Aiming at the problems of low efficiency of traditional methods for water body extraction and poor extraction effect of tiny water bodies in large-range and high-resolution remote sensing images,this paper proposes an improved U-Net model for fine extraction of water bodies in remote sensing images.The experiment uses aerial high-resolution visible images as the data source,and the results show that the improved U-Net model is higher than the classical U-Net model,the spectral feature-based method and the classifier-based method in terms of IoU and precision rate indexes.Meanwhile,the improved network water body extraction results are more complete and it can accurately extract small target water bodies.This model improves the performance of the semantic segmentation algorithm for water body extraction,and makes the remote sensing water body extraction work more automatic and intelligent.This study not only verifies the feasibility of aerial sub-meter optical image-based extraction of urban water bodies,but also provides new exploration ideas for future related research.
关键词
水体提取/U-Net/高分遥感影像/深度学习/洞庭湖
Key words
water extraction/U-Net/high-resolution remote sensing image/deep learning/Dongting lake
万方数据