Classification of MODIS Time Series with Dense Bag-of-Temporal-SIFT-Words: Application to Cropland Mapping in the Brazilian Amazon

Adeline Bailly 1, 2 Damien Arvor 1 Laetitia Chapel 2 Romain Tavenard 1
1 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso. We used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles.
Keywords : Time Series Classification MODIS Vegetation Index D-BoTSW
Type de document :
Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Beijing, China. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 〈10.1109/IGARSS.2016.7729594〉
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Contributeur : Romain Tavenard <>
Soumis le : jeudi 7 juillet 2016 - 17:55:42
Dernière modification le : mardi 5 juin 2018 - 10:14:24

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UNIV-BREST | IRISA_UBS | UNIV-NANTES | INRIA | LETG-COSTEL | LETG | IRISA | UBS | CENTRALESUPELEC | UR1-UFR-ISTIC | PSL | UNIV-RENNES2 | UR2-HB | COMUE-NORMANDIE | UNIV-RENNES

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Adeline Bailly, Damien Arvor, Laetitia Chapel, Romain Tavenard. Classification of MODIS Time Series with Dense Bag-of-Temporal-SIFT-Words: Application to Cropland Mapping in the Brazilian Amazon. IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Beijing, China. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 〈10.1109/IGARSS.2016.7729594〉. 〈halshs-01343211〉

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