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EMMCVPR
2007
Springer

3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes

11 years 7 months ago
3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes
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 GLCMbased 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 texture...
Fuan Tsai, Chun-Kai Chang, Jian-Yeo Rau, Tang-Huan
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where EMMCVPR
Authors Fuan Tsai, Chun-Kai Chang, Jian-Yeo Rau, Tang-Huang Lin, Gin-Ron Liu
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