On Extracting Evolutions from Satellite Image Time Series
Andreea Julea, Nicolas Méger, Emmanuël Trouvé, Philippe Bolon · 2008
Nowadays, there is a growing need for processing huge volumes of observation data due to the increase in size, in resolution, in spectral channel number and in acquisition frequency of remote sensing images. When data is gathered over time for a same geographical zone, this data is said to be a Satellite Image Time Series (SITS). The informational content of SITS is rich because the observed scene is described both in time and in space. In order to exhibit potential interesting spatio-temporal patterns, we propose to extract pixel-based evolutions from SITS data by using two different symbolic techniques. The first one is based on data mining techniques that aim at extracting frequent sequential patterns (e.g.,). The second one relies on the use of tries (e.g.,) for classifying pixels according to their evolution in time. Encouraging experiments on a SPOT SITS are detailed.