Contrastive Learning for Regression on Hyperspectral Data
Mohamad Dhaini Maxime Berar Paul Honeine Antonin Van Exem
Contrastive learning has demonstrated great effectiveness in representation
learning especially for image classification tasks. However, there is still a
shortage in the studies targeting regression tasks, and more specifically
applications on hyperspectral data. In this paper, we propose a contrastive
learning framework for the regression tasks for hyperspectral data. To this
end, we provide a collection of transformations relevant for augmenting
hyperspectral data, and investigate contrastive learning for regression.
Experiments on synthetic and real hyperspectral datasets show that the proposed
framework and transformations significantly improve the performance of
regression models, achieving better scores than other state-of-the-art
transformations.