The new generation of remote sensing sensors with increased spatial, temporal, and spectral resolution imposes large challenges for the (near-real-time) processing of the Big Data generated by the new satellites. In this presentation we show how to use machine learning techniques for solving complex remote sensing problems: (a) parameterization of radiative transfer model simulations, (b) solving inverse problems and (c) merging datasets from different satellites.
 

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• PosterLast updated: May 16, 2019 20:41