This paper examines characteristics of urban land-use and land-cover (LULC) classes using spectral mixture analysis (SMA), and develops a conceptual model for characterizing urban LULC patterns. A Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City was used in this research and a minimum noise fraction (MNF) transform was employed to convert the ETM+ image into principal components. Five image endmembers (shade, green vegetation, impervious surface, dry soil, and dark soil) were selected, and an unconstrained least-squares solution was used to un-mix the MNF components into fraction images. Different combinations of three or four endmembers were evaluated. The best fraction images were chosen to classify LULC classes based on a hybrid procedure that combined maximum-likelihood and decision-tree algorithms. The results indicate that the SMA-based approach significantly improved classification accuracy as compared to the maximum-likelihood classifier. The fraction images were found to be effective for characterizing the urban landscape patterns.
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