Texture Analysis for Correcting and Detecting Classification Structures in Urban Land Uses; "Metropolitan area case study - Spain"

Texture analysis can be a good indicator of the presence of buildings and other objects and they are usually easier to detect than the often-complex multi-textured objects which cause them. Spot 5 images present complex scene of urban area. However, behind this complexity, within a focused area, buildings and industries tend to be aligned following some specific direction [Sohn, G., et al., 2001]. Elements of built-form, which together with land surface, physical infrastructure and communication networks comprise artificialised areas, tend to align in some dominant direction in a small area and possess geometric regularity. Therefore, their boundaries also align following these dominant directions in a small area in spite of the acquisition condition. Classification errors are caused by similar reflection (wave length) from different elements inside the satellite image, such as urban areas and irrigated land, affecting the separation between the built-form and non-built-form areas to define them in the classification process. What dose this mean? The amount of pixels in each category will play an important role in defining or increasing the accuracy of the final classification [David, C.H., et al., 2002]. The resulting land activity classification of Spot 5 scenes covering the metropolitan areas (MA) of Madrid and Barcelona form the basis of this study. The result shows that the classification process confused similar parts of land cover. Moreover, unlike other classes in urban areas, the boundary can be successfully segmented by a conventional pixel-base classification method. The promising results from this analysis prove that an amount of pixels in each category boundary could be used as a potential cue for automated detection and for the correction of classification errors which had arisen in the process. This paper focuses on the development of a methodology based on the texture analysis of urban areas that may improve the urban investigation through remote sen- sing. This study can be divided into two fundamental steps: the first, to work over the initial classification results that showed an error between different elements such as urban areas and irrigated field areas that have a similar classification result. The idea is to apply texture analysis to separate the different elements by using a number of pixels in each category boundary. The second, recovering isolated missing urban fabric data. In fact, it is hard to face this problem through the high resolution images for clearer illustration of all urban fabric areas but it is hard to get small elements that occupied small pixels areas to appear in the different classification process, in case these elements surroundings the different pixels that occupied with completely different elements. Texture analysis will play an important role in detecting this isolated data and reducing the error and improving the classification results [Vu, T.T., et al., 2004].