Based on high-resolution SAR data, in this paper, a novel automatic matching model is proposed. The model, which employs a coarse to fine strategy as a whole, consists of three steps. In the first step, edge features are extracted on different levels of pyramid images and an efficient Hausdorff distance-based method is used to yield a coarse global feature match. Due to bi-tree searching, the bottleneck of Hausdorff distance's matching is well resolved. Secondly, SSDA (Sequence Similarity Detection Algorithm) is employed to acquire tie-points using a cross-searching approach which treats features extracted from master and slave images equally. Finally, local-adaptive splitting algorithm with MMSE (Minimum Mean Square Error) is used to achieve a fine matching; local-adaptive splitting algorithm is the essential process to achieve sub-pixel matching accuracy, which enhances the process's flexibility and robustness.

Airborne SAR images with high resolution are provided by the Institute of Electronics, CAS and used for experiments—the results of the experiments demonstrate that the model proposed in this paper is robust, with high accuracy (up to a fraction of a pixel), and can be successfully applied to automatic matching of high-resolution SAR images.