Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/70589
Título: Comparative study of upsampling methods for super-resolution in remote sensing
Autores/as: Salgueiro Romero, Luis
Marcello Ruiz, Francisco Javier 
Vilaplana, Verónica
Clasificación UNESCO: 250616 Teledetección (Geología)
2209 Óptica
Fecha de publicación: 2020
Editor/a: The international society for optics and photonics (SPIE) 
Conferencia: 12th International Conference on Machine Vision, ICMV 2019 
Resumen: 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: http://hdl.handle.net/10553/70589
DOI: 10.1117/12.2557357
Fuente: Proceedings SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331J (31 January 2020)
Colección:Actas de congresos
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