典型文献
Ephemeral gully recognition and accuracy evaluation using deep learning in the hilly and gully region of the Loess Plateau in China
文献摘要:
Ephemeral gullies are widely distributed in the hilly and gully region of the Loess Plateau and play a unique role in the slope gully erosion system.Rapid and accurate identification of ephemeral gullies impacts the distribution law and development trend of soil erosion on the Loess Plateau.Deep learning algorithms can quickly and accurately process large data samples that recognize ephemeral gullies from remote sensing images.Here,we investigated ephemeral gullies in the Zhoutungou watershed in the hilly and gully region of the Loess Plateau in China using satellite and unmanned aerial vehicle images and combined a deep learning image semantic segmentation model to realize automatic recognition and feature extraction.Using Accuracy,Precision,Recall,F1 value,and AUC,we compared the ephemeral gully recognition results and accuracy evaluation of U-Net,R2U-Net,and SegNet image semantic segmentation models.The SegNet model was ranked first,followed by the R2U-Net and U-Net models,for ephemeral gully recognition in the hilly and gully region of the Loess Plateau.The ephemeral gully length and width between predicted and measured values had RMSE values of 6.78 m and 0.50 m,respectively,indicating that the model has an excellent recognition effect This study identified a fast and accurate method for ephemeral gully recognition in the hilly and gully region of the Loess Plateau based on remote sensing images to provide an academic reference and practical guidance for soil erosion monitoring and slope and gully management in the Loess Plateau region.
文献关键词:
中图分类号:
作者姓名:
Boyang Liu;Biao Zhang;Hao Feng;Shufang Wu;Jiangtao Yang;Yufeng Zou;Kadambot H.M.Siddique
作者机构:
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,Northwest A&F University,Yangling,712100,China;Institute of Water Saving Agriculture in Arid Areas of China,Northwest A&F University,Yangling,712100,China;College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling,712100,China;Department of Foreign Languages,Northwest A&F University,Yangling,712100,China;The UWA Institute of Agriculture and School of Agriculture&Environment,The University of Western Australia,Perth,WA,6001,Australia
文献出处:
引用格式:
[1]Boyang Liu;Biao Zhang;Hao Feng;Shufang Wu;Jiangtao Yang;Yufeng Zou;Kadambot H.M.Siddique-.Ephemeral gully recognition and accuracy evaluation using deep learning in the hilly and gully region of the Loess Plateau in China)[J].国际水土保持研究(英文),2022(03):371-381
A类:
Ephemeral,Zhoutungou
B类:
gully,recognition,accuracy,evaluation,using,deep,learning,hilly,region,Loess,Plateau,China,gullies,widely,distributed,play,unique,role,slope,erosion,system,Rapid,identification,ephemeral,impacts,distribution,law,development,trend,soil,Deep,algorithms,can,quickly,accurately,process,large,data,samples,that,recognize,from,remote,sensing,images,Here,investigated,watershed,satellite,unmanned,aerial,vehicle,combined,semantic,segmentation,realize,automatic,feature,extraction,Using,Accuracy,Precision,Recall,compared,results,R2U,SegNet,models,was,ranked,first,followed,by,length,width,between,predicted,measured,values,had,RMSE,respectively,indicating,has,excellent,effect,This,study,identified,fast,method,provide,academic,reference,practical,guidance,monitoring,management
AB值:
0.404536
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