Research
International Peer-Review Journals
2023
[J14] A. Shifaz, C. Pelletier, F. Petitjean, & G. I. Webb. (2023). Elastic Similarity Measures for Multivariate Time Series Classification. Knowledge and Information Systems, 65, 2665-2698. doi:10.1007/s10115-023-01835-4
2022
[J13] J. Nyborg, C. Pelletier, S. Lefèvre, & I. Assent. (2022). TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 188, 301-313. doi:10.1016/j.isprsjprs.2022.04.018
2021
[J12] S. Ofori-Ampofo, C. Pelletier, & S. Lang (2021). Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning. Remote Sensing, 13(22), 4668. doi:10.3390/rs13224668
[J11] N. Mboga, S. D'Aronco, T. Grippa, C. Pelletier, S. Georganos, S. Vanhuysse, E. Wolf, B. Smets, O. Dewitte, M. Lennert, & J. D. Wegner. (2021) Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS International Journal of Geo-Information, 10(8), 523. doi:10.3390/ijgi10080523
[J10] B. Lucas, C. Pelletier, D. F. Schmidt, G. I. Webb, & F. Petitjean. (2021). A Bayesian inspired, deep learning, semi supervised domain adaptation technique for land cover mapping. Machine Learning, 1-33. doi:10.1007/s10994-020-05942-z
2020
[J09] H. Ismail Fawaz, B. Lucas, G. Forestier, C. Pelletier, D. F. Schmidt, J. Weber, G. I. Webb, L. Idoumghar, P-A. Muller, & F. Petitjean. (2020). InceptionTime: Finding AlexNet for Time Series Classification. Data Mining and Knowledge Discovery, 34, 1936-1962. doi:10.1007/s10618-020-00710-y
[J08] A. Shifaz, C. Pelletier, F. Petitjean, & G. I. Webb. (2020). TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification, Data Mining and Knowledge Discovery, Springer, 34, 742-775. doi:10.1007/s10618-020-00679-8
2019
[J07] S. Bousbih, M. Zribi, C. Pelletier, A. Gorrab, Z. Lili Chabaane, N. Baghdadi, N. Ben Aissa, & B. Mougenot. (2019) Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sensing, 11(13), 1520, doi:10.3390/rs11131520
[J06] R. Giblin, J. Kennedy, C. Pelletier, J. Thomas, K. Weatherall, & F. Petitjean. (2019) What can 100,000 books tell us about the international public library e-lending landscape? Information Research, 24, 3 (project website)
[J05] C. Pelletier, G. I. Webb, & F. Petitjean. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series, Remote Sensing, 11(5), 523. doi:10.3390/rs11050523
[J04] B. Lucas, A. Shifaz, C. Pelletier, L. O'Neill, N. Zaidi, B. Goethals, F. Petitjean, & G. Webb. (2019). Proximity Forest: An effective and scalable distance-based classifier for time series. Data Mining and Knowledge Discovery, 33(3), 607-635. arxiv.org/abs/1808.10594
2017
[J03] S. Ferrant, A. Selles, M. Le Page, P-A. Herrault, C. Pelletier, A. Al Bitar, S. Mermoz, S. Gascoin, A. Bouvet, M. Saqalli, B. Dewandel, Y. Caballero, S. Ahmed, J-C Maréchal, & Y. Kerr. (2017). Detection of irrigated crops from Sentinel-1 and Sentinel-2 to estimate seasonal groundwater use in South India. Remote Sensing MDPI, 9 (11), 1119. doi:10.3390/rs9111119
[J02] C. Pelletier, S. Valero, J. Inglada, N. Champion, C. Marais Sicre, & G. Dedieu. (2017). Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sensing, 9(2), 173. doi:10.3390/rs9020173
2016
[J01] C. Pelletier, S. Valero, J. Inglada, N. Champion, & G. Dedieu. (2016). Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sensing of Environment, 187, 156-168, doi:10.1016/j.rse.2016.10.010
Book Chapter
[B01] C. Pelletier, & S. Valero. (2021). Pixel‐based Classification Techniques for Satellite Image Time Series. Change Detection and Image Time Series Analysis 2: Supervised Methods, 33-84.
