DocumentCode :
2134058
Title :
Image time-series mining
Author :
Heas, Patrick ; Marthon, Philippe ; Datcu, Mihai ; Giros, Alain
Author_Institution :
Inst. de Recherche en Informatique de Toulouse, France
Volume :
4
fYear :
2004
fDate :
20-24 Sept. 2004
Firstpage :
2420
Abstract :
A visual information mining concept is proposed for spatio-temporal patterns discovery in remotely sensed image time-series (ITS). An information theory framework is adopted to first model information content. It results in the inference of a relevant directed graph characterizing ITS. Then the user conjecture is modeled via visual information representations: similarity measures between sub-graphs, which represents spatio-temporal events are derived and included in an interactive learning and probabilistic retrieval procedure of user-specific event-types. The present concept for ITS mining is demonstrated on multitemporal SPOT data.
Keywords :
data acquisition; data mining; geophysical techniques; geophysics computing; information retrieval; information theory; learning (artificial intelligence); remote sensing; time series; directed graph; image time series mining; inference; information content; information theory; multitemporal SPOT data; probabilistic retrieval; remote sensing; spatiotemporal pattern discovery; user conjecture; visual information mining; visual information representation; Data mining; Image databases; Image sensors; Information representation; Information retrieval; Information systems; Information theory; Multidimensional systems; Satellites; Spatiotemporal phenomena;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
Print_ISBN :
0-7803-8742-2
Type :
conf
DOI :
10.1109/IGARSS.2004.1369779
Filename :
1369779
Link To Document :
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