Combining Contextual Information by Integrated Attention Mechanism in Convolutional Neural Networks for Digital Elevation Model Super-Resolution-Reference-Cited by-同舟云学术

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Combining Contextual Information by Integrated Attention Mechanism in Convolutional Neural Networks for Digital Elevation Model Super-Resolution

Published:2024 Issue: Volume:62 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Zhanlong¹^ORCID,Han Xiaoyi²,Ma Xiaochuan³^ORCID

Affiliation:

1. School of Computer Science, China University of Geosciences, Wuhan, China

2. Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan, China

3. School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

Funder

Commission of Science and Technology

National Natural Science Foundation of China

Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10585320.pdf?arnumber=10585320

Reference52 articles.

1. Pluvial flooding: High-resolution stochastic hazard mapping in urban areas by using fast-processing DEM-based algorithms

2. A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods

3. Evaluating performance dependency of a geomorphologic instantaneous unit hydrograph-based hydrological model on DEM resolution

4. CONVOLUTIONAL NEURAL NETWORK BASED DEM SUPER RESOLUTION

5. The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

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