2026-04-13T14:21:23Zhttps://recercat.cat/oai/requestoai:recercat.cat:2117/3751232026-02-07T09:08:14Zcom_2072_1033col_2072_452950
RECERCAT
author
Salgueiro Romero, Luis Fernando
author
Marcello Ruiz, Javier
author
Vilaplana Besler, Verónica
2019
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 ReviewedPostprint (author's final draft)
Open Access
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
Remote-sensing images
Deep learning
Super-resolution
WorldView-2
Imatges satel·litàries
Aprenentatge profund
Comparative study of upsampling methods for super-resolution in remote sensing
Conference report