针对人工描述子在多模态匹配任务中辨识能力不足导致匹配效果不佳的问题，基于构造的累积结构特征图对多模态图像特征匹配方法的特征点提取、主方向分配和描述子构造等3方面进行扩展。在特征提取阶段，在不同尺度的累积结构特征图上提取混合特征点，兼顾特征点重复性和定位精度；在主方向分配阶段，采用累积结构特征和方向构造局部结构特征场提取特征点主方向，缓解特征点主方向估计容易出错的问题；在描述子构造阶段，对累积结构特征描述子进行L1距离归一化及开方操作替代L2距离归一化，提高描述子在特征匹配阶段的辨识能力。多模态匹配对比实验结果表明：相较于LHOPC、RIFT和HAPCG，所提方法在平均匹配正确点数目和平均匹配正确率等综合指标上明显占优；相较于CSF，所提方法平均正确率提升6.6%，平均匹配精度提升5.8%，表明其有效性。

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.