Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon

Adeline Bailly; Damien Arvor; Laetitia Chapel; Romain Tavenard

doi:10.1109/IGARSS.2016.7729594

Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon

Adeline Bailly
, Damien Arvor
, Laetitia Chapel
, Romain Tavenard

Université de Rennes 2
IRDL

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2300-2303
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7729594
Publication status	Published - 1 Nov 2016
Externally published	Yes
Event	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November
ISSN (Electronic)	2153-7003

Conference

Conference	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

D-BoTSW
MODIS
Time Series Classification
Vegetation Index

Access to Document

10.1109/IGARSS.2016.7729594

Cite this

Bailly, A., Arvor, D., Chapel, L., & Tavenard, R. (2016). Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 2300-2303). Article 7729594 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729594

Bailly, Adeline ; Arvor, Damien ; Chapel, Laetitia et al. / Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words : Application to cropland mapping in the Brazilian Amazon. 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2300-2303 (International Geoscience and Remote Sensing Symposium (IGARSS) ).

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title = "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.",

keywords = "D-BoTSW, MODIS, Time Series Classification, Vegetation Index",

author = "Adeline Bailly and Damien Arvor and Laetitia Chapel and Romain Tavenard",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

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series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",

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Bailly, A, Arvor, D, Chapel, L & Tavenard, R 2016, Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7729594, International Geoscience and Remote Sensing Symposium (IGARSS) , vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 2300-2303, 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729594

Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. / Bailly, Adeline; Arvor, Damien; Chapel, Laetitia et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2300-2303 7729594 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Arvor, Damien

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AU - Tavenard, Romain

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Y1 - 2016/11/1

N2 - 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.

AB - 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.

KW - D-BoTSW

KW - MODIS

KW - Time Series Classification

KW - Vegetation Index

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Bailly A, Arvor D, Chapel L, Tavenard R. Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2300-2303. 7729594. (International Geoscience and Remote Sensing Symposium (IGARSS) ). doi: 10.1109/IGARSS.2016.7729594

Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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