Long-term urban impervious surface monitoring using spectral mixture analysis: A case study of Wuhan city in China

Impervious surface has been recognized as a key indicator in assessing urban environments. Referring to the previous research, linear spectral mixture analysis has been widely used to extract impervious surface. In this paper, a material-based endmember selection is applied to support linear spectral unmixing, which suggests that the impervious surface should be classified by their essential impervious materials. Taking Landsat images of Wuhan city for experiment, the results show that the classification accuracy is around 95%. Besides, the extracted impervious surface distribution is highly similar to the ground truth and its variation possesses a similar tendency with Urban Heat Island Intensity.