In this article, we propose an automatic and efficient method to solve optical and synthetic aperture radar (SAR) image registration using the improved phase congruency (PC) model. First, evenly distributed keypoints are extracted from the optical images via the block-Harris method. Complementary grid points are then selected in image regions with poor structural information and supplemented to the keypoint set. For each keypoint, a robust feature representation that captures the local spatial relationship is proposed based on the improved PC model. Specifically, we propose to use two different PC models, the classic PC and the SAR-PC, to construct features for optical and SAR images, respectively. The PC features of several directions are aggregated to construct the feature descriptors, and a similarity metric via the phase correlation of feature descriptors is obtained. The proposed similarity metric cannot only find accurate correspondence but also present efficient results without presetting the size of the search region. We compare the proposed method with two baselines and state-of-the-art (SOTA) methods, i.e., OS-SIFT, histogram of oriented PC, and channel features of oriented gradients, in various scenarios. The results show that the proposed method outperforms the baselines and shows comparable performance with SOTA methods in regions with abundant structural information and better performance in regions with less structural information. Moreover, we build a high-resolution optical and SAR image matching dataset, which consists of 10 692 nonoverlapping patch pairs of $256\times 256$ pixels and 1-m resolution. Results of two benchmarks, Siamese deep matching network, and conditional generative adversarial networks show that this dataset is practical and challenging.

中文翻译：

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通过改进的相位一致性模型自动配准光学和 SAR 图像

在本文中，我们提出了一种使用改进的相位一致性 (PC) 模型来解决光学和合成孔径雷达 (SAR) 图像配准的自动有效方法。首先，通过块哈里斯方法从光学图像中提取均匀分布的关键点。然后在结构信息较差的图像区域中选择互补网格点并补充到关键点集。对于每个关键点，基于改进的 PC 模型提出了捕获局部空间关系的鲁棒特征表示。具体来说，我们建议使用两种不同的 PC 模型，经典 PC 和 SAR-PC，分别为光学和 SAR 图像构建特征。将多个方向的PC特征聚合起来构造特征描述子，并通过特征描述符的相位相关性获得相似性度量。所提出的相似性度量不仅可以找到准确的对应关系，而且还可以在不预设搜索区域大小的情况下呈现有效的结果。我们将所提出的方法与两种基线和最先进的 (SOTA) 方法进行比较，即在各种场景中，OS-SIFT、定向 PC 的直方图和定向梯度的通道特征。结果表明，所提出的方法优于基线，并且在结构信息丰富的区域表现出与 SOTA 方法相当的性能，在结构信息较少的区域表现出更好的性能。此外，我们构建了一个高分辨率的光学和 SAR 图像匹配数据集，它由 10 692 个不重叠的补丁对组成，像素为 256 美元×256 美元，分辨率为 1 米。