Impervious surface extraction in urban areas from high spatial resolution imagery using linear spectral unmixing

Abstract

Impervious surface, as an important indicator of urbanization assessment, plays a significant role in analyzing the climate, environment and hydrologic cycle in urban areas. Impervious surface extraction in urban areas from satellite imagery attracts growing attention in many applications. Recently, the increasing availability of high spatial resolution satellite imagery provides new opportunities for impervious surface extraction at a fine scale. However, impervious surface like asphalt roads, parking lots, and sidewalks is often obscured by tree canopies, which can remarkably underestimate impervious surface area in urban areas. In order to overcome this problem, this study adopts linear spectral unmixing to detect impervious surface information through tree canopies, and further incorporates with object-based classification to mitigate the negative effects of tree canopy obscurity when extracting impervious surface from high spatial resolution imagery. The performance of the proposed impervious surface extraction method is investigated by a subset of QuickBird imagery in Beijing urban areas. Results demonstrate that the proposed method effectively reduces impervious surface underestimation in urban areas by 11.20%, and more accurate impervious surface extraction and mapping can assist government policy makers for timely monitoring of urban hydrological environment.