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This master thesis explores the application of compressive sensing in satellite Earth
observation instruments. Firstly, a general state of the art of compressive sensing is
made by introducing the mathematical concepts and describing some existing designs
that implement the method. The essence of compressive sensing consists in reconstructing
images with fewer measurements than in classical imaging. The method can bring drastic
reduction of data quantity requirements and detector sizes as well as an increase of spatial
resolution. These advantages are particularly interesting in Earth observation instruments
considering the vast amount of data that they generate and the size limitations of satellites.
This is even more considerable in the infrared spectrum where detectors are typically
large.
A deep learning compressive sensing reconstruction algorithm dubbed ISTA-Net+ is
tested an proved to work on satellite multispectral data during simulations. Finally, a
complete compressive sensing experimental chain has been implemented within laboratory
environment. For the reconstruction, the hardware-compressed data could not be passed to
the ISTA-Net+ algorithm, thus a simpler algorithm applying an inpainting using iterative
hard thresholding is applied. The experiment is satisfactory and the method is proven to
work. Nonetheless, the optical system has to be optimized and a more efficient algorithm
must be implemented. Therefore, this work opens the way to further improvements and
investigations.

Compressive Sensing --- Earth Observation --- Signal processing --- Optics --- Physique, chimie, mathématiques & sciences de la terre > Aérospatiale, astronomie & astrophysique