  Airborne hyperspectral images can be used to map the land cover in large
urban areas, thanks to their very high spatial and spectral resolutions on a
wide spectral domain. While the spectral dimension of hyperspectral images is
highly informative of the chemical composition of the land surface, the use of
state-of-the-art machine learning algorithms to map the land cover has been
dramatically limited by the availability of training data. To cope with the
scarcity of annotations, semi-supervised and self-supervised techniques have
lately raised a lot of interest in the community. Yet, the publicly available
hyperspectral data sets commonly used to benchmark machine learning models are
not totally suited to evaluate their generalization performances due to one or
several of the following properties: a limited geographical coverage (which
does not reflect the spectral diversity in metropolitan areas), a small number
of land cover classes and a lack of appropriate standard train / test splits
for semi-supervised and self-supervised learning. Therefore, we release in this
paper the Toulouse Hyperspectral Data Set that stands out from other data sets
in the above-mentioned respects in order to meet key issues in spectral
representation learning and classification over large-scale hyperspectral
images with very few labeled pixels. Besides, we discuss and experiment
self-supervised techniques for spectral representation learning, including the
Masked Autoencoder, and establish a baseline for pixel-wise classification
achieving 85% overall accuracy and 77% F1 score. The Toulouse Hyperspectral
Data Set and our code are publicly available at
https://www.toulouse-hyperspectral-data-set.com and
https://www.github.com/Romain3Ch216/tlse-experiments, respectively.
