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Denoising of SAR Data Affected by Quantization Noise using Convolutional Neural Networks

Carcereri, Daniel (2020) Denoising of SAR Data Affected by Quantization Noise using Convolutional Neural Networks. Master's, University of Trento.

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Item URL in elib:https://elib.dlr.de/134289/
Document Type:Thesis (Master's)
Title:Denoising of SAR Data Affected by Quantization Noise using Convolutional Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Carcereri, Danieldaniel.carcereri (at) studenti.unitn.itUNSPECIFIED
Date:September 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Unpublished
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 - Erdbeobachtung
DLR - Research theme (Project):R - Projekt TanDEM-X
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:02 Mar 2020 12:06

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