Assessing the Adaptability of Self-Supervised Learning Methods for Small-Scale Hyperspectral Imaging

This paper investigates the adaptability of computer vision self-supervised learning (SSL) methods, traditionally designed for RGB data, to small hyperspectral datasets. Focusing on different SSL approaches (e.g. contrastive, redundancy reduction, autoencoders,…), we conduct experiments using the Pavia and the Salinas hyperspectral datasets, employing cross-validation to ensure robustness. Our study aims to determine whether these methods can be directly applied to hyperspectral data or if significant modifications are necessary, given the unique properties of hyperspectral imaging. This exploratory research serves as a step in understanding the transferability of SSL techniques from standard computer vision to the hyperspectral domain.