Automated Extraction of Lake Water Bodies in Complex Geographical Environments by Fusing Sentinel-1/2 Data

Abstract

Lakes are an important component of global water resources. Lake water bodies extraction based on satellite remote sensing mainly utilizes optical or radar data. However, due to the influence of water quality, ground features with low reflectivity, and smooth surface features, it is still challenging to accurately extract water bodies in complex geographic environments. In this work, we proposed a lake water bodies extraction method by fusing Sentinel-1/2 data. Firstly, the proposed method analyzed the difference of the spectral polarization features between water and non-water in complex geographical environment. Then, the spectral polarization and water index were fused to multidimensional features by feature stacking. Finally, support vector machines are used to classify. Six typical lakes (including urban, mountains, and polluted and clean lakes) in China were used to verify the mapping accuracy. The results showed that extracting lake water bodies by fusing Sentinel-1/2 data had a better performance than using optical or radar data solely, all types of lakes achieved better extraction results, the overall accuracy of lake water extraction is improved by 3%, and the error of commission and omission is controlled within 6%. Comparative experiments indicate that combine radar polarization information with spectral information is helpful to improve the accuracy of different types of lakes extraction in complex geographical environment.