Spatio-Temporal Mining of PolSAR Satellite Image Time Series
Résumé
This paper presents an original data mining approach for describing Satellite Image Time Series (SITS) spa- tially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the frequent grouped sequential patterns, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are ac- tively used to face large data volumes and to select evolu- tions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Preliminary experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont-Blanc test-site are used to illustrate the proposed approach.