Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data
摘要
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.
引用
@article{arxiv.2602.10745,
title = {Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data},
author = {Mohamad Dhaini and Paul Honeine and Maxime Berar and Antonin Van Exem},
journal= {arXiv preprint arXiv:2602.10745},
year = {2026}
}