Abstract
The SR4RS software includes tools to apply super-resolution methods on remote sensing images. It employs TensorFlow on the deep learning side, and relies on GDAL and the Orfeo ToolBox to deal with geospatial data. The software is written in simple python, and provides user-oriented applications to train and apply state of the art models on images.
Highlights
Single image super-resolution consists in generating a high-resolution image from a low-resolution image
Images generated with super-resolution methods must have an updated metadata, determined from the transformation applied to the low resolution image
While a growing number of software are available on GitHub for traditional images, no such open-source software is available for remote sensing image processing, integrating the specificity of geospatial data
Summary
Single image super-resolution consists in generating a high-resolution image from a low-resolution image. TO CITE THIS ARTICLE: Cresson R 2022 SR4RS: A Tool for Super Resolution of Remote Sensing Images. A second application enables to apply the trained model over low resolution images, to generate synthetic high resolution images.
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