Downscaling Surface Albedo to Higher Spatial Resolutions With an Image Super-Resolution Approach and PROBA-V Satellite Images
Author:
Affiliation:
1. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD, Australia
2. Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Fuenlabrada, Madrid, Spain
Funder
University of Southern Queensland Domestic Ph.D. Research Scholarship
Research and Training Scheme Fee Scholarship from Australian Government
Senetas Corporation under the Australian Postgraduate Research Internship and an International Research Project funded by the Spanish Ministry of Science and Innovation (MICINN), Spain
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10015024.pdf?arnumber=10015024
Reference61 articles.
1. Convolutional neural networks at constrained time cost
2. Depthwise separable convolutions for neural machine translation;kaiser;arXiv 1706 03059,2017
3. Deep Learning for Image Super-Resolution: A Survey
4. Accurate Image Super-Resolution Using Very Deep Convolutional Networks
5. Image Super-Resolution Using Deep Convolutional Networks
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