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

• Published in: Energy Minimization Methods in Computer Vision and Pattern Recognition pp. 429 - 440
• Main Authors: Tsai, Fuan, Chang, Chun-Kai, Rau, Jian-Yeo, Lin, Tang-Huang, Liu, Gin-Ron
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2007
• Series: Lecture Notes in Computer Science
• Subjects: Hyperion Data; Hyperspectral Imagery; Image Cube; Kernel Size; Minimum Noise Fraction
• ISBN: 354074195X; 9783540741954
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-74198-5_33

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.