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Personal profile

Biography

Charlotte Pelletier joined the machine learning team at the Faculty of Information Technology at Monash University in Melbourne, Australia in 2018. Her postdoctoral, supervised by Pr. Geoff Webb and Dr. François Petitjean, mainly focuses on the scalability of time series classification algorithms. 

In December 2017, she completed her Ph.D. with the French Space Agency (CNES) and the French Mapping Agency (IGN), at CESBIO laboratory in Toulouse (France). She worked under the supervision of Pr. Gérard Dedieu (CNES) and Dr. Silvia Valero (Associate Professor at the University of Toulouse). Her Ph.D work aimed at improving the classification of new high resolution satellite image time series, such as the Sentinel-2 one. In particular, she analyzed the classifier robustness to the presence of mislabeled training data. She also explored the use of mislabeled data detection techniques, and she proposed new filtering approaches to deal with mislabeled data in the classification framework. 

In 2014, she received her electrical engineering degree from ENSEIRB-MATMECA/INP in Bordeaux (France) and her master degree from Université Bordeaux (France). 

Keywords

  • Machine Learning
  • Image Processing
  • Outlier Detection
  • Time Series Classification
  • Data Analysis
  • Remote Sensing
  • Satellite Images
  • Random Forests (RF)
  • Support Vector Machines (SVM)

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Research Output 2015 2019

  • 5 Conference Paper
  • 5 Article

Proximity Forest: an effective and scalable distance-based classifier for time series

Lucas, B., Shifaz, A., Pelletier, C., O’Neill, L., Zaidi, N., Goethals, B., Petitjean, F. & Webb, G. I., May 2019, In : Data Mining and Knowledge Discovery. 33, 3, p. 607-635 29 p.

Research output: Contribution to journalArticleResearchpeer-review

Temporal Convolutional Neural Network for the classification of satellite image time series

Pelletier, C., Webb, G. I. & Petitjean, F., 4 Mar 2019, In : Remote Sensing. 11, 5, 25 p., 523.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in South India

Ferrant, S., Selles, A., Le Page, M., Herrault, P. A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., Saqalli, M., Dewandel, B., Caballero, Y., Ahmed, S., Maréchal, J. C. & Kerr, Y. H., 3 Nov 2017, In : Remote Sensing. 9, 11, 21 p., 1119.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Effect of training class label noise on classification performances for land cover mapping with satellite image time series

Pelletier, C., Valero, S., Inglada, J., Champion, N., Sicre, C. M. & Dedieu, G., 18 Feb 2017, In : Remote Sensing. 9, 2, 24 p., 173.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Filtering mislabeled data for improving time series classification

Pelletier, C., Valero, S., Inglada, J., Dedieu, G. & Champion, N., 2017, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2017): Brugge, Belgium 27-29 June 2017. Swinnen, E. & De Lannoy, G. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 4 p. 8035217

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch