Using Sentinel-2 image time series to map the State of Victoria, Australia

Charlotte Pelletier; Zehui Ji; Olivier Hagolle; Elizabeth Morse-McNabb; Kathryn Sheffield; Geoffrey I. Webb; Francois Petitjean

doi:10.1109/Multi-Temp.2019.8866921

Using Sentinel-2 image time series to map the State of Victoria, Australia

Charlotte Pelletier, Zehui Ji, Olivier Hagolle, Elizabeth Morse-McNabb, Kathryn Sheffield, Geoffrey I. Webb, Francois Petitjean

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

Abstract

Sentinel-2 satellites are now acquiring images of the entire Earth every five days from 10 to 60 m spatial resolution. The supervised classification of this new optical image time series allows the operational production of accurate land cover maps over large areas. In this paper, we investigate the use of one year of Sentinel-2 data to map the state of Victoria in Australia. In particular, we produce two land cover maps using the most established and advanced algorithms in time series classification: Random Forest (RF) and Temporal Convolutional Neural Network (TempCNN). To our knowledge, these are the first land cover maps at 10 m spatial resolution for an Australian state.

Original language	English
Title of host publication	2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019)
Editors	Francesca Bovolo, Sicong Liu
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	13-16
Number of pages	4
ISBN (Electronic)	9781728146157
ISBN (Print)	9781728146164
DOIs	https://doi.org/10.1109/Multi-Temp.2019.8866921
Publication status	Published - 2019
Event	International Workshop on the Analysis of Multitemporal Remote Sensing Images 2019 - Shanghai, China Duration: 5 Aug 2019 → 7 Aug 2019 Conference number: 10th https://multitemp2019.tongji.edu.cn/

Conference

Conference	International Workshop on the Analysis of Multitemporal Remote Sensing Images 2019
Abbreviated title	MultiTemp 2019
Country	China
City	Shanghai
Period	5/08/19 → 7/08/19
Internet address	https://multitemp2019.tongji.edu.cn/

Keywords

land cover map
Random Forests
Sentinel-2 images
Temporal Convolutional Neural Networks
time series

Access to Document

10.1109/Multi-Temp.2019.8866921

Link to publication in Scopus

Cite this

Pelletier, C., Ji, Z., Hagolle, O., Morse-McNabb, E., Sheffield, K., Webb, G. I., & Petitjean, F. (2019). Using Sentinel-2 image time series to map the State of Victoria, Australia. In F. Bovolo, & S. Liu (Eds.), 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019) (pp. 13-16). [8866921] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Multi-Temp.2019.8866921

Pelletier, Charlotte ; Ji, Zehui ; Hagolle, Olivier ; Morse-McNabb, Elizabeth ; Sheffield, Kathryn ; Webb, Geoffrey I. ; Petitjean, Francois. / Using Sentinel-2 image time series to map the State of Victoria, Australia. 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019). editor / Francesca Bovolo ; Sicong Liu. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 13-16

@inproceedings{cfdd0831b33a407b8108563d8323f78d,

title = "Using Sentinel-2 image time series to map the State of Victoria, Australia",

abstract = "Sentinel-2 satellites are now acquiring images of the entire Earth every five days from 10 to 60 m spatial resolution. The supervised classification of this new optical image time series allows the operational production of accurate land cover maps over large areas. In this paper, we investigate the use of one year of Sentinel-2 data to map the state of Victoria in Australia. In particular, we produce two land cover maps using the most established and advanced algorithms in time series classification: Random Forest (RF) and Temporal Convolutional Neural Network (TempCNN). To our knowledge, these are the first land cover maps at 10 m spatial resolution for an Australian state.",

keywords = "land cover map, Random Forests, Sentinel-2 images, Temporal Convolutional Neural Networks, time series",

author = "Charlotte Pelletier and Zehui Ji and Olivier Hagolle and Elizabeth Morse-McNabb and Kathryn Sheffield and Webb, {Geoffrey I.} and Francois Petitjean",

year = "2019",

doi = "10.1109/Multi-Temp.2019.8866921",

language = "English",

isbn = "9781728146164",

pages = "13--16",

editor = "Bovolo, {Francesca } and Liu, {Sicong }",

booktitle = "2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Workshop on the Analysis of Multitemporal Remote Sensing Images 2019, MultiTemp 2019 ; Conference date: 05-08-2019 Through 07-08-2019",

url = "https://multitemp2019.tongji.edu.cn/",

}

Pelletier, C, Ji, Z, Hagolle, O, Morse-McNabb, E, Sheffield, K, Webb, GI & Petitjean, F 2019, Using Sentinel-2 image time series to map the State of Victoria, Australia. in F Bovolo & S Liu (eds), 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019)., 8866921, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 13-16, International Workshop on the Analysis of Multitemporal Remote Sensing Images 2019, Shanghai, China, 5/08/19. https://doi.org/10.1109/Multi-Temp.2019.8866921

Using Sentinel-2 image time series to map the State of Victoria, Australia. / Pelletier, Charlotte; Ji, Zehui; Hagolle, Olivier; Morse-McNabb, Elizabeth; Sheffield, Kathryn; Webb, Geoffrey I.; Petitjean, Francois.

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019). ed. / Francesca Bovolo; Sicong Liu. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 13-16 8866921.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

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AU - Webb, Geoffrey I.

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AB - Sentinel-2 satellites are now acquiring images of the entire Earth every five days from 10 to 60 m spatial resolution. The supervised classification of this new optical image time series allows the operational production of accurate land cover maps over large areas. In this paper, we investigate the use of one year of Sentinel-2 data to map the state of Victoria in Australia. In particular, we produce two land cover maps using the most established and advanced algorithms in time series classification: Random Forest (RF) and Temporal Convolutional Neural Network (TempCNN). To our knowledge, these are the first land cover maps at 10 m spatial resolution for an Australian state.

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Pelletier C, Ji Z, Hagolle O, Morse-McNabb E, Sheffield K, Webb GI et al. Using Sentinel-2 image time series to map the State of Victoria, Australia. In Bovolo F, Liu S, editors, 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 13-16. 8866921 https://doi.org/10.1109/Multi-Temp.2019.8866921