Proba-V Super-Resolution in combination with Sentinel-2
Gabriele Inzerillo
Proba-V Super-Resolution in combination with Sentinel-2.
Rel. Enrico Magli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
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Abstract
In the field of Deep Learning, and more specifically Computer Vision, one of the tasks that covers quite a lot of interest is that of Super-Resolution: a set of techniques used to improve the resolution of digital images. Super-Resolution techniques have found room for application in many fields, among which one of the most interesting is that of remote sensing and earth observation; this is amply evidenced by the very numerous challenges on the subject organized by space entities such as ESA and NASA. Being able to improve the spatial resolution, i.e. the physical measurement (meters) that represents the size of a pixel, of a satellite image can be particularly useful for a number of reasons including being able to make object classification and detection tasks easier to solve, or again, to monitor at a greater level of detail the earth's surface.
High spatial resolution images, however, are produced by remote sensing satellites less frequently than low spatial resolution ones, which is why having models available that allow one to increase resolution from one or more low resolution images turns out to be a critically important task
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