Automatic matching of high-resolution SAR images

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 tiepoints 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; localadaptive 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.