Optimal estimation of distributed scatterer phase history parameters from meter-resolution SAR data

Measuring the long-term line-of-sight deformation using a multipass SAR data stack by standard persistent scatterer technique has been explored since the late 1990s. Researches have been continuously conducted on increasing the data coverage at non-PS-rich areas. The recently developed SqueeSAR™ technique has validated the potential of extracting useful information from distributed scatterers. With the availability of high resolution TerraSAR-X spotlight data, this technique can benefit greatly from its higher data density and quality. This article presents an algorithm of parameter estimation at distributed scatterers by maximum likelihood estimator in high resolution TS-X spotlight data. Different to SqueeSAR™, this article pays particular attention to the accurate covariance matrix estimation for phase history retrieval on each individual distributed scatterer. Solutions are presented for adaptive sample selection by a different statistical test (Anderson-Darling). An adaptive multi-resolution defringe algorithm is introduced to cope with the problem of accurate fringe removal and in turn accurate covariance matrix estimation. And finally maximum likelihood estimator was employed to estimate the model parameters by weighting each measurement according to its coherence. By combining both the persistent scatterers and distributed scatterers, the increase in the capable monitoring area is phenomenal.