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Abstract
The accuracy of traditional multispectral maximum‐likelihood image classification is limited by the multi‐modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery with the goal of improving urban land cover classification accuracy. Tools available in the ERDAS ImagineTM software package and the Image Characterization and Modeling System (ICAMS) were used to analyse Landsat ETM + imagery of Atlanta, Georgia. Images were created from the ETM + panchromatic band using the three texture indices. These texture images were added to the stack of multispectral bands and classified using a supervised, maximum likelihood technique. Although each texture band improved the classification accuracy over a multispectral only effort, the addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per‐pixel spectral classification techniques.
Acknowledgements
This research is supported by a research grant from NASA Intelligent Systems research grant (NCC‐2‐1246). We also thank Chris Caird, Ernie Anderson, and Wei Zhao for their technical assistance.
