Automated detection of impervious surfaces using night-time light and Landsat images based on an iterative classification framework

ABSTRACT

A novel workflow for automated detecting of impervious surface by using night-time light and Landsat images at the individual city scale is proposed. This approach is composed by of three steps. In the beginning, urban, peri-urban and rural regions are detected from the night-time light image by a contour line algorithm. Then, using Landsat TM image, region-specific spectral index analysis is employed to generate initial training samples of urban land covers. Finally, an iterative classification framework is applied to select new training samples by integrating spectral and spatial information and to obtain the final mapping result. Experimental results of two cities show that the proposed method produces higher classification accuracy than the ones using the manual-sampling methods. Moreover, further validations suggested that the spatial information is able to effectively increase the producer’s accuracy of impervious surface. This automated approach is potentially important for large-scale regional impervious surface mapping and application.