Wavelets for urban spatial feature discrimination: Comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches

Traditional image processing techniques have proven inadequate for urban mapping using high spatial resolution remote-sensing images. This study examined and evaluated wavelet transforms for urban texture analysis and image classification using high spatial resolution ATLAS imagery. For the purpose of comparison and to evaluate the effectiveness of the wavelet approaches, two different fractal approaches (isarithm and triangular prism), spatial autocorrelation (Moran's I and Geary's C), and spatial co-occurrence matrix of the selected urban classes were examined using 65 x 65, 33 x 33, and 17 x 17 samples with a pixel size of 2.5 m. Results from this study suggest that a multi-bond and multi-level wavelet approach con be used to drastically increase the classification accuracy. The fractal techniques did not provide satisfactory classification accuracy Spatial autocorrelation and spatial co-occurrence techniques were found to be relatively effective when compared to the fractal approaches. It con be concluded that the wavelet transform approach is the most accurate of all four approaches.