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.
MAP-MRF Cloud Detection Based on PHD Filtering
ADDESSO, PAOLO;CONTE, ROBERTO;LONGO, Maurizio;RESTAINO, Rocco;VIVONE, GEMINE
2012-01-01
Abstract
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.File in questo prodotto:
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