Kalman filter application to GBSAR displacement map and velocity estimation

The Kalman filter (KF) is a powerful tool able to estimate and predict the time evolution of a multidimensional variable by using a specific kinematic model and a model error. Recently, KF has been successfully applied to reduce the noise of scenario with low coherence by estimating the kinematic and error model in those areas with high coherence. In this paper, KF was applied to retrieve the cumulative displacement map and to estimate the velocity map of a vegetated scenario in Spain. The results show the capability of KF to recover the displacement of noisy areas. Moreover, using KF it is also possible to reduce the atmospheric residual in cumulative displacement.