The triangulation constraint method is introduced into Synthetic Aperture Radar(SAR) image matching. This method uses the Scale-Invariant Feature Transform(SIFT) operator to generate feature points. After the SIFT feature points are generated, the RANdom Sample Consensus(RANSAC) algorithm is adopted to eliminate the false matching points, and the higher-precision points with the same name are obtained. Finally, the Delaunay triangulation is established by the same-named point obtained by the SIFT algorithm in the similar triangulation of the same name, the triangle center of gravity is used as the interpolated virtual name of the same name. A Normalized Cross-Correlation(NCC) constraint is performed on the virtual point of the same name, and the virtual point-name pairs that do not meet the threshold requirements are eliminated. Then, a new triangulation network is established according to the virtual same-named point obtained by the interpolation, and the triangulation network is dynamically updated to obtain more virtual names with the same name until the matching requirement is met. The experimental results show that the proposed method can effectively increase the number of matching feature points and improve the matching accuracy of radar images.