Improve multi-baseline InSAR parameter retrieval by semantic information from optical images

One of the most unique benefits of multi-baseline synthetic aperture radar interferometry (InSAR) is the longterm monitoring of subtle ground deformation over large areas. Most state-of-the-art algorithms for retrieving such parameter are based on single pixels, e.g. Permanent Scatterer InSAR or clusters of ergodic pixels with stationary phases e.g. SqueeSAR. None of the studies has addressed the joint inversion in an object level, where the true interferometric phase may be varying subject to topography and deformation. Recently, one study has investigated SAR and optical data fusion in order to make use of the rich semantic information from optical images. Based on that work, we seek to investigate the possibility of an object-level multi-baseline InSAR deformation reconstruction given the semantic information from the corresponding optical images. In this paper, we introduced the tensor model for the multi-baseline InSAR inversion and proposed a maximum a posteriori estimator of the deformation parameters by including a spatial prior function in the objective function. Substantial improvement in the deformation estimation is observed in the experiments using both simulated and the real SAR data

Improve multi-baseline InSAR parameter retrieval by semantic information from optical images

One of the most unique benefits of multi-baseline synthetic aperture radar interferometry (InSAR) is the longterm monitoring of subtle ground deformation over large areas. Most state-of-the-art algorithms for retrieving such parameter are based on single pixels, e.g. Permanent Scatterer InSAR or clusters of ergodic pixels with stationary phases e.g. SqueeSAR. None of the studies has addressed the joint inversion in an object level, where the true interferometric phase may be varying subject to topography and deformation. Recently, one study has investigated SAR and optical data fusion in order to make use of the rich semantic information from optical images. Based on that work, we seek to investigate the possibility of an object-level multi-baseline InSAR deformation reconstruction given the semantic information from the corresponding optical images. In this paper, we introduced the tensor model for the multi-baseline InSAR inversion and proposed a maximum a posteriori estimator of the deformation parameters by including a spatial prior function in the objective function. Substantial improvement in the deformation estimation is observed in the experiments using both simulated and the real SAR data