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 penalty term which is determined according to the history of cloud masses. Multi Target Tracking of clouds is accomplished by methods of FInite Set STatistics (FISST) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.