Robust Local Structure Visualization for Remote Sensing Image Registration

Image registration is a fundamental and important task in remote sensing. In this article, we focus on feature-based image registration. Existing attempts often require estimating a transformation model or imposing relaxed geometric constraints to establish reliable feature correspondences. However, a parametric model cannot handle image pairs undergoing complex transformations, and relaxed methods discard a lot of structure information and the results are often coarse. To solve the above issues, we propose a local structure visualization descriptor to preserve the original structure information, and cast the feature matching task into an evaluation of the consensus of visual structure under a convolutional neural network. This strategy can effectively measure the similarity of neighborhood structure for mismatch removal. In summary, our method does not depend on a specific transformation model and can process arbitrary remote sensing images (e.g., different deformations, severe outliers, various rotations, and scaling changes). To demonstrate the robustness of our strategy for image registration, extensive experiments on various real remote sensing images for feature matching are conducted and compared against nine state-of-the-art methods, where our method gives the best performances in most scenarios.

Automated summary

This article proposes a method based on local structure visualization descriptors and convolutional neural networks for image registration, aiming to improve the reliability and precision of feature matching. The method is not restricted by specific transformation models, has good adaptability to various remote sensing images, and extensive experiments demonstrate its superior performance.