A Multilayer Fusion Network With Rotation- Invariant and Dynamic Feature Representation for Multiview Low-Altitude Image Registration

Abstract

Due to human and natural factors, when the small unmanned aerial vehicles (UAVs) are monitoring the ground, multiview transformation problems such as image distortion and low overlap will occur, which will inhibit the accuracy of low-altitude image registration and limit the subsequent application. In this letter, we propose a mismatch removal method based on the Siamese architecture to solve the issues of multiview images. A dynamic neighbor-guided patch representation is designed to enhance the representation of each feature point. Meanwhile, a multilayer fusion is used to obtain more comprehensive information on feature points, and whether a pair of points correspond depends on the similarity of its descriptors. The network is trained by adding a rotation-invariant layer to solve the inevitable rotation and image distortion in multiview scenarios. The experimental results prove that our method can deal with the scenarios of the horizontal rotation, vertical rotation, mixture, scaling, and extreme, and is better than the other five state-of-the-art methods in most scenarios.