Spatio-temporal data classification through multidimensional sequential patterns: Application to crop mapping in complex landscape. (January 2015)

• Record Type: Journal Article
• Authors: Pitarch, Yoann; Ienco, Dino; Vintrou, Elodie; Bégué, Agnès; Laurent, Anne; Poncelet, Pascal; Sala, Michel; Teisseire, Maguelonne
• Is Part Of: Engineering applications of artificial intelligence. Volume 37(2015:Jan.)
• Page Start: 91
• Page End: 102
• Publication Date: 2015-01
• Subjects: Knowledge discovery -- Data mining -- MODIS images -- Remote sensing -- Land cover; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2014.09.001
• Languages: English
• ISSNs: 0952-1976

Abstract

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.