Comparative study of upsampling methods for super-resolution in remote sensing

Luis Salgueiro Romero; Javier Marcello; Verónica Vilaplana

doi:10.1117/12.2557357

31 January 2020 Comparative study of upsampling methods for super-resolution in remote sensing

Luis Salgueiro Romero, Javier Marcello, Verónica Vilaplana

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331J (2020) https://doi.org/10.1117/12.2557357
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

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.

Citation Download Citation

Luis Salgueiro Romero, Javier Marcello, and Verónica Vilaplana "Comparative study of upsampling methods for super-resolution in remote sensing", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331J (31 January 2020); https://doi.org/10.1117/12.2557357

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