Composite Extraction Index to Enhance Impervious Surface Information in Remotely Sensed Imagery

Fast and accurate impervious surface extraction is important to monitor urban dynamics and ensure people's well-being. In order to improve the extraction accuracy of impervious surface, a composite impervious surface extraction index CBI based on NDBI, NDVI and MNDWI is proposed in the paper. The impervious surface extraction images in different land cover areas were compared with the NDBI and NDISI, respectively, and were assessed by using visual and statistical analysis. The results show that CBI has good robustness, less affected by the type of ground objects and sensors. The Kappa coefficients of CBI are the highest in different sensors and OLI images with different land cover areas, and the overall accuracy is above 91%. In the area with high coverage of vegetation and water, the extraction accuracy of CBI was significantly improved. CBI is more suitable for areas with high coverage of vegetation and water and has an improved Kappa coefficient of 0.1116 than that of the areas containing only a small amount of water and vegetation. As the NDISI algorithm uses the thermal infrared band, when the area of the study area is small, its extraction accuracy is low, indicating that the accuracy of the algorithm is affected by the size of the study area.