A Union Matching Method for SAR Images Based on SIFT and Edge Strength

Multiplicative speckle noise often significantly affects the accuracy and adaptability of the scale-invariant feature transform (SIFT) matching method for synthetic aperture radar (SAR) images. To address this problem, this study proposes a union matching method based on the SIFT and edge strength of the SAR image. First, the rotation constraint iteratively refines the initial SIFT match set based on the parameter decomposition of the common geometry transformation model. Using this model, square summation strength (SSS) similarity is then determined. To get the optimal SIFT matches in the searching space, SSS matching based on pixel migration is applied. Furthermore, we outlined the optimization procedure of a union matching strategy. In the iterative process of global searching, the correctly matched tie-points are added individually. Finally, the accuracy, adaptability, and precision of the proposed method are validated through matching experiments on SAR images. Results showed that the proposed method is accurate and robust with respect to the automatic matching of SAR images.