LA-UNet网络模型在城市绿地遥感分类中的应用

Application of LA-UNet network model in remote sensing classification of urban green space

城市绿地的精准识别和监测在城市规划和生态管理方面具有重要意义。城市绿地背景复杂,使用传统遥感分类技术容易出现错分粘连的问题。本研究以长沙市雨花区为研究区,高分二号(GF-2)遥感影像为数据源,提出一种基于LA-UNet模型的城市绿地遥感分类方法,该方法以UNet模型为基础,引入DWTCA通道注意力机制模块提升网络对绿地信息的关注度,并使用CARAFE模块对提取特征进行上采样,实现对城市复杂背景下乔木、灌草等多种地类的精准分类。结果表明:利用标准假彩色遥感影像时,LA-UNet模型的城市绿地分类效果最优,总体准确率和平均交并比分别为96.3%和90.9%,比UNet模型分别提高2.8%和6.1%。在波茨坦公开数据集中,LA-UNet模型的总体准确率和平均交并比也同样优于UNet模型,分别提高0.9%和1.8%,表明LA-UNet模型具有较好的鲁棒性和通用性。本研究提出的LA-UNet模型能有效缓解城市绿地错分粘连问题,在城市绿地遥感分类中具有显著优势。改进后的LA-UNet模型比UNet模型具有更小的参数体积,能有效提升城市绿地分类精度。本研究将为城市绿地的精准分类及其空间分布规律研究提供方法参考。

The accurate identification and monitoring of urban green space is of great significance in urban planning and ecological management.In view of the complex background of urban green space,the traditional remote sensing classification technology is prone to the problem of misalignment and adhesion.Taking Yuhua District of Changsha City as the research area and Gaofen-2(CF-2)remote sensing image as the data source,we proposed a remote sensing classification method for urban green space based on the LA-UNet model,which was based on the UNet model.We introduced the DWTCA channel attention mechanism module to improve the attention of the network to green space information,and used the CARAFE module to up sample the extracted features to achieve accurate classification of trees,shrubs and other land types in the complex background of the city.The results showed that the LA-UNet model had the best classification effect of urban green space when using standard false color remote sensing images.The overall accuracy and mean intersection over union were 96.3% and 90.9%,which were 2.8% and 6.1% higher than the UNet model,respectively.In the Potsdam public dataset,the overall accuracy and mean intersection over union of the LA-UNet model were also better than those of the UNet model,which increased by 0.9% and 1.8%,respectively,indicating that the LA-UNet model had good robustness and versatility.In summary,the proposed LA-UNet model could effectively alleviate the problems of misalignment and adhesion of urban green space,with advantages in the remote sensing classification of urban green space.The improved LAUNet model had a smaller parameter volume than the UNet model,which could effectively improve the classification accuracy of urban green space.This study would provide a methodological reference for the accurate classification and understanding the spatial distribution of urban green space.