A comparison of local variance, fractal dimension, and Moran's I as aids to multispectral image classification

摘要

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 Imagine™ 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.