A water extraction method based on airborne hyperspectral images in highly complex urban area

Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; especially, hyperspectral remote sensing image characterized with rich spectrum information provide greater potential for high-accuracy land cover classification, however, the hundreds of bands contained in the image also poses a huge burden on data processing. In this study, aims for water extraction in the densely built urban area, we proposed a fast water extraction method based on spectral analysis of the hyperspectral images. The performance of the new method performs well especially for the extraction of water surface which casts many building shadows. In comparison with the normalized difference water index (NDWI) and K-means classifier, new method obtains significantly higher accuracy than that of NDWI and K-means. Therefore, new method can be used for extracting water with high accuracy, especially in urban areas where shadow caused by high buildings is an important source of classification error.