Conference Papers
2023
[C21] F. Painblanc, L. Chapel, N. Courty, C. Friguet, C. Pelletier, & R. Tavenard. (2023). Match-And-Deform: Time Series Domain Adaptation Through Optimal Transport and Temporal Alignment. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 341-356). [paper] (+ presentation at CaP/RFIAP 2022)
[C20] C. Dufourg, C. Pelletier, S. May, & S. Lefèvre. (2023). Graph Dynamic Earth Net: Spatio-Temporal Graph Benchmark for Satellite Image Time Series. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 7164-7167). [paper, presented as a poster]
[C19] A. D. Adebayo, C. Pelletier, S. Lang, & S. Valero. (2023). Detecting Land Cover Changes between Satellite Image Time Series by Exploiting Self-Supervised Representation Learning Capabilities. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 7168-7171). [paper, presented as a poster]
[C18] C. Dufourg, C. Pelletier, S. May, & S. Lefèvre (2023). Analyse de techniques d'apprentissage sur graphes pour la segmentation sémantique de séries temporelles d'images satellitaires. In ORASIS 2023.
2022
[C17] J. Nyborg, C. Pelletier, I. Assent. (2022) Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1392-1402). [paper]
2020
[C16] R. Fischer, N. Piatkowski, C. Pelletier, G. I. Webb, F. Petitjean, & K. Morik. (2020) No cloud on the horizon: probabilistic gapfilling in satellite image series. In The 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA) - Environmental and Geo-spatial Data Analytics (EnGeoData) [presentation]
[C15] B. Lucas, C. Pelletier, D. Schmidt, G. I. Webb, & F. Petitjean. (2020) Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1074-1077). [paper]
[C14] M. Rußwurm, C. Pelletier, M. Zollner, S. Lefèvre, & M. Körner. (2020). BreizhCrops: A Time Series Dataset for Crop Type Mapping. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 43. [paper]
[C13] S. Bousbih, M. Zribi, Z. Lili-Chabaane, B. Mougenot, C. Pelletier, M. El Hajj, & N. Baghdadi. (2020) Sentinel-1 and Sentinel-2 data for the characterisation of the states of continental surface over a semi-arid region in Tunisia. In 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) (pp. 285-288).
2019
[C12] C. Pelletier, Z. Ji, O. Hagolle, E. Morse-McNabb, K. Sheffield, G. I. Webb, & F. Petitjean. (2019) Using Sentinel-2 Image Time Series to map the State of Victoria, Australia. In 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) (pp. 1-4).
[C11] B. Lucas, C. Pelletier, J. Inglada, D. Schmidt, G. I. Webb, & F. Petitjean. (2019). Exploring data quantity requirements for domain adaptation in the classification of Satellite Image Time Series, In 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) (pp. 1-4).
[C10] C. Pelletier, G. I. Webb, & F. Petitjean. (2019). Deep learning for Sentinel-2 image time series. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 461-464).
[C09] J. Grimaldi, F. Helen, C. Pelletier, V. Bustillo, & T. Houet. (2019) Mapping the height of heterogeneous vegetation from UAV-borne visible images and DSM. In 4th World Congress on Agroforestry (pp. 933-p). [Awarded best poster.]
2017
[C08] C. Pelletier, S. Valero, J. Inglada, N. Champion, & G. Dedieu. (2017). New iterative learning strategy to improve classification systems by using outlier detection. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3676-3679).
[C07] C. Pelletier, S. Valero, J. Inglada, N. Champion, & G. Dedieu. (2017). Filtering mislabeled data for improving time series classification. In 2017 9th international workshop on the analysis of multitemporal remote sensing images (MultiTemp) (pp. 1-4).
[C06] J. Grimaldi, W. Trambouze, T. Dufourcq, M. Vergne, R. Fieuzal, C. Pelletier, T. Houet, & V. Bustillo. (2017). Can intercropped trees mitigate heat and drought effects on grapevines? A study of microclimate patterns in agroforestry vineyards, Southern France. In Proceedings of the IUFRO Landscape Ecology Conference (pp. 24-29).
