由于卫星遥感合成孔径雷达（SAR）与可见光图像之间存在显著的非线性辐射差异，故现有的SAR与可见光图像配准算法存在实时性差、旋转与尺度不变性弱等问题。针对目前算法只关注图像局部特征的外观信息，而忽略几何结构信息的问题，提出了一种结合卷积与图神经网络（GNN）的SAR与可见光图像匹配方法。该方法采用卷积神经网络进行特征检测与描述的同时，引入了GNN进行特征匹配。与最近邻匹配算法仅利用局部描述符信息相比，GNN先将特征点的位置坐标嵌入到描述符中，使得描述符具有几何位置信息，再利用注意力机制进一步聚合特征描述符的几何上下文信息，最后利用可微分的最优传输算法直接输出特征点的匹配结果，保证了网络可进行端到端的训练。实验结果表明：所提方法在大范围旋转与尺度变化的配准任务上，获得了最先进的性能；与目前主流配准算法辐射不变特征变换相比，所提方法在提升匹配精度的同时，计算速度也提高了50倍以上。

Due to the significant nonlinear radiometric differences between synthetic aperture radar (SAR) and optical images obtained by satellite remote sensing, the current SAR and optical images registration algorithms are weakened by their poor real-time performance and weak rotation and scale invariance. To address the problem that the current algorithms only focus on the appearance information on the local features of images and ignore the geometric structure information, a SAR and optical image matching method combining the convolutional and graph neural network (GNN) is proposed. The method uses the convolutional neural network for feature detection and description, and adopts the GNN for feature matching. In contrast to the nearest neighbor matching algorithm that merely uses local descriptor information, the GNN embeds the location coordinates of feature points into the descriptors, thereby providing the descriptors with geometric location information. Then, the geometric context information of the feature descriptors is further aggregated with the attention mechanism. Finally, the matching results of the feature points are directly output by the differentiable optimal transport algorithm to ensure that the network can be trained in an end-to-end manner. The experimental results show that the proposed method achieves state-of-the-art performance on the registration task featuring rotation and scale variation in a large range. In addition, compared with the current mainstream registration algorithm radiation-invariant feature transform, the proposed method not only improves matching accuracy, but also increases the computational speed by more than 50 times.