1.Introduction
In general, texture analysis approaches are used for recognition and distinction of different spatial characteristics of spatial arrangement and frequency of tonal variation related to patterns or phenomena contained in the digital image or the sensor image. Previous works related to texture image have been carried out into the three categories: development and improvement of texture extraction algorithms, comparison between texture extraction schemes, and domain application of extracted texture images. These types of researches are similar to other cases in digital image processing, such as image classification. Some previous researches compared texture analysis methods; Dulyakarn et al. (2000) compared each texture image from GLCM and Fourier spectra, in the classification. Maillard (2003) performed comparison works bewteen GLCM, semi-variogram, and Fourier spectra at the same purpose. Bharati et al. (2004) studied comparison work of GLCM, wavelet texture analysis, and multivariate statistical analysis based on PCA (Principle Component Analysis). In those works, GLCM is suggested as the effective texture analysis schemes.

Considered application of texture images, these secondary images can be utilized to classification with multi-spectral data as an additional layer or layers. Zhang (1999) combined multi-spectral classification and texture filtering for building detection in the urban area, and suggested that this approach increases classification accuracy. On the other hand, Smith et al. (2002) said that texture image is not always good to accuracy of quality in the multi-spectral classification. In the urban remote sensing, texture image analysis is one of useful approaches for urban class extraction and separation in Wang and Hanson (2001) Herold et al. (2003). As for useful types of texture image by GLCM, Franklin et al. (2001) and Kiema (2002) proposed that homogeneity is the most useful one among several types of texture measures. In the case of GLCM algorithms, new algorithms related to performance improvement have been proposed by Al-Janobi (2001) and Clausi and Zhao (2003), in the fast computation aspect.

This study and implementation concerned is based on the original concept of GLCM (Grey Level Co-occurrence Matrix) and GLDV (Grey Level Difference Vector), which are the most popular texture image generation and analysis scheme, summarized by Haralick et al. (1973), Parker (1997) and Hall-Beyer (2004). Various types of extracted texture image were investigated and then HIS data fusion with those also applied to 1 M Pan-sharpened IKONOS image at the urban area composed of complex features.

2. Brief Overview of Texture Analysis: GLCM and GLDV
Basic of GLCM Texture considers the relation between two neighboring pixels in one offset, as the second order texture. 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 so forth.

In the transformation from the image space into the co-occurrence matrix space, the neighboring pixels in one or some of the eight defined directions can be used; normally, four direction such as 0°, 45°, 90°, and 135° is initially regarded, and its reverse direction (negative direction) can be also counted into account.

Therefore, general GLCM texture measure is dependent upon kernel size and directionality, and known measures such as contrast, entropy, energy, dissimilarity, angular second moment (ASM) and homogeneity are expressed as follows:


where i and j are coordinates of the co-occurrence matrix space, g(i,j) is element in the co-occurrence matrix at the coordinates i and j, Ng is dimension of the co-occurrence matrix, as grey value range of the input image. While, in GLCM texture measure, normalization of GLCM matrix, by each value dividing by the sum of element values, is applied, and then g(i,j) is replaced to the probability value. Furthermore, measures related to each texture variables also can use weights related to the distance from the GLCM diagonal.

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