Projects per year
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 |
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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 |
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Abbreviated title | MultiTemp 2019 |
Country/Territory | 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
Projects
- 1 Finished
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Time series classification for new-generation Earth observation satellites
Petitjean, F.
1/06/17 → 31/12/20
Project: Research