Artificial intelligence is at the heart of technological, scientific, economic, societal and environmental transformations. In this context, data modeling for description, decision, prediction and/or forecasting purposes becomes an essential issue. Our workgroup is dedicated to this issue and brings together methodological works dealing with machine learning (deep learning, data mining), the fusion of uncertain data (probabilities, possibilities, belief functions, fuzzy sets, intervals) and signal processing (wavelets, statistical learning, differential geometry). These works are mainly applied to/triggered by the analysis of remote sensing data with a temporal dimension; most often for environmental monitoring purposes (crustal deformation, erosion, deforestation, glacial retreat, marine pollution). Remote sensing work is also being carried out to produce such data, with the main objectives of measuring displacements, detecting changes and inverting models. Our scientific perspectives are multiple and concern 1) the consideration of the volume, uncertainty and complexity (spatial, temporal, physical properties) of data, 2) the fusion of data and/or models, and 3) the interpretability of the obtained results.
Tutorial on the mining of displacement fields and their confidence measures using DTFS-P2miner, in collaboration with the DM2L and Imagine teams of UMR LIRIS. Autumn school of the MDIS 2019 national conference, Strasbourg, France, October 15, 2019.
Statistical Learning for Signal Processing workshop, Annecy, France, July 15-16, 2019.
SAR & Cryosphere workshop, Annecy, France, June 11, 2019.
Gradual Interval Arithmetic and Fuzzy Interval Arithmetic. Reda Boukezzoula, Laurent Foulloy, Didier Coquin, Sylvie Galichet. Granular Computing, 2019. doi : 10.1007/s41066-019-00208-z
A Data-Adaptive EOF-Based Method for Displacement Signal Retrieval From InSAR Displacement Measurement Time Series for Decorrelating Targets. Rémi Prébet, Yajing Yan, Matthias Jauvin, Emmanuel Trouvé. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 5829-5852, 2019.
On Elliptical Possibility Distributions. C. Lesniewska-Choquet, G. Mauris, A. Atto, G. Mercier. IEEE Transactions on Fuzzy Systems, Institute of Electrical and Electronics Engineers, June 2019. doi : 10.1109/TFUZZ.2019.2920803.
Random Matrix Improved Covariance Estimation for a Large Class of Metrics. M. Tiomoko, F. Bouchard, G. Ginolhac, R. Couillet. International Conference on Machine Learning (ICML), Long Beach, USA, June 2019.
Ranking Evolution Maps for Satellite Image Time Series Exploration – Application to Crustal Deformation and Environmental Monitoring. N. Méger, C. Rigotti, C. Pothier, T. Nguyen, F. Lodge, L. Gueguen, R. Andréoli, M-P. Doin and M. Datcu. Data Mining and Knowledge Discovery, volume 33, issue 1, pp. 131-167, January 2019. doi: 10.1007/s10618-018-0591-9.
New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images. A. Mian, G. Ginolhac, J.P. Ovarlez, A. Atto. IEEE Transactions on Signal Processing, vol. 67(2), pp 520-534, January 2019.