Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods

Carcereri, Daniel (2020) Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods. Master's, University of Trento.

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Item URL in elib:

https://elib.dlr.de/134289/

Document Type:

Thesis (Master's)

Title:

Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Carcereri, Daniel	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED

Date:

October 2020

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Number of Pages:

Status:

Published

Keywords:

Synthetic Aperture Radar (SAR), Quantization, Data Volume Reduction, Convolutional Neural Network (CNN), Deep Learning

Institution:

University of Trento

Department:

Information Engineering and Computer Science

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Space

HGF - Program Themes:

Earth Observation

DLR - Research area:

Raumfahrt

DLR - Program:

R EO - Earth Observation

DLR - Research theme (Project):

R - Projekt TanDEM-X (old)

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute > Spaceborne SAR Systems

Deposited By:

Martone, Michele

Deposited On:

02 Mar 2020 11:56

Last Modified:

12 Nov 2020 10:41

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