robust regression-based linear model adjustment method for SAR interferogram stacking techniques

• Published in: International journal of remote sensing 2013, Vol.34 (16), p.5651-5665
• Main Authors: Zhang, Kui, Ge, Linlin, Li, Xiaojing, Ng, Alex Hay-Man
• Format: Article
• Language: English
• Subjects: Accuracy; Animal, plant and microbial ecology; Applied geophysics; Atmospherics; Biological and medical sciences; Deformation; Dynamical systems; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Interferometric synthetic aperture radar; Internal geophysics; least squares; new methods; Nonlinear dynamics; remote sensing; Stacking; Synthetic aperture radar; Teledetection and vegetation maps
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2013.791757

Differential interferometric synthetic aperture radar (DInSAR) is recognized as an effective remote-sensing technique for a variety of ground deformation mapping applications. Centimetre-level measurement accuracy can be achieved with the DInSAR technique. However, two key limitations – temporal decorrelation and phase delay due to atmospheric inhomogeneities – might decrease the accuracy of deformation measurements. To overcome such problems, interferogram stacking techniques, which extend the DInSAR technique, have been developed in recent years. In most implementations of such techniques, a so-called ‘linear model adjustment’ step is required to obtain the relative linear deformation rate and digital elevation model error from the double-differenced phase observations along the stack. In this step, since a non-linear system has to be resolved, the traditional least squares method cannot be directly applied. In order to overcome this problem, several methods have been developed in recent years. In this article, a new method has been developed to deal with the problem of linear model adjustment. This method repeatedly uses robust regression to resolve the non-linear system and is much easier to implement compared with other methods. This method is applied to both simulated and real data, and the results demonstrate that it can be efficiently used for linear model adjustment.