Matthieu Gallet

Since October 2021, I am a PhD student at the LISTIC laboratory of the University of Savoie Mont Blanc under the supervision of Abdourrahmane Atto, Fatima Karbou and Emmanuel Trouvé.

My research interests are in the field of SAR image processing and analysis. I am particularly interested in the development of new methods for the classification of SAR images. I study the statistical properties of SAR images and their use in various machine learning frameworks.

I am interested in their application to the study of the cryosphere, in particular the study of the wet snow cover, in both space and time at a large scale.

I previously graduated from the Institut Physique du Globe de Paris in 2021 with a Master’s degree in Remote Sensing. My master’s thesis was about robust inversion of Ground Penetrating Radar (GPR) data and classification of GPR images.

Selected publications

All publications are available via the Publications tab.

2024

JSTARS
Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Gallet, Matthieu, Atto, Abdourrahmane, Karbou, Fatima and 1 more author

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024

Abs Bib HTML PDF Code

The detection of wet snow by satellite imaging is currently done in an unsupervised way and lacks quantitative evaluation due to the difficulty of collecting ground truths in extreme environments. In this article, we propose to take into account information associated with a physical model to label satellite data for the purpose of supervised learning of snow properties using synthetic aperture radar (SAR) imagery. This dataset is constructed from Sentinel-1 SAR images, augmented with topographic information obtained from a digital elevation model. The labeling of this data is done at the scale of the Northern Alps using the CROCUS physical snow model. Then, we trained, over 13 combinations of labeled dataset, a wide range of machine learning models to quantitatively identify the most relevant learners for the wet snow detection task. The results demonstrate consistency among the different algorithms, with significant improvement observed when incorporating polarimetric combinations and topographic orientation data in the input of the model. The best algorithmic solution trained on this dataset is evaluated by comparing the obtained wet snow map over a validation area in the French massif of the Grandes Rousses with the existing Copernicus products, fractional snow cover, and SAR wet snow. We also compare the temporal results obtained at one meteorological station located in the test area. The results show a better representation of wet snow during the melting period using the supervised learning approach, as well as a reduction in areas classified as wet during the winter season.
@article{10360230, title = {Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling}, author = {Gallet, Matthieu and Atto, Abdourrahmane and Karbou, Fatima and Trouvé, Emmanuel}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, year = {2024}, doi = {10.1109/JSTARS.2023.3342990}, keywords = {Snow,Labeling,Satellites,Radar polarimetry,Machine learning,Liquids,Backscatter}, url = {}, volume = {17}, number = {}, pages = {2901-2917}, month = {}, issn = {2151-1535} }
ICASSP
Renyi Divergences Learning for explainable classification of SAR Image Pairs

Gallet, Matthieu, Mian, Ammar, and Atto, Abdourrahmane

In International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024

Abs Bib HTML

We consider the problem of classifying a pair of Synthetic Aperture Radar (SAR) images by proposing an explainable and frugal algorithm that integrates a set of divergences. The approach relies on a statistical framework that takes standard probability distributions into account for modelling SAR data. Then, by learning a combination of parameterized Renyi divergences and their parameters from the data, we are able to classify the pair of images with fewer parameters than regular machine learning approaches while also allowing an interpretation of the results related to the priors used. Experiments on real multi-class data demonstrate the virtues of the suggested method when compared to both Random Forest and Convolutional Neural Networks (CNN) classifiers, showing its resilience to disturbances such as polluted labels and variations in the percentage of training data.
@inproceedings{10448227, title = {Renyi Divergences Learning for explainable classification of SAR Image Pairs}, author = {Gallet, Matthieu and Mian, Ammar and Atto, Abdourrahmane}, booktitle = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2024}, pages = {7445-7449}, keywords = {Training, Training data, Signal processing algorithms, Radar polarimetry,Probability distribution, Classification algorithms, Convolutional neural networks, SAR, Rényi divergence, explainable machine learning, classification}, doi = {10.1109/ICASSP48485.2024.10448227}, }

2023

TGRS
New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

Gallet, Matthieu, Mian, Ammar, Ginolhac, Guillaume and 2 more authors

IEEE Transactions on Geoscience and Remote Sensing 2023

Abs Bib HTML PDF Code

In this paper, we propose two algorithms to enhance the interpretability of the hyperbola in B-scans obtained with a Ground Penetrating Radar (GPR). These hyperbolas are the responses of buried objects or cavities. To correctly detect and classify them, a denoising is typically necessary for GPR images as the signal-to-noise ratio is low, and the various interfaces naturally present in the earth have a strong response. Both algorithms are based on a sparse convolutional coding model plus a low rank component. It is solved through an Alternating Direction Method of Multipliers (ADMM) framework. In order to take into account the presence of outliers and the artifacts caused by the acquisition, the second algorithm is based on the Huber norm instead of the classic L2 -norm. These algorithms are tested on a real dataset labeled by geophysicists. The results show the denoising efficiency of this approach, and in particular the robustness of the second algorithm.
@article{gallet:hal-04075772, title = {New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images}, author = {Gallet, Matthieu and Mian, Ammar and Ginolhac, Guillaume and Ollila, Esa and Stelzenmuller, Nickolas}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, year = {2023}, doi = {10.1109/TGRS.2023.3268477}, keywords = {Ground Penetrating Radar ; Sparse Inversion ; Convolutive Model ; Robust methods ; Dictionaries ; Radar ; Signal to noise ratio ; Radar imaging ; Signal processing algorithms ; Shape ; Radar antennas}, url = {https://hal.science/hal-04075772}, }