The PCA-NDWI Urban Water Extraction Model Based on Hyperspectral Remote Sensing

Abstract

Accurate extraction of water bodies is the basis of remote sensing monitoring of water environments. Due to the complex types of ground objects around urban water bodies, high spectral and spatial resolution are needed to achieve accurate extraction of water bodies. Addressing the limitation that most spectral index methods used for water body extraction are more suitable for open waters such as oceans and lakes, this study proposes a PCA-NDWI accurate extraction model for urban water bodies based on hyperspectral remote sensing, which combines Principal Component Analysis (PCA) with Normalized Difference Water Index (NDWI). Furthermore, aiming at the common water shadow problem in urban hyperspectral remote sensing images, the advantages of the PCA-NDWI model were further verified by experiments. By comparing the accuracy and F1-Measure of the PCA-NDWI, NDWI, HDWI, and K-means models, the results demonstrated that the PCA-NDWI model was better than the other tested methods. The accuracy and F1-Measure of the PCA-NDWI model water extraction data were 0.953 and 0.912, respectively, and the accuracy and F1-Measure of the PCA-NDWI model water shadow extraction data were 0.858 and 0.872, respectively. Therefore, the PCA-NDWI model can effectively separate shadows and the surrounding features of urban water bodies, accurately extract water body information, and has great application potential in water resources management.