中国空间科学技术（中英文）2025，Vol.45Issue(1)：162-172,11.DOI:10.16708/j.cnki.1000-758X.2025.0016
基于拉普拉斯边缘增强的SAR影像水体提取研究
Water extraction from SAR images based on Laplacian edge enhancement
摘要
Abstract
In deep learning water extraction,there exists the problem that convolutional neural network has poor recognition effect on low-level semantic features,such as small lakes and small rivers.To solve this problem,a water extraction method based on Laplace edge enhancement is proposed.Synthetic Aperture Radar(SAR)data set is convolved with the pre-processed SAR data set,using the Laplacian operator to generate the Laplacian edge feature layer.Then the original image is fused with the generated edge feature layer to obtain the enhanced edge SAR data set,which makes the water edge clearer.On this basis,DeeplabV3+and U-net semantic segmentation models are used for water extraction.The experiment shows that,compared with the unprocessed DeeplabV3+and U-net models,the two models after Laplace operator processing have improved effect on water extraction in different regions.The U-net model after Laplace operator treatment has the best extraction effect on large water bodies,small lakes and small rivers.关键词
水体提取/深度学习/SAR图像/拉普拉斯边缘增强/语义特征Key words
water extraction/deep learning/SAR image/Laplacian edge enhancement/semantic feature分类
信息技术与安全科学引用本文复制引用
李可,李大成,苏巧梅,杨毅..基于拉普拉斯边缘增强的SAR影像水体提取研究[J].中国空间科学技术（中英文）,2025,45(1):162-172,11.基金项目
国家重点研发计划重点专项(2022YFB3903304) （2022YFB3903304）
