MAP-MRF cloud detection based on PHD filtering

Temporal correlation has been recently taken into consideration to improve the performances of cloud detection algorithms. We exploit this concept within the Maximum A Posteriori Markov Random Field MAP-MRF framework by adding a penalization term which is determined according to the hystory of cloud masses. Multi Target Tracking of clouds is accomplished by methods of Finite Set Statistics (FISS) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.