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 | |
| 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 |
Keywords
- land cover map
- Random Forests
- Sentinel-2 images
- Temporal Convolutional Neural Networks
- time series
Cite this
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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
TY - GEN
T1 - Using Sentinel-2 image time series to map the State of Victoria, Australia
AU - Pelletier, Charlotte
AU - Ji, Zehui
AU - Hagolle, Olivier
AU - Morse-McNabb, Elizabeth
AU - Sheffield, Kathryn
AU - Webb, Geoffrey I.
AU - Petitjean, Francois
PY - 2019
Y1 - 2019
N2 - 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.
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.
KW - land cover map
KW - Random Forests
KW - Sentinel-2 images
KW - Temporal Convolutional Neural Networks
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=85074293581&partnerID=8YFLogxK
U2 - 10.1109/Multi-Temp.2019.8866921
DO - 10.1109/Multi-Temp.2019.8866921
M3 - Conference Paper
AN - SCOPUS:85074293581
SN - 9781728146164
SP - 13
EP - 16
BT - 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019)
A2 - Bovolo, Francesca
A2 - Liu, Sicong
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -