@conference {cSalgueiro,
	title = {Comparative study of upsampling methods for super-resolution in remote sensing},
	booktitle = {International Conference on Machine Vision},
	year = {2019},
	month = {11/2019},
	abstract = {<p>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 low-resolution 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.\&nbsp; The best results are obtained training a model with RGB-IR channels and using progressive upsampling.\&nbsp;</p>},
	keywords = {deep learning, Remote sensing, Super-resolution, WorldView-2},
	author = {Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana}
}
