Research Article An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas Soe Win Myint Nina Lam Department of Geography Department of Geography University of Oklahoma Louisiana State University John Tyler Department of Computer Science Louisiana State University Abstract Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral-based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine-resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high-resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high-resolution ATLAS image, this study illustrates four different wavelet decomposition procedures ± the standard, horizontal, vertical, and diagonal decompositions ± for urban land use and land cover feature extraction with the use of 3333 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C) and the spatial co-occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches. Transactions in GIS, 2002, 6(4): 403±429 ß 2002 Blackwell Publishers Ltd, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA. Address for correspondence: Soe Win Myint, Department of Geography, University of Oklahoma, 100 East Boyd Street, Norman, OK 73019-1007, USA. E-mail: swmyint@ou.edu