摘要
针对传统全卷积神经网络无法实现高分影像耕地精确提取的问题,以高分二号遥感卫星影像为数据源,采用融合残差结构和多种注意力机制的改进U-Net网络模型(RMAU-Net网络模型)对研究区的耕地进行精细提取。使用耕地样本对RMAU-Net网络模型进行训练,并用训练后的网络模型对测试集影像中的耕地进行提取。为了验证RMAU-Net网络模型提取耕地的效果,选取DeeplabV3+、PSPNet、UNet 3种传统的全卷积神经网络模型与RMAU-Net网络模型进行对比分析。结果表明,RMAU-Net网络模型提取的精确率、召回率、交并比、F1 Score分别为90.36%、90.78%、82.57%、90.57%。与DeepLabv3+、PSPNet和U-Net网络模型相比,RMAU-Net网络模型效果最佳。RMAU-Net网络模型为耕地精细提取提供了新的思路与方法,为农作物面积监测和产量估算等实际应用提供基础数据支持。
In order to solve the problem that the traditional full convolutional neural network could not achieve accurate extraction of cultivated land from high-resolution image,this study used the high-resolution 2 remote sensing satellite imagery as the data source,and used the improved U-Net network model(RMAU-Net network model)that integrated residual structure and multiple attention mechanisms to extract the cultivated land in the study area.The RMAU-Net network model was trained by using cultivated land samples,and cultivated land was extracted from the test set images using the trained network model.In order to verify the effect of RMAU-Net network model in extracting cultivated land,three traditional full Convolutional neural network models,DeeplabV3+,PSPNet and U-Net,were selected for comparative analysis with RMAU-Net network model.The results showed that the accuracy,recall,Intersection over Union,and F1 score of the RMAU-Net network model extraction were 90.36%,90.78%,82.57%,and 90.57%,respectively.Compared with DeepLabv3+,PSPNet,and U-Net network models,the RMAU-Net network model performed the best.RMAU-Net network model provided new ideas and methods for precise extraction of cultivated land,and provided basic data support for practical applications such as crop area monitoring and yield estimation.
作者
袁鹏
王珂
肖坚
YUAN Peng;WANG Ke;XIAO Jian(College of Hydrology and Water Resources,Hohai University,Nanjing210098,China;College of Computer and Information,Hohai University,Nanjing210098,China)
出处
《湖北农业科学》
2023年第8期182-188,196,共8页
Hubei Agricultural Sciences
基金
国家自然科学基金项目(41771358)
广东省水利科技创新项目(2020-04)
中央高校基本科研业务费专项(B210202011)。
关键词
高分影像
耕地提取
深度学习
注意力机制
残差结构
RMAU-Net网络模型
high-resolution imaging
extraction of cultivated land
deep learning
attention mechanism
residual structure
RMAU-Net network model
