Prospects for Mitigating Spectral Variability in Tropical Species Classification Using Self-Supervised Learning
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Airborne hyperspectral imaging is a promising method for identifying tropical
species, but spectral variability between acquisitions hinders consistent
results. This paper proposes using Self-Supervised Learning (SSL) to encode
spectral features that are robust to abiotic variability and relevant for
species identification. By employing the state-of-the-art Barlow-Twins approach
on repeated spectral acquisitions, we demonstrate the ability to develop stable
features. For the classification of 40 tropical species, experiments show that
these features can outperform typical reflectance products in terms of
robustness to spectral variability by 10 points of accuracy across dates.
