提出光谱-空间对比学习框架，提升高光谱回归性能
Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data
- 设计光谱-空间对比学习框架，适配3D卷积与Transformer模型
- 在合成与真实数据集上显著提升各类主干模型的回归精度
- 提供专用于高光谱数据的增强方法，适用于遥感建模场景
对比学习在表征学习中表现出色，尤其在图像分类任务中。然而，针对回归任务，特别是高光谱数据的应用研究仍显不足。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，采用模型无关设计，可有效增强3D卷积和基于Transformer的网络。同时，我们构建了一套适用于高光谱数据的变换策略。在合成与真实数据集上的实验表明，该框架及所提变换显著提升了所有测试主干模型的性能。
原文摘要 · Abstract (English)
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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