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

作　　者：李可 李大成[1] 苏巧梅[1] 杨毅 LI Ke;LI Dacheng;SU Qiaomei;YANG Yi(College of Mining Engineering,Taiyuan University of Technology,Taiyuan 030024,China;College of Physics and Optoelectronics,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学矿业工程学院,太原030024 [2]太原理工大学物理与光电工程学院,太原030024

出　　处：《中国空间科学技术(中英文)》2025年第1期162-172,共11页Chinese Space Science and Technology

基　　金：国家重点研发计划重点专项(2022YFB3903304)。

摘　　要：在深度学习水体提取中,存在卷积神经网络对低级语义特征识别效果不佳的问题,比如对小型湖泊、细小河流识别不到。针对此问题,提出了一种基于拉普拉斯(Laplace)边缘增强的水体提取方法,通过拉普拉斯算子对预处理后的SAR(Synthetic Aperture Radar)数据集进行卷积操作,生成拉普拉斯边缘特征层,再将原始图像与生成后的边缘特征层进行融合得到边缘增强后的SAR数据集,使水体边缘更加清晰;在此基础上再利用DeeplabV3+和U-net两种语义分割模型进行水体提取。实验表明,相较于无处理的DeeplabV3+和U-net模型,经过Laplace算子处理后的两种模型对不同地区的水体提取效果均有提升,其中,经过Laplace算子处理后的U-net模型对大型水体、小型湖泊以及细小河流的提取效果最佳。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 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]