A novel feature matching method in airborne SAR image registration

The main aim of this research is to propose a novel technique for airborne SAR image registration using feature matching. The problem can be formulated as a model with three vital steps, including normalization, shape matrix estimation and geometrical invariant constraint algorithm. Starting from normalization, rotation angle between the two images can be obtained directly and independently. After rotation angle is corrected, this paper employs Shape Matrix (SM)estimation, one of the most classical feature descriptors to obtain a coarse translation matching. With the help of hierarchical strategy, an initial translation matching can be achieved rapidly and exactly. Finally, in this paper, cross ratio as geometrical invariant constraint is employed to detect and correct the mismatches due to SM estimation's deficiency. This is an optimization process with constraints based on projective invariance of cross-ratio of five coplanar feature points. Airborne SAR data acquired in eastern China in 2003 during the flood season is used for experiment, and the results show the method is effective and reliable.