Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations.
@article{arxiv.2509.05630,
title = {Self-supervised Learning for Hyperspectral Images of Trees},
author = {Moqsadur Rahman and Saurav Kumar and Santosh S. Palmate and M. Shahriar Hossain},
journal= {arXiv preprint arXiv:2509.05630},
year = {2025}
}