Please use this identifier to cite or link to this item:
https://accedacris.ulpgc.es/handle/10553/70589
Title: | Comparative study of upsampling methods for super-resolution in remote sensing | Authors: | Salgueiro Romero, Luis Marcello Ruiz, Francisco Javier Vilaplana, Verónica |
UNESCO Clasification: | 250616 Teledetección (Geología) 2209 Óptica |
Issue Date: | 2020 | Publisher: | The international society for optics and photonics (SPIE) | Conference: | 12th International Conference on Machine Vision, ICMV 2019 | 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. | URI: | https://accedacris.ulpgc.es/handle/10553/70589 | DOI: | 10.1117/12.2557357 | Source: | Proceedings SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331J (31 January 2020) |
Appears in Collections: | Actas de congresos |
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.