Abstract: ‍ ‍Synthetic Aperture Radar （SAR） image matching is the basis for tasks such as image registration and image-based localization. This paper studied the feature-based SAR image matching problem. To address the high mismatch ratio of SAR features， a novel neighborhood consensus method was developed with combination of the feature shape information. There are mainly two steps： neighborhood construction and consensus measurement， which are all based on the shape information. In the neighborhood building step， we exploited the shape information to build an affine-invariant neighborhood. In the consensus measurement step， the local transformation matrix， calculated from the shape information was used to re-project the neighbors to evaluate the geometric consensus. In the experiment， the proposed method was compared to five popular methods. Twenty-one pairs of multi-angle airborne SAR and multi-resolution spaceborne SAR images were used as the test data. The results showed that the proposed method considerably outperformed all other methods in precision， recall and F1-measure， especially for multi-resolution data. In addition， by experimenting with three common local features， this paper found that the proposed method was widely effective for different features. However， the efficiency of the proposed algorithm was relatively lower than other methods.