Matthieu Gallet

Selected publications

2024

1. 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

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   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}
   }

2. IGARSS

   Supervised Classification for Analysis of Cryospheric Zones Using SAR Statistical Timeseries

   Lin-Kwong-Chon, Christophe,  Gallet, Matthieu,  Kaushik, Suvrat and 1 more author

   In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium 2024

   Abs

   This study explores machine learning for classifying X-band Synthetic Aperture Radar (SAR) monovariate time series from four cryospheric zones in the Mont-Blanc massif. We aim to classify ablation zones, accumulation zones, hanging glaciers, and ice aprons using log-cumulants and Dynamic Time Warping Barycentric Averaging. Our approach evaluates distances between time series and estimated reference centroids, employing HH and HV polarimetric channels. We propose an extension to this method by aggregating class membership probabilities from selected polarimetric combinations. Results are compared across polarimetric channels, revealing insights into classification performance.

3. 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

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   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

1. 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

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   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},
   }