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

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

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 reso...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-01, Vol.11 (1), p.52
Main Authors: Tao, Yu, Muller, Jan-Peter
Format: Article
Language:eng
Subjects:
Artificial intelligence
Deep learning
feature matching
GAN
generative adversarial network
Gotcha
GPT
Image enhancement
Image processing
Image resolution
Landsat
Landsat satellites
Machine learning
MISR
Neural networks
Partial differential equations
Quality
Remote sensing
Restoration
Satellite imagery
Sea ice
Spatial discrimination
Spatial resolution
SRR
super-resolution restoration
University colleges
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Summary: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.
ISSN:2072-4292
2072-4292