2026-04-19T11:31:39Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3751232026-02-07T09:08:14Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Salgueiro Romero, Luis Fernando author Marcello Ruiz, Javier author Vilaplana Besler, Verónica author 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 Reviewed Postprint (author's final draft) 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