一种融合空洞卷积与池化模型的遥感影像水体提取方法  

A Remote Sensing Image Water Extraction Method by Combining Atrous Convolution and Pooling Models

作　　者：赵云飞 薛存金 ZHAO Yunfei;XUE Cunjin(Faculty of Land Resource Engineering,Kunming University of Science and Technology,Kunming 650093,China;Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)

机构地区：[1]昆明理工大学国土资源工程学院,云南昆明650093 [2]中国科学院空天信息创新研究院数字地球重点实验室,北京100094

出　　处：《电子科技》2025年第3期40-46,共7页Electronic Science and Technology

基　　金：国家自然科学基金(42376193)。

摘　　要：植被、阴影和云层等同谱异物物体的干扰导致遥感影像水体提取完整性较低、提取效果差。文中提出一种融合多层次空洞卷积和池化模型的遥感影像水体提取模型MAP_UNet(A UNet of Combining Multi Atrous Convolution and Pooling Model)。该模型以UNet为基准编解码网络,提取水体的不同尺寸特征,引入双递归残差模块防止出现深层网络梯度消失现象,并使用多模块来融合空间空洞卷积和最大池化以捕捉更大范围的特征信息,进一步加强相邻尺度的特征语义关系。为验证所提方法的有效性与先进性,利用高分辨率可见光遥感影像数据进行实验,并与公开深度学习语义分割算法进行对比。实验结果表明,MAP_UNet模型在提取精度和防止同谱异物体误检方面都取得了较好效果,其精确率、召回率、F1-Score和MIoU(Mean Intersection over Union)分别达96.20%、92.64%、87.27%和89.10%,相比UNet(U-shaped Network)、UNet++和UNet_ASPP(UNet with Atrous Spatial Pyramid Pooling Network)网络均有较大提升。The interference of spectral foreign objects such as vegetation,shadows and clouds leads to the low integrity and poor extraction effect of remote sensing images.A remote sensing image water extraction model MAP_UNet(A UNet of Combining Multi Atrous Convolution and Pooling Model)is proposed in this study,which combines multi-level cavity convolution and pooling model.The model uses UNet as the reference codec network to extract different dimensional features of water bodies,introduces double recursive residual module to prevent gradient disappearance of deep network,and uses multiple modules to integrate spatial cavity convolution and maximum pooling to capture a larger range of feature information and further strengthen the feature semantic relationship of adjacent scales.In order to verify the effectiveness and advance of the proposed method,experiments are carried out using high-resolution visible remote sensing image data and the results are compared with open deep learning semantic segmentation algorithm.The experimental results show that the MAP_UNet model has achieved good results in extracting accuracy and preventing misdetection of different objects of the same spectrum.The accuracy rate,recall rate,F1-Score and MIoU(Mean Intersection over Union)are 96.20%,92.64%,87.27%and 89.10%,respectively.Compared with UNet(U-shaped Network),UNet++and UNet ASPP(UNet with Atrous Spatial Pyramid Pooling Network)networks,the performance of proposed method has significant improvement.

关 键 词：水体提取 深度学习 神经网络 空洞卷积 语义分割 UNet 遥感影像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]