Mapping mountain glaciers using an improved U-Net model with cSE  

作　　者：Suzheng Tian Yusen Dong Ruyi Feng Dong Liang Lizhe Wang 

机构地区：[1]School of Computer Science,China University of Geosciences,Wuhan,People’s Republic of China [2]Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,People’s Republic of China [3]University of Chinese Academy of Sciences,Chinese Academy of Sciences,Beijing,People’s Republic of China [4]International Research Center of Big Data for Sustainable Development Goals,Beijing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2022年第1期463-477,共15页国际数字地球学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China(No.41925007);the National Natural Science Foundation of China(No.U1711266).

摘　　要：Global warming is melting glaciers.Changes in mountain glaciers have a tremendous impact on human life.Regular identification and extraction of glaciers from satellite images are necessary.However,when studying glaciers,materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification process.Therefore,in this study of glacier extraction,we used an improved U-Net model(a channel-attention U-Net)to map glaciers.The model was trained on Landsat 8 Operational Land Imager(OLI)data and a Shuttle Radar Topography Mission(SRTM)digital elevation model(DEM),and was tested on glaciers in the Pamir Plateau.The results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and GlacierNet.The obtained results were fine-tuned by the conditional random field model,effectively reducing background misidentification.

关 键 词：U-Net channel-attention mechanism conditional random field glacier extraction Pamir Plateau 

分 类 号：P343.6[天文地球—水文科学] P2[天文地球—地球物理学]