Multi-view SAR image matching enhancement based on epipolar constraint

Multi view SAR is an important observation mode in SAR, which can obtain information from different views of the scene. Due to the inconsistent anisotropic scattering parameters of the target, the same target exhibits different grayscale changes on SAR images from different views, resulting in fewer feature points being extracted. For scenes with complex height variations, such as mountain areas, the offset of heights under different views is inconsistent, leading to severe local distortion in multi view SAR images. When extracting features, it is easy to contain a large number of incorrect matches. Therefore, it is crucial to effectively preserve correct correspondence and eliminate incorrect matches in multi-view SAR images. This paper combines the principles of multi view SAR imaging and proposes the use of nearest and second neighbor ratio algorithm and epipolar constraint for feature point matching of multi view SAR images. Firstly, based on the different scattering coefficients of the same target contained in multi view SAR images, feature extraction is proposed for multi view SAR images, and the nearest and second neighbor ratio algorithm is used for initial coarse matching. Subsequently, in order to increase the accuracy of feature matching, it was proposed to introduce the epipolar constraint of multi view SAR images for feature matching. Experiments showed that the proposed algorithm retained more correct feature points compared to the nearest and second neighbor ratio matching algorithm and MSAC algorithm for multi view SAR image feature matching.