@inproceedings{0af4fd5880bb4f2fa679a7716059c939,
title = "Texture analysis for three dimensional remote sensing data by 3D GLCM",
abstract = "In recent years, 3D image formats have become more and more popular, providing the possibility of examining texture as volumetric characteristics. This study extended traditional 2D Grey Level Co-occurrence Matrix (GLCM) to a 3D form. For 2D GLCM analysis, a primary issue was to determine the optimal window (kernel) sizes in the computational process. Previous studies demonstrated that the window size could account for 90% of the variability in the results of classification. Therefore, how to determine the most appropriate window size for GLCM computation has become a critical issue. In order to solve this problem, an extended semi-variance analysis was proposed to determine the optimal kernel size for 3D GLCM. Experimental results of this study indicated that the proposed extended semi-variance analysis could successfully identify appropriate kernel sizes for the 3D GLCM computation.",
keywords = "GLCM, Semi-variance, Texture analysis, Volumetric data",
author = "Fuan Tsai and Chang, {Chun Kai} and Liu, {Gin Rong}",
year = "2006",
language = "???core.languages.en_GB???",
isbn = "9781604231380",
series = "Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006",
pages = "430--435",
booktitle = "Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006",
note = "27th Asian Conference on Remote Sensing, ACRS 2006 ; Conference date: 09-10-2006 Through 13-10-2006",
}