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 thawing.In this study,GaoFen-1 re...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 thawing.In 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 Plateau.The 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 outputs.Additionally,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 features.Our study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfgured neural networks,ResNet101,VGG16,and ResNet152.Through 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,respectively.This 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.展开更多
基金supported by the Second Tibetan Plateau Scientifc Expedition and Research Program(STEP)(Grant No.2019QZKK0905)the National Science Foundation of China(Grant No.42071097)+1 种基金the foundation of the State Key Laboratory of Frozen Soil Engineering(Grant No.SKLFSE202003)the 14th Graduate Education Innovation Fund of Wuhan Institute of Technology(Grant No.CX2022164).
文摘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 thawing.In 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 Plateau.The 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 outputs.Additionally,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 features.Our study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfgured neural networks,ResNet101,VGG16,and ResNet152.Through 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,respectively.This 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.