[C05] J. Inglada, B. Tardy, D. Derksen, C. Pelletier, A. Vincent, S. Valero, J. Michel, & V. Thierion. (2017) Operational land cover map production using Sentinel image time series supervised classification with out-of-date reference data. WorldCover 2017, Esrin [poster]
2016
[C04] C. Pelletier, S. Valero, J. Inglada, N. Champion, & G. Dedieu. (2016). An assessment of image features and Random Forest for land cover mapping over large areas using high resolution satellite image time series. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3338-3341). [paper]
[C03] S. Valero, C. Pelletier, & M. Bertolino. (2016). Patch-based reconstruction of high resolution satellite image time series with missing values using spatial, spectral and temporal similarities. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2308-2311). [paper, presented as a poster]
[C02] J. Grimaldi, R. Fieuzal, C. Pelletier, V. Bustillo, T. Houet, & D. Sheeren. (2016) Microclimate patterns in an agroforestry intercropped vineyard: first results. In 3rd European Agroforestry Conference (EURAF). [poster]
2015
[C01] S. May, & C. Pelletier. (2015) Primal sketch of image series with edge preserving filtering. Application to change detection. In 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp) (pp. 1-4). [paper]
X. XXX: co-supervised Ph.D. or MSc students
Talks / Workshops / Seminars
November 2023. Domain Adaptation for Satellite Image Time Series. Machine Vision for Earth Observation and Environmental Monitoring Workshop (@BMVC). (Keynote). [slides]
April 2023. Crop-type mapping from optical and radar time series using attention-based deep learning. Brazilian Symposium on Remote Sensing. (Invited speaker). [slides]
March 2023. Unsupervised domain adaptation for satellite image time series. Bristol doctoral school. (Invited speaker)
December 2022. Automatic mapping of the land surfaces from Earth observation time series. 7èmes Rencontres de Statistique - Environnement et climat (Conférencière invitée)
November 2022. Unsupervised cross-region adaptation by temporal shift estimation. Mulhouse seminar. (Invited speaker) [slides]
November 2022. How to automatically produce crop-type maps from high-resolution satellite images time series? (Invited speaker). TREES Research Lab - National Institute for Space Research (INPE). [slides]
May 2022. Unsupervised domain adaptation for satellite image time series (Invited speaker). Data Science@CESBIO [slides on the website]
November 2021. Deep Learning for the Analysis of Remote Sensing Time Series (Invited speaker.) LASTIG seminar: deep learning for environment monitoring. En ligne.
October 2021. Time Series Classification: recent advances and challenges. (Invited speaker). Séminaire recherche Unité MIAT - INRAE. [slides]
July 2021. Deep Learning for Time Series Classification: Application to Earth Observation time series. (Invited speaker). ISPRS Geospatial Lecture Day.
December 2020. Deep Learning for Time Series Classification: application to Earth Observation time series. (Invited speaker.) Séminaire recherche ENS - Département Informatique. En ligne. [slides]
October, 2020. Deep Learning for Time Series Classification: application to Earth Observation time series. (Invited speaker.) GdR-ISIS & COMET-TSI-CNES. Extraction d'attributs et apprentissage pour l'analyse des images de télédétection. https://www.gdr-isis.fr/index.php/reunion/425/
July, 2020. Panel discussion: machine learning, time series and Earth Observation--opportunities and challenges. (Invited pannel member). Symposium GdR MaDICS (action MACLEAN), online.
December, 2019. Présentation parcours (Talk, invited speaker). Journée Jeunes Chercheurs MACLEAN. Paris, France [slides]
November, 2017. Supervised classification of satellite image time series from noisy labeled data (Talk, invited speaker). LabelNoise 2017. Nancy, France
November, 2016. A study of class label noise effects on supervised learning algorithms for land cover mapping, GDR ISIS Filtrage de contenus sensibles et sécurité des méthodes d'apprentissage
November, 2016. Performances d'algorithmes de classification supervisée en présence de données mal-étiquetées dans l'ensemble d'apprentissage, CES OSO (Centre d'Expertise Occupation des Sols)
October, 2016. Cartographie de l'occupation des sols sur de grandes étendues par imagerie satellitaire optique, Journée CNES Jeunes Chercheurs