang Huang; Liu, Gin Ron.

Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. Vol. 4679 LNCS 2007. p. 429-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4679 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Tsai F, Chang CK, Rau J-Y, Lin TH, Liu GR. 3D computation of gray level co-occurrence in hyperspectral image cubes. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. Vol. 4679 LNCS. 2007. p. 429-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).