Robust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering

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.