We propose a deep learning framework by the probability of the predicting semantic spatial position distribution for remote sensing image registration. Traditional matching methods optimize similarity metrics with pixel-by-pixel searching, which is time consuming and sensitive to radiometric differences. Driven by learning-based methods, we take the reference and template images as inputs and map them to the semantic distribution position of the corresponding reference image. We apply the affine invariant to obtain a correspondence between the overlap of the barycenter position of the semantic template and the center pixel point, which transforms the semantic boundary alignment into a point-to-point matching problem. Additionally, two loss functions are proposed, one for optimizing the subpixel matching position and the other for determining the semantic spatial probability distribution of the matching template. In this work, we explore the influence of the template radius size, the filling form of training labels, and the weighted combination of loss function on the matching accuracy. Our experiments show that the trained model is robust to template deformation while still operating orders of magnitude faster. Furthermore, our proposed method implements high matching accuracy in four large scene images with radiometric differences. This ensures the improved speed of remote sensing image analysis and pipeline processing while facilitating novel directions in learning-based registration. Our code is freely available at https://github.com/liliangzhi110/semantictemplatematching.

我们通过预测语义空间位置分布的概率为遥感图像配准提出了一个深度学习框架。传统的匹配方法通过逐像素搜索来优化相似性度量，这既耗时又对辐射差异敏感。在基于学习的方法的驱动下，我们将参考和模板图像作为输入，并将它们映射到相应参考图像的语义分布位置。我们应用仿射不变量来获得语义模板的重心位置与中心像素点的重叠之间的对应关系，从而将语义边界对齐转化为点对点匹配问题。此外，提出了两个损失函数，一个用于优化子像素匹配位置，另一个用于确定匹配模板的语义空间概率分布。在这项工作中，我们探索了模板半径大小、训练标签的填充形式以及损失函数的加权组合对匹配精度的影响。我们的实验表明，经过训练的模型对模板变形具有鲁棒性，同时仍能更快地运行几个数量级。此外，我们提出的方法在具有辐射测量差异的四个大场景图像中实现了高匹配精度。这确保了遥感图像分析和管道处理的速度提高，同时促进了基于学习的配准的新方向。我们的代码可在 https://github.com/liliangzhi110/semantictemplatematching 免费获得。