Impervious Surface Extraction by Linear Spectral Mixture Analysis with Post-Processing Model

Abstract

Accurate estimations of impervious surface areas are essential for urban planning development. Linear spectral mixture analysis (LSMA) is commonly adopted to extract the impervious surface (IS) fraction in a mixed pixel at the subpixel scale. However, owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this study, two Landsat 8 Operational Land Imager (OLI) images-acquired on 20 September 2019 (Path/Row: 121/44) and 14 November 2019 (Path/Row: 122/44)-of Guangzhou and Shenzhen were unmixed by LSMA using spectral indices in endmember selection. A post-processing model using the Dry Bare-soil Index (DBSI) and Normalized Difference Vegetation Index (NDVI) as thresholds was established to improve the IS fraction of the LSMA result. Comparative analysis reveals that LSMA with the post-processing model achieves better performance for IS fraction extraction (R2 = 0.910 and 0.926 and root mean square error [RMSE] = 10.08% and 10.83% for Guangzhou and Shenzhen, respectively), and the distribution of IS is basically consistent with the IS of the actual areas. The post-processing model solves the problem of overestimation of pervious surface and underestimation of impervious surface.

Keywords

• Impervious surface
• post-processing model
• DBSI
• linear spectral mixture analysis