基于改进的U-Net卷积神经网络的遥感影像水体信息提取方法

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中文摘要：针对当前遥感影像水体信息提取存在细节水体提取能力较弱、重要特征损失较大的问题,文章提出一种基于改进的U-Net网络实现遥感影像水体信息提取的方法.该方法首先通过引入Resnet残差卷积模块深化传统U-Net网络架构提升特征挖掘能力,并引入Respath残差连接模块减少跳跃连接过程中的语义差距,同时引入PSConv多尺度卷积模块、Eca有效通道注意力机制模块,提高网络特征学习能力,构建 PS-Eca-M ultiresunet网络模型,弥补传统U-Net网络存在的细节特征提取能力较弱问题.选择"2020 年第四届中科星图杯高分遥感图像解译软件大赛"数据集进行实验,结果表明,与传统U-Net网络模型相比,该方法水体提取的平均交并比提高了9.08,像素精度提升了 7.4%.改进的网络提取结果能够有效避免阴影影响,提高对细节水体的提取精度,实现遥感影像水体信息的高精度提取.

外文标题：Water body information extraction method for remote sensing images based on improved U-Net convolutional neural network

外文摘要：Aiming at the problems of weak ability to extract detailed water bodies and large loss of im-portant features in current water body information extraction from remote sensing images,this paper proposes a method to extract water body information from remote sensing images using an improved U-Net network.The method firstly deepens the traditional U-Net network architecture by introducing the Resnet residual convolution module to improve the feature mining ability,and introduces the Res-path residual connection module to reduce the semantic gap in the skip connection process,while in-troducing the PSConv multi-scale convolution module and Eca effective channel attention mechanism module to improve the network feature learning ability,and constructs the PS-Eca-Multiresunet net-work model to compensate for the shallow feature loss problem that exists in general networks.The dataset of 2020 GEOVIS Cup Gaofen Challenge on Automated High-Resolution Earth Observation Im-age Interpretation is selected for the experiment.The results show that the average intersection ratio of water extraction by this method is 9.08 higher than that of the traditional U-Net network model,and the precision of image elements is 7.4%higher than that of the traditional U-Net network model.The improved network extraction results can effectively avoid the influence of shadows,improve the extraction accuracy of detailed water bodies,and achieve high-precision extraction of water body infor-mation from remote sensing images.

外文关键词：

water body extractiondeep learningmulti-scale convolutioneffective channel attention mechanismMultiresunet network

作者：

宋子俊、董张玉、张鹏飞、张远南

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作者单位：

合肥工业大学计算机与信息学院,安徽合肥 230601

工业安全与应急技术安徽省重点实验室,安徽合肥 230601

合肥工业大学智能互联系统安徽省实验室,安徽合肥 230601

关键词：

水体提取深度学习多尺度卷积有效通道注意力机制 Multiresunet网络

基金：

安徽省重点研究与开发计划资助项目安徽省自然科学基金资助项目中央高校基本科研业务费专项资金资助项目

项目编号：

202004a070200302108085MF233JZ2021HGTB0111

出版年：

2024

DOI：

10.3969/j.issn.1003-5060.2024.04.009

合肥工业大学学报(自然科学版)

合肥工业大学

合肥工业大学学报(自然科学版)

CSTPCD北大核心

影响因子：0.608

ISSN：1003-5060

年,卷(期)：2024.47(4)

参考文献量19