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.
Highlights
High spatial resolution imaging data is always considered desirable in many scientific and commercial applications of Earth Observation (EO) satellite data
We introduce the University College London (UCL) MAGiGAN super-resolution restoration (SRR) system based on multi-angle feature restoration, estimating an observation/degradation model, and using generative adversarial network (GAN) as a further refinement process
We demonstrate MISR SRR results from four test sites at the Railroad Valley, the
Summary
High spatial resolution imaging data is always considered desirable in many scientific and commercial applications of Earth Observation (EO) satellite data. Given the physical constraints of the imaging instruments themselves, we always need to trade-off spatial resolution against launch mass, usable swath-width, and telecommunications bandwidth for transmitting data back to the Earth. One solution to this is through the application of super-resolution restoration (SRR) techniques to combine image information from repeat observations at multiple viewing angles, exploiting information learnt from multiple imaging sources, to generate images at much higher spatial resolutions.
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