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.
Abstract (translated)
对比学习在表示学习方面取得了巨大成功，特别是在图像分类任务上。然而，在回归任务的研究中仍然存在不足，尤其是针对高光谱数据的应用。本文提出了一种适用于高光谱数据回归任务的光谱-空间对比学习框架，并采用一种模型无关的设计方式，可以增强如3D卷积和基于变换器的网络等骨干网络。此外，我们还提供了一系列用于增强高光谱数据的相关转换方法。在合成数据集和真实数据集上的实验表明，所提出的框架和转换显著提升了所有研究骨干模型的表现性能。
URL
https://arxiv.org/abs/2602.10745
