Boundary Extraction of Urban Built-Up Area Based on Luminance Value Correction of NTL Image

Nighttime light image (NTL) is an effective data for urban built-up area. Due to the shortcomings of low resolution and the blooming effect of NTL, the extraction result of the original NTL image is not ideal. We combined point of interest (POI), road network, and land surface temperature (LST) data to propose the POI, road density, and LST-adjusted NTL Index (PRLANI) to correct the luminance value of the NTL image. The proposed index was verified by NTL images of different developed areas and different spatial resolutions. The threshold segmentation method was used to obtain the result of the built-up area extraction from the original NTL and the image modified by the PRLANI. Then, we generated the vector boundary from the built-up area. Taking Changchun urban area as an example, the kappa coefficient based on PRLANI-VIIRS is 0.802, which is 0.293 higher than of NTL-VIIRS. The kappa coefficient based on PRLANI-LJ is 0.838, which is 0.179 higher than that of NTL-LJ. The results show that: the PRLANI can effectively suppress the blooming effect and improve the overestimation of the built-up area caused by light dimming; the PRLANI can enhance the weak light signal in the original NTL image and compensate for the missing information of the built-up area; and the fusion of road network data improves the extraction accuracy and refines the internal structure of built-up area, especially the NPP/VIIRS. The vector boundary was consistent with the built-up area range of reference data, and the visualization effect was good.