Contrastive Learning for Regression on Hyperspectral Data

• URL: http://arxiv.org/abs/2403.17014v1
• Date: Mon, 12 Feb 2024 21:33:46 GMT
• Title: Contrastive Learning for Regression on Hyperspectral Data
• Authors: Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem,
• Abstract summary: We propose a contrastive learning framework for the regression tasks for hyperspectral data. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models.
• Score: 4.931067393619175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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