Characterizing Urban Landscape by Using Fractal-based Texture Information

This study examined the potential of integrating fractal texture with spectral information on urban landscape characterization by the maximum likelihood image classifier. The fractal texture was first derived from the red band of a Landsat ETM+ image by applying the triangular prism algorithm at different window sizes. The quality of the twenty-five resultant texture bands were then analyzed by fourteen approaches at both of the pre- and post-classification stages. Results showed all evaluations employed at the pre-classification stage are useful to screen out texture bands more useful than others to facilitate the later supervised image classification. This texture information is observed useful only for classifying medium- and low- density residential categories. The window size leading to the best overall classification accuracy is identified as 31 × 31, implying this scale should be kept as a guideline for future studies if a similar methodology is adopted.