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.

翻译：对比学习在表征学习领域已展现出巨大成功，尤其在图像分类任务中。然而，针对回归任务的研究仍显不足，特别是高光谱数据上的应用。本文提出一种面向高光谱数据回归任务的光谱-空间对比学习框架，该框架采用模型无关设计，可增强如三维卷积网络和基于Transformer的网络等骨干模型。此外，我们提供了一系列适用于高光谱数据增强的变换方法。在合成数据集和真实数据集上的实验表明，所提出的框架与变换方法显著提升了所有被研究骨干模型的性能。