This repository contains the code to reproduce the experiments of the paper:
Please cite this paper if you use the code in this repository as part of a published research project:
@misc{thoreau2023toulouse, title={Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques}, author={Romain Thoreau and Laurent Risser and Véronique Achard and Béatrice Berthelot and Xavier Briottet}, year={2023}, eprint={2311.08863}, archivePrefix={arXiv}, primaryClass={cs.CV} }
The Toulouse Hyperspectral Data Set is the combination of 1) an airborne hyperspectral image acquired by the AisaFENIX sensor over Toulouse, France, during the CAMCATT-AI4GEO campaign and of 2) a land cover ground truth, provided with standard train / test splits for the validation of machine learning models.
The code was tested with python 3.8:
- create a python virtual environment
- clone this repo:
git clone https://github.com/Romain3Ch216/tlse-experiments.git - navigate to the repository:
cd tlse-experiments - install python requirements:
pip install -r requirements.txt
The main.py script allows to train a standard Autoencoder or a Masked Autoencoder (and to perform a random search of the best hyperparameters). To validate the potential of the learned spectral representations, the k_neighbours.py and random_forest.py scripts allow to train a KNN and a RF on top of the frozen features, respectively.
Pre-trained models and training logs are available in the checkpoints folder.
Please send any feedback to romain.thoreau@cnes.fr