遙測影像中的特徵大部分呈現不規則及複雜型態，紋理分析能夠考慮鄰近像元間的關係，對地物辨識度能達到不錯的成果，因此是遙測影像分析的重要方法。遙測影像的紋理分析，主要利用以統計法為基礎的灰階共生矩陣萃取特徵，提高分類時地物的識別力。傳統的灰階共生矩陣主要以二維形式產生，而高光譜影像在特徵空間觀點中，已含有volumetric data的特性，故有三維紋理計算的可行性。 過去有研究將傳統的灰階共生矩陣計算拓展成三維形式，且證實能提升分類精度。然而，灰階共生矩陣三維計算仍是二階統計。為了符合真正三維紋理特徵萃取的本質，本研究提出灰階共生張量場的概念，記錄三個像元的灰階出現頻率，並進行三階統計指標運算。 在紋理分析計算因子中，視窗(核)尺寸對分類成果佔有極大的影響。利用半變異元分析決定視窗尺寸，有一定程度的參考價值與不錯的成果。但是高光譜影像立方體其中一個維度屬於光譜資訊而非空間距離，若以空間變異為導向的方式決定光譜方向的視窗尺寸，可能產生偏差的成果。針對此一問題，本文利用光譜分離度分析，藉此求得最能區別各類別組的波長取樣間距，做為三維紋理計算時光譜方向的視窗尺寸。 由於本研究以特徵空間的觀點，視高光譜影像為volumetric data，使得三維紋理計算有其可行性。因此本文先利用真實volumetric data的磁振造影資料萃取三維紋理特徵，進行研究方法的初步驗證，再以高光譜影像立方體測試。研究成果顯示，灰階共生張量場於磁振造影如預期可增進分類精度。而在高光譜影像立方體的分析，以半變異元分析決定空間的視窗尺寸，配合分離度分析訂定光譜方向的視窗大小，可產生較佳的三維紋理計算；而灰階共生張量場在多數的統計指標也能有效地提升特徵萃取與地物分類的成果。 The characteristics of remote sensing imagery exhibit a majority of irregular and complex patterns. Because texture analysis can achieve good results in extracting spatial features from complex images by considering the relationship among adjacent pixels, it is an important method in remote sensing image analysis. Texture analysis of remote sensing imagery mainly uses statistics-based gray level co-occurrence matrix (GLCM) to extract features and improve the classification results. The traditional GLCM is in two-dimensional (2D) form. Because hyperspectral imagery in the feature space has the characteristic of volumetric data, it has a great potential for three-dimensional (3D) texture analysis. Previous studies have extended the computation of traditional GLCM to a 3D form, and performed better in classification. However, the core of 3D computation of GLCM was still in a 2D matrix. To truly explore volumetric texture characteristics, this study further extended texture matrix to a tensor field (Gray Level Co-occurrence Tensor Field, GLCTF) that uses three voxels to extract subtle features from image cubes, and utilizes third order statistical computation. For classification applications, the kernel size for texture computation has a significant impact to the results. For 3D texture computation, kernel size can be determined effectively with semi-variance analysis in the spatial domain. However, in a hyperspectral image cube, one of the dimensions is spectral. Therefore, semi-variance analysis might yield improper kernel size in this dimension. To address this issue, this study developed an algorithm based on separability measures to identity appropriate kernel size in the spectral dimension for 3D texture computation. The developed algorithms were applied to 3D texture computation of Magnetic Resonance Images (MRI), whose dimensions are all spatial, to test its validity. Experimental results demonstrate that GLCTF performs better as expected in real volumetric datasets. Consequently, the method was further extended to extract subtle features from hyperspectral image cubes. Evaluations of the classification results indicate that semi-variance analysis and separability measures can determine more appropriate kernel sizes for 3D texture computation and GLCTF in most indexes has better classification results in the test cases.
