Deep Learning techniques for Satellite data: Atmospheric Corrections

Remote sensing is a technique that allows obtaining information about 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 sequence of satellite data is one of its most important applications.

As is well known, this process requires the application of atmospheric corrections to the images, so that the changes detected are only attributable to real landscape modifications. One of the techniques often recommended for applications of classification and detection of changes is the Dark Object Subtraction (DOS).

The technological advances in the sensors and the spatial and temporal resolutions now available open the door to new ways of monitoring the Earth by applying the latest techniques in machine learning or deep learning to these data.

EGU ( European Geosciences Union) 2019

Vienna | Austria | 7–12 April 2019