Tao, Y;
Muller, J-P;
(2019)
Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System.
Remote Sensing
, 11
(1)
, Article 52. 10.3390/rs11010052.
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Abstract
High spatial resolution Earth observation imagery is considered desirable for many scientific and commercial applications. Given repeat multi-angle imagery, an imaging instrument with a specified spatial resolution, we can use image processing and deep learning techniques to enhance the spatial resolution. In this paper, we introduce the University College London (UCL) MAGiGAN super-resolution restoration (SRR) system based on multi-angle feature restoration and deep SRR networks. We explore the application of MAGiGAN SRR to a set of 9 MISR red band images (275 m) to produce up to a factor of 3.75 times resolution enhancement. We show SRR results over four different test sites containing different types of image content including urban and rural targets, sea ice and a cloud field. Different image metrics are introduced to assess the overall SRR performance, and these are employed to compare the SRR results with the original MISR input images and higher resolution Landsat images, where available. Significant resolution improvement over various types of image content is demonstrated and the potential of SRR for different scientific application is discussed.
| Type: | Article |
|---|---|
| Title: | Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/rs11010052 |
| Publisher version: | https://doi.org/10.3390/rs11010052 |
| Language: | English |
| Additional information: | © 2018 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | MISR, super-resolution restoration, SRR, feature matching, Gotcha, GPT, generative adversarial network, GAN, deep learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10070068 |
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