Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data

• URL: http://arxiv.org/abs/2602.10745v1
• Date: Wed, 11 Feb 2026 11:16:57 GMT
• Title: Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data
• Authors: Mohamad Dhaini, Paul Honeine, Maxime Berar, Antonin Van Exem,
• Abstract summary: We propose a spectral-spatial contrastive learning framework for regression tasks for hyperspectral data.<n>We provide a collection of transformations relevant for augmenting hyperspectral data.
• Score: 6.420595567019138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive learning has demonstrated great success in representation learning, especially for image classification tasks. However, there is still a shortage in studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a spectral-spatial contrastive learning framework for regression tasks for hyperspectral data, in a model-agnostic design allowing to enhance backbones such as 3D convolutional and transformer-based networks. Moreover, we provide a collection of transformations relevant for augmenting hyperspectral data. Experiments on synthetic and real datasets show that the proposed framework and transformations significantly improve the performance of all studied backbone models.

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