An Exploration of Frequency-Based Multi-Scale Multi-Decomposition Techniques for Effective Urban Mapping

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Despite significant advances in geographic information science, accurately classifying high-resolution digital images into urban land categories remains a challenge. The high frequency appearance of spatial features is the major limitation in urban mapping. The conventional classifiers employ spectral information based on single pixel values without considering the images' spatial properties. Current geospatial approaches are also ineffective, since they are incapable of extracting different spatial features in different directions at multiple scales. Wavelet theory could open up a lot of opportunities for characterizing complex features, since it provides four different pieces of spatial information at each scale. The investigator will develop a wavelet based framework and operational algorithms to identify urban classes accurately by combining different spatial information in different direction at multiple scales. For comparison purposes, this project will also develop and evaluate other geospatial techniques - fractal isarithm, fractal variogram, lacunarity, spatial autocorrelation, and G index.This research will have implications for scale dependency of geographic phenomena, data mining, pattern recognition, and self-similarity of features. Results and findings from the project will provide new opportunities for modeling and understanding urban systems. This study will also reveal the advantages and weaknesses of different geospatial techniques, and help improve automated analysis procedures.