Evaluation of the impact of super-resolution on GEOSAT-2 multispectral images
Keywords:super-resolution, GEOSAT-2, precision agriculture, artificial intelligence
The growing need to observe the Earth in greater detail means the appearance of new techniques to improve the geometric value of images, preserving their radiometric characteristics. Security and Defence sectors are strategic users of these advances, but not the only ones. By being able to preserve the radiometric characteristics of the data, precision agriculture is a key beneficiary of these improvements. In this way, more detailed data and information can be provided on the specific needs of each crop, which means a direct implication for the farmer, the agricultural companies, and the environment. In this work, the Random Forest and XGBoost methods were applied in order to improve the resolution of the GEOSAT-2 images while preserving their radiometric values. In addition, the quality of the enhanced images was evaluated. Also, the satisfactory evaluation of the improved images is presented, both in terms of resolution and the final quality obtained. This evaluation has been conducted by the Copernicus Coordinated data Quality Control (CQC) team, allowing the addition of a new product to the GEOSAT portfolio, ready to be integrated into the Copernicus Programme data offer.
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Copyright (c) 2023 César Fernández, Carolina de Castro, Lucía García López, María Elena Calleja, Rubén Niño, Silvia Fraile, Rafael Sousa
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This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International