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Urban Feature Characterization using High-Resolution Satellite Imagery: Texture Analysis Approach
As for GLDV texture measures, it is the sum of the diagonals of the GLCM mentioned above. In GLDV, these types of texture measures are also possible by Hall-Beyer (2004).
Texture measure in GLCM and GLDV needs to interpretation reference. Each measure contains different meaning for this. Homogeneity is measure for uniformity of co-occurrence matrix, and if most elements lie on the main diagonal, its value will be large, compared to other case. Dissimilarity measures how different elements of the co-occurrence matrix are from each other. Contrast measures how most elements do not lie on the main diagonal. Entrophy is to measure randomness, and it will be the maximum when the all elements of the co-occurrence matrix are same. In case of Energy and ASM, they measure extent of pixel pair repetitions and pixel orderliness, respectively. 3. Implementation: GLCM and GLDV Stand-alone application program for GLCM and GLDV texture measure and texture image creation is implemented in this study (Fig. 1). In this program, general graphic image formatted as jpp, tiff, bmp can be used as input data. The menu functions for user selection are as follows: Set_Depth_Level: Grey level quantization into 2 (Binary), 8, and 16 GLCM_Texture GLDV_Texture: Computation and generation of six types of texture measures of Homogeneity, Dissimilarity, Contrast, Entrophy, Energy, ASM(Angular Second Moment) While, a user determines two texture parameters such as window kernel size and direction in the main frame. After the grey value relationships in a target are transformed into the co-occurrence matrix space by a given kernel mask such as 3*3, 5*5, 7*7 and 11*11, the neighboring pixels as one of the four directions as East-West of 0°, North-East of 45°, North-South of 90°, North-West of 135°, and omni-direction will be computed in the co-occurrence matrix space. Among them, texture image of omni-direction is obtained as the average value with those of four directions. 
Fig. 1. GLCM and GLDV texture image generation application program. 4. Texture Analysis and HIS Fusion of Texture Images Several texture images by GLCM and GLDV are presented and investigated, and sample image in Fig. 2 is from Demin (2002). 
Fig. 2. Test image (excerpted from Demin, 2002). Fig. 3 demonstrates direction dependency of texture measures. In Fig. 3, the notation of (8,GLCM, 5*5, E-W, ENT) means quantization level (2,8, or 16), application scheme (GLCM or GLDV), rectangle kernel size (3, 5, 7, or 11), direction (EW, NE, NW, NS, or OMNI), texture type, respectively. In this, ENT and HOMO represent entrophy and homogeneity, respectively. As shown in Fig. 3, two results is only different direction with other same parameters. The interpretation of texture image is somewhat complicated, and direction dependency for texture measure is still problematic. Therefore, the omni-direction is helpful to summarize texture measures. 
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(B) Fig. 3. (A) (8, GLCM, 5*5, E-W, ENT), (B) (8, GLCM, 5*5, NW, ENT). |