Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting

Feature matching is a fundamental problem in feature-based remote sensing image registration. Due to the ground relief variations and imaging viewpoint changes, remote sensing images often involve local distortions, leading to difficulties in high-accuracy image registration. To address this issue, in this article, we propose a robust feature matching method called First Neighbor Relation Guided (FNRG) for remote sensing image registration via guided hyperplane fitting. The key idea of FNRG is to exploit the first neighbor relation of feature points between two images for seeking consistent seeds in a parameter-free manner. To boost more consistent matches based on the consistent seeds, we formulate the feature matching problem into an affine hyperplane fitting problem by imposing the motion consistency, and then we design a hyperplane updating strategy to refine the fitting model. We also introduce a locality preserving structure-based cost function to promote the matching performance of the hyperplane updating strategy. Our method can mine consistent matches from thousands of putative ones within only a few milliseconds, and it also can handle the data with a large-scale change, rotation, or severe nonrigid deformation. Extensive experiments on the remote sensing image data sets with different types of image transformations show that the proposed method achieves significant superiority over several state-of-the-art methods.

Automated summary

This article proposes a robust feature matching method called FNRG for remote sensing image registration, which addresses the problem of local distortions in images. The method utilizes the first neighbor relation of feature points between two images to find consistent matches and employs hyperplane fitting and a cost function to improve matching performance. Experimental results demonstrate that the proposed method outperforms several state-of-the-art methods in handling complex remote sensing images.