Antonello, Arthur (2011) Implementation of Compressed Sensing Algorithms in Python. Diploma. DLR-Interner Bericht.
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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.
|Document Type:||Monograph (DLR-Interner Bericht, Diploma)|
|Additional Information:||Betreuer: Pau Prats, Matteo Nannini|
|Title:||Implementation of Compressed Sensing Algorithms in Python|
|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)|
|Institutes and Institutions:||Microwaves and Radar Institute > SAR Technology|
|Deposited By:||Jens Fischer|
|Deposited On:||20 Oct 2011 09:25|
|Last Modified:||20 Oct 2011 09:25|
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