Archivio Istituzionale della RicercaMulti-image super-resolution (MISR) is a technique used to increase the spatial resolution of images acquired by remote sensing platforms by combining the images acquired through multiple revisits. Supervised training of MISR models requires collecting high-resolution images to be used as ground truth. Except for a few special cases, this involves acquiring images from a different satellite, resulting in a shift in the optical and radiometric characteristics with respect to the sensor to be super-resolved. In this paper, we explore the use of Sentinel-2 images to train a MISR model for Proba-V images and highlight the challenges of this pursuit.
Proba-V Multi-Temporal Super-Resolution Guided by Sentinel-2 / Inzerillo, G; Valsesia, D; Magli, E; Niro, F; De Grandis, E. - ELETTRONICO. - (2023), pp. 5139-5142. (Intervento presentato al convegno 2023 IEEE International Geoscience and Remote Sensing Symposium) [10.1109/IGARSS52108.2023.10283177].
Proba-V Multi-Temporal Super-Resolution Guided by Sentinel-2
Inzerillo G;Valsesia D;Magli E;
2023
Abstract
Multi-image super-resolution (MISR) is a technique used to increase the spatial resolution of images acquired by remote sensing platforms by combining the images acquired through multiple revisits. Supervised training of MISR models requires collecting high-resolution images to be used as ground truth. Except for a few special cases, this involves acquiring images from a different satellite, resulting in a shift in the optical and radiometric characteristics with respect to the sensor to be super-resolved. In this paper, we explore the use of Sentinel-2 images to train a MISR model for Proba-V images and highlight the challenges of this pursuit.
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https://hdl.handle.net/11583/2995713
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