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Überwachte Methoden für die spektrale Entmischung mit künstlichen neuronalen Netzen

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In 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 da...

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Xehetasun bibliografikoak
Egile nagusia: Anastasiadis, Johannes (auth)
Formatua: Baliabide elektronikoa Liburu kapitulua
Argitaratua: KIT Scientific Publishing 2023
Saila:Forschungsberichte aus der Industriellen Informationstechnik 29
Gaiak:
Electrical engineering
data generation; data augmentation; supervised training; artificial neural network; hyperspectral image; Datenerzeugung; Datenaugmentierung; überwachtes Training; Hyperspektralbild; künstliche neuronale Netze
Sarrera elektronikoa:OAPEN Library: download the publication
OAPEN Library: description of the publication
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Deskribapena
Gaia:In 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.
Deskribapen fisikoa:1 electronic resource (198 p.)
ISBN:KSP/1000159281
Sartu:Open Access

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