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Remote Sens. 2019, 11(1), 52; https://doi.org/10.3390/rs11010052

Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System

Imaging Group, Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH56NT, UK
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Received: 26 October 2018 / Revised: 21 December 2018 / Accepted: 21 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue MISR)
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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. View Full-Text
Keywords: MISR; super-resolution restoration; SRR; feature matching; Gotcha; GPT; generative adversarial network; GAN; deep learning MISR; super-resolution restoration; SRR; feature matching; Gotcha; GPT; generative adversarial network; GAN; deep learning
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Tao, Y.; Muller, J.-P. Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System. Remote Sens. 2019, 11, 52.

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