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Automatic Identification of Thaw Slumps Based on Neural network Methods and Thaw Slumping Susceptibility

International Journal of Disaster Risk Science 2023年第4期14卷 539-548页

作者： Huarui Zhang Huini Wang Jun Zhang Jing Luo Guoan Yin School of Civil Engineering and Architecture Wuhan Institute of Technology State Key Laboratory of Frozen Soil Engineering Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences

Thaw slumping is a periglacial process that occurs on slopes in cold environments,where the ground becomes unstable and the surface slides downhill due to saturation with water during *** this study,GaoFen-1 remote sensing and fused multi-source feature data were used to automatically map thaw slumping landforms in the Beilu River Basin of the Qinghai-Tibet *** bi-directional cascade network structure was used to extract edges at different scales,where an individual layer was supervised by labeled edges at its specific scale,rather than directly applying the same supervision to all convolutional neural network ***,we conducted a 5-year multi-scale feature analysis of small baseline subset interferometric synthetic aperture radar deformation,normalized difference vegetation index,and slope,among other *** study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfigured neural networks,ResNet101,VGG16,and *** verification using site surveys and multi-data fusion results,we obtained the best ResNet101 model score of intersection over union of 0.85(overall accuracy of 84.59%).The value of intersection over union of the VGG and ResNet152 are 0.569 and 0.773,*** work provides a new insight for the potential feasibility of applying the designed edge detection method to map diverse thaw slumping landforms in larger areas with high-resolution images.

关键词： bi-directional cascade network Remote sensing SBAS-InSAR Thaw slumping

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Automatic Identifcation of Thaw Slumps Based on Neural network Methods and Thaw Slumping Susceptibility

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International Journal of Disaster Risk Science 2023年第4期14卷 539-548页

作者： Huarui Zhang Huini Wang Jun Zhang Jing Luo Guoan Yin School of Civil Engineering and Architecture Wuhan Institute of TechnologyWuhan 430074China State Key Laboratory of Frozen Soil Engineering Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China

Thaw slumping is a periglacial process that occurs on slopes in cold environments,where the ground becomes unstable and the surface slides downhill due to saturation with water during *** this study,GaoFen-1 remote sensing and fused multi-source feature data were used to automatically map thaw slumping landforms in the Beilu River Basin of the Qinghai–Tibet *** bi-directional cascade network structure was used to extract edges at diferent scales,where an individual layer was supervised by labeled edges at its specifc scale,rather than directly applying the same supervision to all convolutional neural network ***,we conducted a 5-year multi-scale feature analysis of small baseline subset interferometric synthetic aperture radar deformation,normalized diference vegetation index,and slope,among other *** study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfgured neural networks,ResNet101,VGG16,and *** verifcation using site surveys and multi-data fusion results,we obtained the best ResNet101 model score of intersection over union of 0.85(overall accuracy of 84.59%).The value of intersection over union of the VGG and ResNet152 are 0.569 and 0.773,*** work provides a new insight for the potential feasibility of applying the designed edge detection method to map diverse thaw slumping landforms in larger areas with high-resolution images.

关键词： bi-directional cascade network Remote sensing SBAS-InSAR Thaw slumping

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