Spatio-temporal data classification through multidimensional sequential patterns: Application to crop mapping in complex landscape
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.
| Main Authors: | , , , , , , , |
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | U30 - Méthodes de recherche, E11 - Économie et politique foncières, E10 - Économie et politique agricoles, E90 - Structure agraire, terre agricole, imagerie par satellite, télédétection, utilisation des terres, cartographie de l'utilisation des terres, paysage agricole, traitement des données, analyse de données, http://aims.fao.org/aos/agrovoc/c_2808, http://aims.fao.org/aos/agrovoc/c_36761, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_4182, http://aims.fao.org/aos/agrovoc/c_9000100, http://aims.fao.org/aos/agrovoc/c_37277, http://aims.fao.org/aos/agrovoc/c_10289, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_166, |
| Online Access: | http://agritrop.cirad.fr/578660/ http://agritrop.cirad.fr/578660/1/pitarch_Eng%20Appl%20Artificial%20Intelligence_2015.pdf |
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| Summary: | 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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