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

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.