Texture analysis for three dimensional remote sensing data by 3D GLCM

Fuan Tsai, Chun Kai Chang, Gin Rong Liu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

4 引文 斯高帕斯(Scopus)

摘要

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.

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主出版物標題Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006
頁面430-435
頁數6
出版狀態已出版 - 2006
事件27th Asian Conference on Remote Sensing, ACRS 2006 - Ulaanbaatar, Mongolia
持續時間: 9 10月 200613 10月 2006

出版系列

名字Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006

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???event.eventtypes.event.conference???27th Asian Conference on Remote Sensing, ACRS 2006
國家/地區Mongolia
城市Ulaanbaatar
期間9/10/0613/10/06

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