TY - GEN
T1 - 3D computation of gray level co-occurrence in hyperspectral image cubes
AU - Tsai, Fuan
AU - Chang, Chun Kai
AU - Rau, Jiann-Yeou
AU - Lin, Tang Huang
AU - Liu, Gin Ron
PY - 2007
Y1 - 2007
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38149037157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149037157&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:38149037157
SN - 9783540741954
VL - 4679 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 429
EP - 440
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings
T2 - 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007
Y2 - 27 August 2007 through 29 August 2007
ER -