Automatic and Unsupervised Water Body Extraction Based on Spectral-Spatial Features Using GF-1 Satellite Imagery

Abstract

Water body extraction from remote sensing imagery is an essential and nontrivial issue due to the complexity of the spectral characteristics of various kinds of water bodies and the redundant background information. An automatic multifeature water body extraction (MFWE) method integrating spectral and spatial features is proposed in this letter for water body extraction from GF-1 multispectral imagery in an unsupervised way. This letter first discusses a spatial feature index, called the pixel region index (PRI), to describe the smoothness in a local area surrounding a pixel. PRI is advantageous for assisting the normalized difference water index (NDWI) in detecting major water bodies, especially in urban areas. On the other hand, part of the water pixels near the borders may not be included in major water bodies, $k$ -means clustering is subsequently conducted to cluster all the water pixels into the same group as a guide map. Finally, the major water bodies and the guide map are merged to obtain the final water mask. Our experimental results demonstrate that accurate water masks were achieved for all seven GF-1 imagery scenes examined. Three images with a complex background and water conditions were used to quantitatively compare the proposed method to NDWI thresholding and support vector machine classification, which verified the higher accuracy and effectiveness of the proposed method.