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With the development of radar imaging technology and the diversification of imaging modes, the accuracy of coregistration in the direction of azimuth required by SAR interferometry is different. At present, due to the strict theoretical model and the high accuracy, the coregistration scheme with the image information based on geometric coregistration under the condition of precise orbits become the first choice for interference processing. In this paper, TerraSAR-X images have been taken as an example to demonstrate the accuracy of interferometric coregistration and satellite orbit required by different imaging modes. Furthermore, theoretical analysis and experimental validation have also been applied. It is proved that under the condition of precise orbits, SAR interferometric processing of strip-mode image of TerraSAR-X and other satellites can be achieved by geometric registration. While, spotlight imaging mode requires image coherence or spectral diversity to further optimization of the geometric coregistration results. Since the ESD (Enhanced Spectral Diversity) offset estimation method offers the highest accuracy. It has been used to estimate the geometric coregistration errors of Sentinel-1 satellite images with different orbit and DEM conditions. The results suggest that geometric coregistration error is mainly caused by the tangential error of the satellite orbit, and the difference of geometric corregistration of three kinds of DEM is much less than 0.001 pixels, which can meet the needs of Sentinel-1 image interference registration.

The errors-in-variables (EIV) model is applied to surveying and mapping fields such as empirical coordinate transformation, line/plane fitting and rigorous modelling of point clouds and so on as it takes the errors both in coefficient matrix and observation vector into account. In many cases, not all of the elements in coefficient matrix are random or some of the elements are functionally dependent. The partial EIV (PEIV) model is more suitable in dealing with such structured coefficient matrix. Furthermore, when some reliable prior information expressed by inequality constraints is considered, the adjustment result of inequality constrained PEIV (ICPEIV) model is expected to be improved. There are two kinds of algorithms to solve the ICPEIV model under the weighted total least squares (WTLS) criterion currently. On the one hand, one can linearize the PEIV model and transform it into a sequence of quadratic programming (QP) sub-problems. On the other hand, one can directly solve the nonlinear target function by common used programming algorithms.All the QP algorithms and nonlinear programming methods are complicated and not familiar to the geodesists, so the ICPEIV model is not widely used in geodesy. In this contribution, an algorithm based on standard least squares is proposed. First, the estimation of model parameters and random variables in coefficient matrix are separated according to the Karush-Kuhn-Tucker (KKT) conditions of the minimization problem. The model parameters are obtained by solving the QP sub-problems while the variables are determined by the functional relationship between them. Then the QP problem is transformed to a system of linear equations with nonnegative Lagrange multipliers which is solved by an improved Jacobi iterative algorithm. It is similar to the equality-constrained least squares problem. The algorithm is simple because the linearization process is not required and it has the same form of classical least squares adjustment. Finally, two empirical examples are presented. The linear approximation algorithm, the sequential quadratic programming algorithm and the standard least squares algorithm are used. The examples show that the new method is efficient in computation and easy to implement, so it is a beneficial extension of classical least squares theory.

The TOPS imaging mode is the default operation mode of Sentinel-1 satellite, an overall azimuth coregistration accuracy of better than 0.001 pixels is required for this configuration in order to achieve the interferometric phase bias is less than three degrees. Based on the high accuracy of Sentinel-1 orbital information, this paper demonstrates a sufficiently high coregistration accuracy is able to be achieved by geometrical coregistration algorithm, which satisfies the pre-requirement for estimating the residual coregistration error by enhanced spectral diversity method. Based on DORIS open source software, a coregistration approach for Sentinel-A TOPS mode is implemented by an algorithm of geometric prediction of the shifts using precise orbit information, and an improvement on coregistration processing is achieved by enhanced spectral diversity. An interferometric phase jump between two Bursts could be caused by surface deformation in azimuth direction, and introduce a disturbance when estimating the residual coregistration error by enhanced spectral diversity method. In this paper, based on related theory analysis and real data processing test, an outlier detection estimation method is proposed to reduce the disturbance of enhanced spectral diversity coregistration method, which is caused by phase jumps.

Little is known about the detailed behavior of glaciers in the Karakoram Mountains. The Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data were used to obtain the surface velocity of the Yengisogat Glacier in the Karakoram Mountains. Four data sets covered all four seasons to extract the offset fields and estimate annual average surface velocity based on seasonal velocities. For the ALOS PALSAR data, the Synthetic Aperture Radar (SAR) feature-tracking (SFRT) was utilized instead of SAR interferometry because of low coherence in fast moving glaciers or large time intervals between the acquisitions of images. The accuracy of measurements was discussed and measurements were consistent with previous results. We find that there were two fast moving glacier flows existed in south tributaries of the glacier.