Aiming at the problem of poor matching effect caused by insufficient discriminative ability of artificial descriptors in multimodal matching tasks, the multimodal image feature matching method is extended from three aspects of feature point extraction (FPE), dominant orientation assignment (DOA) and feature descriptor construction (FDC), based on the constructed cumulative structure feature (CSF) map. In FPE stage, the hybrid points are extracted from CSF maps with different scales, taking into account the repeatability of feature points and positioning accuracy. In DOA stage, the CSF map and its orientation map are utilized to build the local structural feature field to extract the orientations of feature points, so as to alleviate the error-prone problem of dominant orientation estimation of feature points. In FDC stage, L1 distance and root operation are used instead of L2 distance to normalize the CSF descriptor, to improve the discriminative ability of descriptor in the feature matching process. The comparative experimental results of multimodal matching show that the proposed method is significantly superior to LHOPC, RIFT and HAPCG in terms of comprehensive indicators such as the average number of correct matching and average ratio of correct matching; and compared with CSF, the average ratio of correct matching of the proposed method was increased by 6.6%, and the average matching accuracy is increased by 5.8%, illustrating the effectiveness of the proposed method.