A Clustered Threshold Method for Extracting Urban Built-up Area Using the DMSP/OLS Nighttime Light Images

A clustered threshold method for conveniently and efficiently extracting urban built-up areas has been proposed in this study. This method has broken through the limitations created by single pixel analysis and the administrative boundaries by using built-up objects identified by the recursive connected-region labeling algorithm as basic spatial units. It classifies urban development levels using spatial clustering on the basis of the size of the objects and the DN value of the geographic center of objects and extracts built-up areas based on the optimal threshold sequence as determined from statistical data. It optimizes the geometric morphology of built-up objects by removing small scraps, stuffing internal holes, and smoothing the jagged edges. Extraction results were analyzed and compared to the statistical data found in the China Statistical Yearbook over the years and images derived from Google Earth. These results show that the clustered threshold method can effectively obtain the total acreage and spatial pattern of urban built-up areas, with high validity and reliability in both quantity scale and spatial pattern.