As a fundamental and critical task in feature-based remote sensing image registration, feature matching refers to establishing reliable point correspondences from two images of the same scene. In this article, we propose a simple yet efficient method termed linear adaptive filtering (LAF) for both rigid and nonrigid feature matching of remote sensing images and apply it to the image registration task. Our algorithm starts with establishing putative feature correspondences based on local descriptors and then focuses on removing outliers using geometrical consistency priori together with filtering and denoising theory. Specifically, we first grid the correspondence space into several nonoverlapping cells and calculate a typical motion vector for each one. Subsequently, we remove false matches by checking the consistency between each putative match and the typical motion vector in the corresponding cell, which is achieved by a Gaussian kernel convolution operation. By refining the typical motion vector in an iterative manner, we further introduce a progressive strategy based on the coarse-to-fine theory to promote the matching accuracy gradually. In addition, an adaptive parameter setting strategy and posterior probability estimation based on the expectation–maximization algorithm enhance the robustness of our method to different data. Most importantly, our method is quite efficient where the gridding strategy enables it to achieve linear time complexity. Consequently, some sparse point-based tasks may inspire from our method when they are achieved by deep learning techniques. Extensive feature matching and image registration experiments on several remote sensing data sets demonstrate the superiority of our approach over the state of the art.

中文翻译：

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通过线性自适应滤波实现遥感图像配准的鲁棒特征匹配

作为基于特征的遥感图像配准中的一项基本和关键任务，特征匹配是指从同一场景的两幅图像中建立可靠的点对应关系。在本文中，我们提出了一种简单而有效的方法，称为线性自适应滤波 (LAF)，用于遥感图像的刚性和非刚性特征匹配，并将其应用于图像配准任务。我们的算法首先基于局部描述符建立假定的特征对应关系，然后专注于使用几何一致性先验以及过滤和去噪理论去除异常值。具体来说，我们首先将对应空间划分为几个不重叠的单元格，并为每个单元格计算一个典型的运动向量。随后，我们通过检查每个假定匹配与相应单元格中的典型运动向量之间的一致性来去除错误匹配，这是通过高斯核卷积操作实现的。通过以迭代的方式细化典型的运动向量，我们进一步引入了基于粗到细理论的渐进策略，以逐步提高匹配精度。此外，基于期望最大化算法的自适应参数设置策略和后验概率估计增强了我们的方法对不同数据的鲁棒性。最重要的是，我们的方法非常有效，网格策略使其能够实现线性时间复杂度。因此，一些基于稀疏点的任务在通过深度学习技术实现时可能会从我们的方法中得到启发。