Evaluation of Urban Built-up Area Improvement Index Based on Weighted Fusion of Multi-source Data
The research on multi-source data extraction in built-up areas predominantly emphasizes equal-weight fusion,overlooking the variations in information content among diverse data sources.The paper proposes an improvement to the urban built-up area extraction index from previous studies through a multi-source data weighted fusion method.The aim is to address the issue of inaccurate built-up area extraction resulting from the direct fusion of data with different quality levels.Firstly,construct an improved PREANI index by integrating nighttime light data,NDVI,road network data,and POI.Then,incorporate impervious surface data and temperature data to formulate both the unweighted index(UCI)and the weighted index(WCI).Finally,employ the iterative method and dynamic threshold method to extract the built-up area and evaluate the three indices separately.The results show that:the Kappa coefficient of PREANI has improved to 0.80,and UCI,with the incorporation of temperature and impervious surface data,increases the Kappa coefficient to 0.83;the constructed area contour extracted by WCI is more accurate and performs well in enhancing the detailed comparison of urban and rural built-up areas and improving the ability to distinguish edge features,and its Kappa coefficient,recall,precision,and F1 score are all above 0.85.
built-up area extractionmulti-source data weighted fusionNTLroad networkPOIimpervious surface
NETL
NSTL
万方数据
