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Implementation of Compressed Sensing algorithms in Python

Antonello, Arthur (2011) Implementation of Compressed Sensing algorithms in Python. Diploma, Instituto Tecnologico de Aeronautica, Engenharia Eletronica.

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Abstract

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 will be validated by using airborne data acquired by the E-SAR sensor of DLR.

Document Type:Thesis (Diploma)
Additional Information:Tutors: Pau Prats, Matteo Nannini
Title:Implementation of Compressed Sensing algorithms in Python
Authors:
AuthorsInstitution or Email of Authors
Antonello, ArthurInstituto Tecnologico de Aeronautica, Engenharia Eletronica, Brazil
Date:December 2011
Refereed publication:Yes
Number of Pages:90
Status:Published
Keywords:SAR tomography, Compressed sensing, Python
Institution:Instituto Tecnologico de Aeronautica, Engenharia Eletronica
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: Matteo Nannini
Deposited On:06 Dec 2011 15:29
Last Modified:06 Mar 2014 14:24

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