LoGSRN: Deep Super Resolution Network for Digital Elevation Model*

@article{Shin2019LoGSRNDS,
  title={LoGSRN: Deep Super Resolution Network for Digital Elevation Model*},
  author={Dongjoe Shin and Stephen Spittle},
  journal={2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)},
  year={2019},
  pages={3060-3065}
}
  • Dongjoe Shin, S. Spittle
  • Published 1 October 2019
  • Computer Science
  • 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
The spatial resolution of a Digital Elevation Model (DEM) plays a crucial role in many practical remote sensing applications. However, it is normally limited by the spatial resolution of the raw input imagery, from which a DEM is derived. One solution to enhance the limited resolution of a DEM during the post-processing, is fusing previously obtained high resolution DEM data. This data-driven approach appears particularly promising, considering the recent success of a deep convolutional network… Expand
2 Citations
An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs
  • A. Zhou, Yumin Chen, John P. Wilson, Heng Su, Zhexin Xiong, Qishan Cheng
  • Geology, Computer Science
  • Remote. Sens.
  • 2021
TLDR
An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. Expand
RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks
TLDR
The efficient sub-pixel convolutional neural networks (ESPCN) is improved and the RSPCN method is proposed to generate higher-resolutionDEMs (HRDEMs) from low-resolution DEMs (LRDEMs) to illustrate the feasibility of deep learning methods in the DEM data processing area. Expand

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