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

研究成果: Conference contribution

34 引文 (Scopus)

摘要

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.

原文English
主出版物標題Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings
頁面429-440
頁數12
4679 LNCS
出版狀態Published - 2007
事件6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007 - Ezhou, China
持續時間: 2007 八月 272007 八月 29

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4679 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007
國家China
城市Ezhou
期間07-08-2707-08-29

指紋

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)

引用此文

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

研究成果: Conference contribution

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N2 - 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. 3D computation of gray level co-occurrence in hyperspectral image cubes. 於 Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings. 卷 4679 LNCS. 2007. p. 429-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).