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Implementation of Compressed Sensing Algorithms in Python

Antonello, Arthur (2011) Implementation of Compressed Sensing Algorithms in Python. DLR-Interner Bericht. Diploma. 90 S.

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Compressed Sensing (CS) is an emerging sampling paradigm that has recently proved to be an effective approach to polarimetric SAR tomography. This work focuses on the practical implementation of CS reconstruction algorithms via convex optimization. Specifically, we used the Python programming language and implemented a second order cone program (SOCP) that deals with multiple looks as well as multiple polarizations simultaneously. Also, special consideration was given to handling complex data appropriately. Finally, the methods are validated by using airborne data acquired by the E-SAR sensor of DLR.

Item URL in elib:https://elib.dlr.de/71280/
Document Type:Monograph (DLR-Interner Bericht, Diploma)
Additional Information:Betreuer: Pau Prats, Matteo Nannini
Title:Implementation of Compressed Sensing Algorithms in Python
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Date:October 2011
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:90
Keywords:Compressed Sensing, Python
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W EO - Erdbeobachtung
DLR - Research area:Space
DLR - Program:W EO - Erdbeobachtung
DLR - Research theme (Project):W - Vorhaben Flugzeug-SAR (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > SAR Technology
Deposited By: Fischer, Jens
Deposited On:20 Oct 2011 11:25
Last Modified:20 Oct 2011 11:25

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