Abstract
Mapping large areas using airborne dual-antenna interferometric synthetic aperture radar (InSAR) usually requires processing and mosaicking of different scenes from multiple strips. The overlapping areas of these multiple strips should have consistent elevation values. Due to the unstable attitude of the plane, the interferometric parameters usually vary for each scene during mapping. Therefore, interferometric calibration technology for high-precision height retrieval is required for the correction of the interferometric errors. The traditional interferometric calibration methods for a single scene usually use ground control points (GCPs) to estimate the interferometric parameters – this method cannot guarantee a consistent height in the area of overlap. Besides, GCPs are difficult to deploy over rough terrain, making it impossible to use traditional calibration methods. In this article, a joint interferometric calibration method based on the block adjustment theory used in photogrammetry is proposed for airborne dual-antenna InSAR. This method considers the accurate digital elevation model (DEM) height reconstruction model and can be applied with sparse GCPs. The principle of the proposed method is to make the best use of the GCPs within all the scenes and the tie points (TPs) between the adjacent scenes to establish an error relationship model. First, the weighting values of all GCPs and TPs based on their retrieval elevation error caused by the interferometric phase error and the position distribution difference are introduced in the proposed method. Next, the interferometric parameters are weighted to reduce the condition number of the normal equation. Then, an alternative approximation approach combined with the sparse matrix decomposition technique LDLT is utilized to solve the normal equation, and the corrected interferometric parameters for each scene are obtained. High-precision joint interferometric calibration results for airborne InSAR systems are achieved by the proposed method and validated by experiment. Using the proposed method, the average mean error (AME) and root mean square error (RMSE) are below 0.6037 and 0.9176 m, respectively. Meanwhile, the maximum AME and RMSE of the reconstructed DEM height difference for the validation TPs in the overlapped area of the adjacent scenes are reduced from 1.2909 and 1.7245 m to 0.8864 and 1.2087 m, respectively.
Acknowledgement
The authors acknowledge the valuable comments and suggestions of the anonymous reviewers and the editor.