近年來，三維形式的影像資料越來越普遍，例如遙測領域的高光譜影像，醫學的核磁共振影像和電腦斷層掃描，地質學的震測資料等，提供進行三維紋理分析的可能性。紋理分析為特徵萃取和影像分析中最重要的方法之ㄧ，然而，傳統的紋理分析方法，大部分都集中在二維的紋理特性，鮮少將其拓展至三維形式以處理三維實體資料。 本研究即拓展傳統二維Grey Level Co-occurrence Matrix (GLCM) 至三維形式 (Grey Level Co-occurrence Matrix for Volumetric Data, GLCMVD)，並將其應用至高光譜影像以萃取有用的紋理特徵。就傳統二維GLCM而言，決定在計算時所需的最佳視窗大小一直是重要的研究課題。先前的研究指出，視窗大小對於分類成果佔百分之九十以上的影響量。其原因在於，在計算時需要夠大的視窗，才能包含足夠的資訊描述資料特性，但在紋理分割時則需要較小的視窗，方可突顯不同紋理區塊之邊界。因此，在GLCMVD計算中，如何決定適合的視窗大小亦是一迫切的問題。為了解決此一問題，本研究提出以半變異元分析於三維資料上之應用，計算在GLCMVD中所需要之最佳視窗大小。 研究成果顯示，將半變異元分析應用至三維資料，確實可以找出最佳視窗大小進行GLCMVD運算。此外本研究之成果亦證實，由三維資料所萃取之紋理特徵較二維紋理在分類上有更佳的識別能力。 In recent years, three-dimensional (3D) image formats have become more and more popular, providing the possibility of examining texture as volumetric characteristics. For example, hyperspectral images of remote sensing applications, magnetic resonance imaging and computerized tomography of medical imagery, and seismic data in geology and earthquake researches. Texture-based algorithms are important methods for feature extraction and image analysis. However, traditional texture analysis concentrates on 2D texture properties, few have truly explored the possibility of extending it to 3D forms for volumetric data analysis. This study extended traditional 2D Grey Level Co-occurrence Matrix (GLCM) to a 3D form (Grey Level Co-occurrence Matrix for Volumetric Data, GLCMVD) for extracting useful texture features in hyperspectral image cubes. For traditional 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. During the evaluation, it usually requires a large window size in order to obtain meaningful description of the whole data set. However, for texture segmentation, a small window size is preferred in order to accurately locate the boundaries between different textured regions. Therefore, how to determine the most appropriate box size for GLCMVD 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 GLCMVD. Experimental results of this study indicated that the proposed extended semi-variance analysis could successfully identify appropriate kernel sizes for the GLCMVD computation of different targets. In addition, the results also indicated that texture information derived directly from volumetric data performed better in discriminating individual image features than 2D texture derived form sliced data.
