Introducing prior knowledge in temporal distances for Satellite Image Time Series analysis

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. It has been shown that the Dynamic Time Warping similarity measure is a consistent tool for the comparison of radiometric profiles of temporal evolution. Actually, it makes it possible to compare time series with both different lengths and different sampling. This property allows us to make the most of partially cloud-covered images, but also to transfer the knowledge learned on an agronomical year in order to classify the next year without using reference data. This article pursues this work on satellite image time series analysis and focuses on the introduction of constraints in the distance in order to fit to the expert's knowledge about the observed phenomena.