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Regression-Induced Representation Learning and Its Optimizer: A Novel Paradigm to Revisit Hyperspectral Imagery Analysis

Hong, Danfeng (2019) Regression-Induced Representation Learning and Its Optimizer: A Novel Paradigm to Revisit Hyperspectral Imagery Analysis. Dissertation, Technical University of Munich.

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Official URL: https://mediatum.ub.tum.de/?id=1485285


Item URL in elib:https://elib.dlr.de/134452/
Document Type:Thesis (Dissertation)
Title:Regression-Induced Representation Learning and Its Optimizer: A Novel Paradigm to Revisit Hyperspectral Imagery Analysis
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hong, DanfengTechnical University of Munich (TUM)https://orcid.org/0000-0002-3212-9584
Date:2019
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:211
Status:Published
Keywords:hyperspectral remote sensing, dimensionality reduction, spectral unmixing, multimodal data analysis, machine learning, regression, optimization
Institution:Technical University of Munich
Department:Department of Civil, Geo and Environmental Engineering
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 - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Yao, Jing
Deposited On:19 Mar 2020 08:26
Last Modified:01 Apr 2020 10:39

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