An Improved Deep Learning Method for Mapping Glacial Lakes Using Satellite Observation and Its Application
Glacial lake outburst floods(GLOFs)are a serious mountain natural disaster,threatening residents and important infrastructure such as railways and highways in China's high-altitude regions.Automatic and efficient glacial lake remote sensing mapping methods are the basis for glacial lake disaster assessment,monitoring and early warning.However,the existing automatic mapping method is difficult to achieve the accuracy of traditional manual and semi-automatic ice lake extraction methods in actual ice lake extraction applications,and it still needs to be further improved.This study is based upon the original U-Net model and incorporates polar self-attention mechanisms at various bridge connections.The input image features with high resolution are maintained both spatially and channel-wise and refined through the synthesis of nonlinear output features.Then,an improved U-Net glacial lake remote sensing deep learning mapping method is constructed and successfully applied in key areas of the plateau railway.The results are as follows.1)Compared with three classical models,namely PSPNet,DeepLabV3+,and the original U-Net,the improved model has improved performance on various metrics in the glacial lake prediction dataset,with the precision,recall,IoU,and F1 values reaching 0.972 5,0.966 5,0.940 8,and 0.969 4,respectively.Relative to the original U-Net network,the precision,recall,IoU,and F1 values of the revised model have been increased by 5.01%,6.05%,10.73%,and 5.53%,respectively.2)Using Landsat-8 satellite remote sensing data,the improved model is applied to automatically and efficiently extract glacial lake information in the Palong Zangbo and Yigong Zangbo case study areas from 2013 to 2022.The mapping glacial lakes in 2020 have an overall accuracy of 98.16%and an overlap rate of 96.66%with the user-interactive mapped reference data,meeting the research requirements for GLOF assessment and monitoring.This method can be used in the practice of glacial lake disaster prevention and control in major engineering projects such as railways.
remote sensing monitoringglacial lake disasterdeep learningself-attention mechanismU-Net
NETL
NSTL
万方数据
