Faculté des Sciences
Faculté des Sciences

Exploring Compressive Sensing for Earth Observation

Thomas, Clément ULiège
Promotor(s) : Georges, Marc ULiège
Date of defense : 29-Jun-2023/30-Jun-2023 • Permalink :
Title : Exploring Compressive Sensing for Earth Observation
Translated title : [fr] Exploration du Compressive Sensing pour l'Observation de la Terre
Author : Thomas, Clément ULiège
Date of defense  : 29-Jun-2023/30-Jun-2023
Advisor(s) : Georges, Marc ULiège
Committee's member(s) : Clermont, Lionel ULiège
Kirkove, Murielle ULiège
Habraken, Serge ULiège
Language : English
Number of pages : 97
Keywords : [en] Compressive Sensing
[en] Earth Observation
[en] Signal processing
[en] Optics
Discipline(s) : Physical, chemical, mathematical & earth Sciences > Space science, astronomy & astrophysics
Research unit : Centre Spatial de Liège
Target public : Researchers
Professionals of domain
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en sciences spatiales, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences


[en] 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
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



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  • Thomas, Clément ULiège Université de Liège > Master sc. spatiales, à fin.


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