Date: Thu, 28 Mar 2024 19:25:23 +0100 (CET)
Message-ID: <589157117.639.1711650323058@confluence.ifca.es>
Subject: Exported From Confluence
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Deep Learning techniques for Satellit=
e data: Atmospheric Corrections
Remote sensing is a technique that allows obtaining information ab=
out an object through the analysis of data acquired by sensors that are not=
in contact with it, and the detection of changes from a multi-temporal seq=
uence of satellite data is one of its most important applications.=
p>
As is well known, this process requires the application of atmosph=
eric corrections to the images, so that the changes detected are only attri=
butable to real landscape modifications. One of the techniques=
often recommended for applications of classification and detection of chan=
ges is the Dark Object Subtraction (DOS).
The technological advances in the sensors and the spatial and temp=
oral resolutions now available open the door to new ways of monitoring the =
Earth by applying the latest techniques in machine learning or deep learnin=
g to these data.
EGU ( European Geosciences Union) 2019=
strong>
Vienna | Austria | 7=E2=80=9312 April 2019
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