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Transfer Learning for transferring machine-learning based models among various hyperspectral sensors

 
: Menz, Patrick; Backhaus, Andreas; Seiffert, Udo

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Fulltext (PDF; )

Verleysen, M. ; International Neural Network Society:
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018. Proceedings : Bruges, Belgium, April 24, 25, 26, 2019
Louvain-la-Neuve: Ciaco, 2019
ISBN: 978-2-87587-065-0
ISBN: 978-2-87587-066-7
pp.589-594
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) <27, 2018, Bruges>
English
Conference Paper, Electronic Publication
Fraunhofer IFF ()

Abstract
Using previously generated machine learning models under changing sensor hardware with nearly the same performance is a desirable goal. This constitutes a model transfer problem. We compare a Radial Basis Function Network adapted for transfer learning to a classical data alignment approach. This approach to transfer machine-learning models is tested on a task of material classification using hyperspectral imaging recorded with different camera systems and the aim to make camera systems interchangeable. The results show that a machine-learning based algorithm outperforms a state-of-the-art hyperspectral data alignment algorithm.

: http://publica.fraunhofer.de/documents/N-592919.html