Comparison of ASTER GDEM and SRTM DEM in deriving the thickness change of Small Dongkemadi Glacier on Qinghai-Tibetan Plateau

Xing, Qiang; Li, Zhen; Zhou, Jianmin; Ping, Zhang

doi:10.1109/igarss.2011.6049892

Cited by 2 publications

(1 citation statement)

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“…Therefore, adding a residual block structure to our 30-layer convolution network can effectively speed up the convergence of the model and avoid the influence of network degradation on the accuracy of the reconstructed DEM. [38]. The SRTM data are a global DEM that was jointly surveyed by NASA and the national mapping agency of the United States Department of Defense (NIMA), and it has experienced several revisions.…”

Section: Methodsmentioning

confidence: 99%

Super-resolution reconstruction of a digital elevation model based on a deep residual network

Jiao

Wang

et al. 2020

Open Geosciences

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The digital elevation model (DEM) is an important basic data tool applied in geoscience applications. Because of its high cost and long development cycle of enhancing hardware performance, designing the related models and algorithms to improve the resolution of DEM is of considerable significance. At present, there is little research on DEM super-resolution based on deep learning, and the results of the reconstructed DEMs obtained by existing methods are inaccurate. Therefore, deepening of the network layers is utilized to improve the accuracy of a reconstructed DEM. This paper designs a neutral network model with 30 convolutional layers to learn the feature mapping relationship between a low- and high-resolution DEM. To avoid the problem of network degradation caused by increasing the number of convolutional layers, residual learning is introduced to accelerate the convergence speed of the model, thereby preferably realizing the DEM super-resolution process. The results show that DEM super-resolution based on a deep residual network is better than that obtained using a neural network with fewer convolutional layers, and the reconstructed result of the DEM based on a deep residual network is remarkably improved in terms of the peak signal to noise ratio and visual effect.

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Section: Methodsmentioning

confidence: 99%