Clustering of satellite image time series under Time Warping

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim at providing a coverage of the Earth every few days with high spatial resolution. In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling and one will need to compare irregularly sensed time series. In this paper, we present an approach to satellite image time series analysis which is able to both deal with irregularly sampled series and to capture distorted behaviors. We present the Dynamic Time Warping from a theoretical point of view and illustrate its abilities for satellite image time series clustering.