多模态影像匹配方法已在地球多源遥感影像中获得了广泛应用，但月球多模态影像的匹配尚缺少对比性研究。为了实现高分辨率月球光学图像与SAR图像的高精度匹配，本文选择月球中纬度、低纬度、南极、北极等多个实验区影像，使用SIFT(Scale-Invariant Feature Transform）、基于区域的CFOG(Channel Features of Orientated Gradients)、HOPC(Histogram of Orientated Phase Congruency)和基于结构特征的RIFT(Radiation Invariant Feature Transform)、HAPCG(Histogram of Absolute Phase Consistency Gradients)、HOWP(Histogram of the Orientation of the Weighted Phase descriptor) 和深度学习SuperGlue、LoFTR(Local Feature TRansformer)共8种特征匹配算法进行实验比较研究，通过正确匹配点数、均方根误差、重复率和覆盖度四种指标对匹配结果进行比较分析。结果表明，HAPCG算法使用了各向异性滤波并结合绝对相位方向梯度直方图构成特征向量，匹配效果最优。LoFTR算子使用了自注意层和互注意层机制，对纹理贫乏的月球影像效果次之。HOWP和SuperGlue匹配效果居中。CFOG、HOPC和RIFT效果最差。SIFT未能实现匹配。匹配点的分布和成像光照条件、影像重叠区域相关，中低纬度地区匹配效果优于南北极地区。对HAPCG匹配结果的Stokes第一参数进行了统计分析，雨海和高地实验区的匹配点的散射特性参数的平均值高于南极北极实验区，和地形特征相符。散点图显示出HAPCG匹配点对应的Stokes第一参数和光学影像灰度值存在相关性，证明了HAPCG对非线性辐射差异较大的月球光学影像和SAR影像匹配的稳健性。本研究为月球光学影像和SAR影像匹配方法的选择提供参考，有助于月球多源数据的应用。

Multi-modal image matching methods have been widely applied in the registration of multi-source remote sensing images of the Earth, but there is a lack of comparative research on the application of multi-modal registration of lunar images. To facilitate high-precision alignment between high-resolution lunar optical imagery and SAR (Synthetic Aperture Radar) imagery, this paper conducts an experimental comparison across various lunar regions, including mid-latitude, low-latitude, the Antarctic, and the Arctic, using a suite of eight algorithms: SIFT (Scale-Invariant Feature Transform), the region-based CFOG (Channel Features of Orientated Gradients), HOPC (Histogram of Orientated Phase Congruency), and the structural feature-based RIFT (Radiation Invariant Feature Transform), HAPCG (Histogram of Absolute Phase Consistency Gradients), HOWP (Histogram of the Orientation of the Weighted Phase descriptor), along with the deep learning models SuperGlue and LoFTR (Local Feature TRansformer). The performance of these algorithms is evaluated through four metrics: the number of correct matches, root mean square error (RMSE), redundancy rate, and coverage. The findings reveal that the HAPCG algorithm, which integrates anisotropic filtering with a composite feature vector, outperforms the others in terms of matching quality. The LoFTR algorithm, leveraging self-attention and cross-attention mechanisms, demonstrates robust performance, particularly for lunar imagery with sparse textures. The HOWP and SuperGlue algorithms exhibit mid-range performance in terms of matching efficacy. In contrast, the CFOG, HOPC, and RIFT algorithms yield the least satisfactory results, with the SIFT algorithm failing to establish any matches. The distribution of matched points is influenced by factors such as imaging illumination conditions and the extent of the overlapping regions, with matches in mid and low latitude areas proving more successful than those in polar regions. A statistical analysis of the Stokes parameter for the HAPCG matches indicates that the mean values of the scattering characteristic parameters for points in the Mare and upland experimental areas are higher than those in polar regions, aligning with the topographical characteristics. Scatter plots also show a correlation between the Stokes parameter of the HAPCG-matched points and the grayscale values of the optical images, underscoring the algorithm"s robustness in matching under conditions of nonlinear radiative variability between optical and SAR imagery. This study offers insights into the selection of appropriate matching methodologies for lunar optical and SAR imagery, thereby enhancing the utility of lunar multi-source data applications.