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Urban Feature Characterization using High-Resolution Satellite Imagery: Texture Analysis Approach
Fig. 4 shows application cases related to GLCM and GLDV. Fig. 4(A) and (B) are from same quantization level, kernel size, direction, and texture type, except application scheme. In the urban remote sensing containing urban feature characterization or classification, both texture images show significant implication: detection of shadow zone, classification of building types, and recognition of pavement condition in the micro-scale.
This implication dealing with texture measures is not a final one, and some further works are needed: applicability of GLCM and GLDV to urban remote sensing, and selection guide of proper type among texture measures to characterize complicated urban features.  (A) 
(B) Fig. 4. (A) (16, GLCM, 7*7, OMNI, HOMO), (B) (16, GLDV, 7*7, OMNI, HOMO). With these preliminary works concerned texture measures, texture images are also utilized to data fusion. Fig. 5 represents two cases of texture image fusion, and applied data is covering urban area containing complex urban features shown in Fig. 1. This approach is based on HIS (Hue-Intensity-Saturation), one of popular or basic data fusion schems. Input images in Fig. 5(A) are GLCM-based entrophy images by same quantization level, and omni-direction, but each kernel size differ from each other as 11*11, 7*7 and 3*3. While, input images in Fig. 5(B) are different quantization level, application scheme, type, with same kernel size and direction. Though this result is not studied in detailed, two cases of (A) and (B) reveals common distinguishable features, in the visual interpretation. 
(A) 
(B) Fig. 5. Fused cases: (A) HIS = (16, GLCM, 11*11, OMNI, ENT), (16, GLCM, 3*3, OMNI, ENT), (16, GLCM, 7*7, OMNI, ENT), (B) HIS = (2, GLCM, 5*5, OMNI, HOMO), (16, GLDV, 5*5, OMNI, ENT), (8, GLCM, 5*5, OMNI, Energy). |