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Spatio-temporal data classification through multidimensional sequential patterns: Application to crop mapping in complex landscape

Yoann Pitarch 1 Dino Ienco 2, 3 Elodie Vintrou 2 Agnès Bégué 2 Anne Laurent 4 Pascal Poncelet 3 Michel Sala 4 Maguelonne Teisseire 2, 3
1 IRIT-SIG - Systèmes d’Informations Généralisées
IRIT - Institut de recherche en informatique de Toulouse
3 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 FADO - Fuzziness, Alignments, Data & Ontologies
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The main use of satellite imagery concerns the process of the spectral and spatial dimensions of the data. However, to extract useful information, the temporal dimension also has to be accounted for which increases the complexity of the problem. For this reason, there is a need for suitable data mining techniques for this source of data. In this work, we developed a data mining methodology to extract multidimensional sequential patterns to characterize temporal behaviors. We then used the extracted multidimensional sequences to build a classifier, and show how the patterns help to distinguish between the classes. We evaluated our technique using a real-world dataset containing information about land use in Mali (West Africa) to automatically recognize if an area is cultivated or not.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01239175
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Submitted on : Monday, December 7, 2015 - 2:52:57 PM
Last modification on : Friday, October 23, 2020 - 3:25:27 AM
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Yoann Pitarch, Dino Ienco, Elodie Vintrou, Agnès Bégué, Anne Laurent, et al.. Spatio-temporal data classification through multidimensional sequential patterns: Application to crop mapping in complex landscape. Engineering Applications of Artificial Intelligence, Elsevier, 2015, 37, pp.91-102. ⟨10.1016/j.engappai.2014.09.001⟩. ⟨lirmm-01239175⟩

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