Contrastive Learning for Regression on Hyperspectral Data
This work addresses a gap in applying contrastive learning to regression problems in hyperspectral data analysis, which is incremental as it adapts existing techniques to a specific domain.
The paper tackled the lack of contrastive learning methods for regression tasks on hyperspectral data by proposing a framework with specialized transformations, resulting in significant performance improvements over state-of-the-art transformations on synthetic and real datasets.
Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models, achieving better scores than other state-of-the-art transformations.