A method for obtaining and applying classification parameters in object-based urban rooftop extraction from VHR multispectral images

Object-based methods of urban feature extraction from high spatial resolution remotely sensed data rely on semantic inference of spatial and contextual classification parameters in scenes of regular spatial or material composition. In this study, a supervised statistics-based method of determining and applying discretive parameters of rooftops in urban scenes of irregular composition is presented. After preprocessing to pansharpen IKONOS image data, the method includes the following steps: (1) image segmentation; (2) supervised object-based classification into broad spectral classes including impervious surfaces; (3) spectral, spatial, textural and contextual parameters are developed from statistical comparison of the sample rooftop and other impervious surface objects and (4) these parameters are implemented in a fuzzy logic rule base to separate rooftops from other impervious surfaces. Classification of a test scene results in 93% accuracy of rooftop identification, demonstrating the applicability of the method to the discrimination of spectrally similar but semantically variable classes.