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Überwachte Methoden für die spektrale Entmischung mit künstlichen neuronalen Netzen

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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Hlavní autor: Anastasiadis, Johannes
Médium: Online
Jazyk:němčina
Vydáno: KIT Scientific Publishing 2023
Témata:
data generation; data augmentation; supervised training; artificial neural network; hyperspectral image; Datenerzeugung; Datenaugmentierung; überwachtes Training; Hyperspektralbild; künstliche neuronale Netze
On-line přístup:OCN: 1404419351
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Shrnutí: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.

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