Comparative study of upsampling methods for super-resolution in remote sensing

Abstract

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a lowresolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution, are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved. The best results are obtained training a model with RGB-IR channels and using progressive upsampling.

This work has been partially supported by the ARTEMISAT-2 (CTM2016-77733-R) and MALEGRA TEC2016-75976-R projects, funded by the Spanish AEI, FEDER funds,and by the Spanish Ministerio de Economía y Competitividad, respectively. L.S.R. would like to acknowledge the BECAL (Becas Carlos Antonio López) scholarship for the financial support.

Peer Reviewed

Postprint (author's final draft)