Issue and perspectives
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.
Talk on pattern discovery in satellite image time series and displacement field time series, MIAI Grenoble Alpes meeting, May 14, 2020.
PhD defense: M. Jauvin, Measurement of surface deformations by satellite radar interferometry – Application to the monitoring of mountain territories and the impact of large construction sites, December 18, 2019.
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.
A new transformation of continuous unimodal asymmetric probability distributions into possibility distributions. Laurent Foulloy, Gilles Mauris. Fuzzy Sets and Systems. 2021. doi: 10.1016/j.fss.2020.12.025.
Thick Fuzzy Sets (TFSs) and Their Potential Use in Uncertain Fuzzy Computations and Modeling. Reda Boukezzoula, Luc Jaulin, Benoît Desrochers, Didier Coquin, IEEE Transactions on Fuzzy Systems, 2021. doi: 10.1109/tfuzz.2020.3018550.
Frames Learned by Prime Convolution Layers in a Deep Learning Framework. Abdourrahmane Mahamane Atto, Rosie Bisset, Emmanuel Trouvé. IEEE Transactions on Neural Networks and Learning Systems, 2020. doi: 10.1109/TNNLS2020.3009059.
Assistance via IoT Networking cameras and Evidence Theory for 3D Object Instance Recognition: Application for the NAO Humanoid Robot. Didier Coquin, Reda Boukezzoula, Alexandre Benoit, Thanh Long Nguyen. Internet of Things, 2020. doi: 10.1016/j.iot.2019.100128.
Construction de distributions de possibilité bivariées à partir de distributions marginales connues. Charles. Lesniewska-Choquet, Gilles Mauris, Abdourrahmane Mahamane Atto. LFA 2020.
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.
iXblue project: use of robust statistical methods for object detection and classification in sonar data. iXblue/LISTIC PhD subject. 2020-2024.
APR CNES SHARE: chronological series of sentinel-1 SAR images in mountainous terrains. Change detection over snow-covered surfaces (dry snow/moist snow) using automatic learning methods. In collaboration with UMR ISTerre, UMR LJK, CNRM, Magellium. 2021-2023.
SMGA project: use of AI techniques to detect cavities on mountain slopes. In collaboration with Géolithe. 2021.
RINA project: creation of a demonstrator using AI methods for an operational management of geological Natural Hazards. In collaboration with CEREMA, BRGM and Géolithe. 2021.
Heliocity project: classification of solar installation monitoring data using machine learning methods. 2021.
ANR MARGARITA: Modern Adaptive Radar: Great Advances in Robust and Inference Techniques and Application. 2019-2021.
TOTAL project: oil slick detection, large ocean surface SAR data volumes, heterogeneous data fusion, deep learning. 2018-2021.
ANR ReVeRIES: recreational, interactive and educational plant recognition on smartphones. 2016-2021.
Post-doctoral fellows: H. Courteille.
LDI I-TURN project: real-time monitoring and control of a bar-turning machine using fuzzy systems (fuzzy rules, rule aggregation). 2020.
SmarterPlan/Linksium project: detection and inventory of business objects by deep learning in 360° images of commercial building interiors. Funded by SATT Linksium. 2020.
APR CNES START Deep: monitoring of vegetated territories using remote sensing and deep learning techniques. In collaboration with UMR TETIS and UMR IMS. 2020.
Géolithe project: AI methods for processing data produced by airborne geological radars. 2020.
PNTS CNES: missing data restoration in displacement time series obtained from SAR images by statistical learning. 2019-2020.
GammaLearn: characterization of gamma radiationS by deep learning approaches applied to Cherenkov images provided by a single telescope. In cooperation with UMR LAPP, jointly funded by the European project ASTERICS and the Savoie Mont Blanc Foundation. 2017-2020.
ANR PHOENIX: parsimony, Huge Observations of Earth Non-stationarities from Images Time Series. 2015-2019.
ANR VIP-Mont Blanc: understanding and predicting environmental changes: a research project on the morphological evolution of the Mont Blanc massif. 2014-2018.
FUI G4M: multi-profession and multi-material geodetection. 2014-2017.
FUI MISAC: multi-functional Intelligent Surface for Automative and Aeronautics Cockpits. 2012-2015.
European project INTERREG GLARISKALP : risque glaciaire. 2011-2013.
ANR FOSTER: spatiotemporal data mining: application to the understanding and monitoring of erosion. 2011-2013.
ANR REVES: plant recognition for smartphone interfaces. 2010-2013.
ANR EFIDIR: information extraction and fusion for the measurement of displacements using radar imaging. 2008-2012.
ADIXEN project: event forecasting in datastreams for predictive maintenance. 2007-2010.
ACI MEGATOR: measurement of the evolution of alpine glaciers by optical and radar remote sensing. 2004-2007.