Urban vegetation information extraction based on deep learning and Gaofen-2 satellite images
Urban vegetation is an important part of ecological civilization construction.Accurately obtaining the distribution of urban vegetation is an important content in the study of urban climate and surface energy balance.For the problems that the accuracy from traditional vegetation information extrac-tion methods is not high,Zhongyuan District of Zhengzhou City is selected as the study area in this paper,the vegetation information in the study area is extracted based on Deeplab V3+deep learning model using Gaofen-2 satellite images,and it is compared with traditional support vector machine,maxi-mum likelihood classification method,and commonly used SegNet deep learning model.The results show that the vegetation information extracted by using the deep learning method proposed in this paper is sig-nificantly better than traditional classification methods and SegNet deep learning model,its accuracy is 87.96％,the recall rate is 91.35％,and the F1-score is 0.90,it can provide some technical support for urban ecological environment evaluation and planning management.
deep learningGaofen-2 satelliteurban vegetationsemantic segmentation model
NETL
NSTL
万方数据
