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

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.
Type de document :
Pré-publication, Document de travail
2016
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https://hal.archives-ouvertes.fr/hal-01254455
Contributeur : Adeline Bailly <>
Soumis le : mercredi 13 janvier 2016 - 10:31:32
Dernière modification le : vendredi 24 février 2017 - 01:11:26
Document(s) archivé(s) le : vendredi 11 novembre 2016 - 00:00:11

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  • HAL Id : hal-01254455, version 1

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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. 2016. <hal-01254455>

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