Title :
A Novel Multitemporal InSAR Model for Joint Estimation of Deformation Rates and Orbital Errors
Author :
Lei Zhang ; Xiaoli Ding ; Zhong Lu ; Hyung-Sup Jung ; Jun Hu ; Guangcai Feng
Author_Institution :
Hong Kong Polytech. Univ., Kowloon, China
Abstract :
Orbital errors, characterized typically as longwavelength artifacts, commonly exist in interferometric synthetic aperture radar (InSAR) imagery as a result of inaccurate determination of the sensor state vector. Orbital errors degrade the precision of multitemporal InSAR products (i.e., ground deformation). Although research on orbital error reduction has been ongoing for nearly two decades and several algorithms for reducing the effect of the errors are already in existence, the errors cannot always be corrected efficiently and reliably. We propose a novel model that is able to jointly estimate deformation rates and orbital errors based on the different spatial-temporal characteristics of the two types of signals. The proposed model is able to isolate a long-wavelength ground motion signal from the orbital error even when the two types of signals exhibit similar spatial patterns. The proposed algorithm is efficient and requires no ground control points. In addition, the method is built upon wrapped phases of interferograms, eliminating the need of phase unwrapping. The performance of the proposed model is validated using both simulated and real data sets. The demo codes of the proposed model are also provided for reference.
Keywords :
radar interferometry; remote sensing by radar; synthetic aperture radar; InSAR imagery; deformation rate estimation; interferogram wrapped phases; interferometric synthetic aperture radar; long-wavelength artifacts; long-wavelength ground motion signal; multitemporal InSAR model; multitemporal InSAR products; orbital error reduction; sensor state vector; Atmospheric modeling; Deformable models; Delays; Orbits; Polynomials; Synthetic aperture radar; Vectors; Interferometric synthetic aperture radar (SAR) (InSAR); SAR; least squares; orbital error; sparse matrix;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2013.2273374