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dc.contributor.authorAnastasiadis, Johannes
dc.date.accessioned2023-11-17T08:32:33Z
dc.date.available2023-11-17T08:32:33Z
dc.date.issued2023
dc.date.submitted2023-08-29T07:50:59Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/75890
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/121744
dc.description.abstractIn this work, artificial neural networks trained in a supervised manner for spectral unmixing are investigated. For this purpose, a suitable network architecture is determined first. After that, the focus lies on the generation of suitable training data. Model-based methods that generate training data from real pure spectra and data-based methods that augment existing training data are presented and evaluated.
dc.languageGerman
dc.relation.ispartofseriesForschungsberichte aus der Industriellen Informationstechnik
dc.rightsopen access
dc.subject.otherdata generation; data augmentation; supervised training; artificial neural network; hyperspectral image; Datenerzeugung; Datenaugmentierung; überwachtes Training; Hyperspektralbild; künstliche neuronale Netze
dc.titleÜberwachte Methoden für die spektrale Entmischung mit künstlichen neuronalen Netzen
dc.typebook
oapen.identifier.doi10.5445/KSP/1000159281
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages198
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
dc.seriesnumber29
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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open access
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