Impervious surface extraction from multispectral images using morphological attribute profiles and spectral mixture analysis
Morphological attribute profiles (MAPs) are one of the most effective methodologies to characterize the spatial information in remote sensing images. This technique extracts components able to accurately describe objects in the surface of the Earth. In this work, we present a new method for impervious surface extraction from multispectral images using morphological attribute profiles. The proposed method first uses morphological profiles to extend Landsat ETM+ images with additional features. Then, we adopt a vegetation-impervious surface-soil (V-I-S) model and extract three pure classes (endmembers) from these images (i.e. vegetation, impervious surface and soil) using the vertex component algorithm (VCA). Finally, linear spectral mixture analysis (SMA) is conducted to extract the impervious surface percentage (ISP). To test the performance of the proposed method, more than 300 test samples including business districts, residential areas and urban roads are randomly selected from QuickBird imagery with very high resolution. The coefficient of determination R2 is 0.7571, which significantly outperformed other standard techniques in the literature. The obtained experimental results demonstrate that the proposed approach based on morphological attribute profiles can lead to very good extraction and characterization of impervious surfaces.