Fusion feature multi-scale pooling for water body extraction from optical panchromatic images

Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined water body extraction from optical panchromatic images often experiences serious under- or over- segmentation problems. In this paper, for producing refined water body extraction results from optical panchromatic images, we propose a fusion feature multi-scale pooling for Markov modeling method. Markov modeling includes two aspects: label field initialization and feature field establishment. These two aspects are jointly created by the fusion feature multi-scale pooling process, and this process is proposed to enhance the feature difference between water bodies and land cover. Then, the greedy algorithm in the iteration conditional method is used to extract refined water bodies according to the rebuilt Markov initial label and feature fields. Finally, to prove the effectiveness of proposed method, extensive experiments were used with collected 2.5m SPOT 5 and 1m GF-2 optical panchromatic images and evaluation indexes (precision, recall, overall accuracy, kappa coefficient and boundary detection ratios) to demonstrate that our proposed method can produce more refined water body extraction results than the state-of-the-art methods. The global and local refined indexes are improved by about 7% and 10%, respectively.