Multiple-view change caused by small unmanned aerial vehicles (UAVs) monitoring the ground, resulting in image distortion, multiview transformation, and low overlap. Thus, such change has a strong effect on the accuracy of image registration. In this study, we utilize a Siamese network to deal with the complexity registration of low-altitude remote-sensing images. A robust neighbor-guided patch representation is designed to describe feature points based on neighborhood relation reconstruction, and patch selection. The network is trained based on rotation-invariant layer to solve the inevitable rotation, and nonrigid deformation caused by multiview images in low-altitude remote-sensing images. With only three training images involving 4500 putative matches, the experiment results demonstrated that the learned network can process the scenarios of yaw rotation, pitch rotation, mixture, and extreme (e.g., mixture, scaling, and distortion occur simultaneously) of UAV better than other six state-of-the-art methods.

中文翻译：

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用于低空遥感图像配准的旋转不变连体网络

小型无人机（UAV）监视地面引起的多视角变化，导致图像失真、多视角变换和低重叠。因此，这种变化对图像配准的准确性有很大影响。在这项研究中，我们利用连体网络来处理低空遥感图像的复杂配准。一个健壮的邻居引导的补丁表示被设计来描述基于邻域关系重建和补丁选择的特征点。该网络基于旋转不变层进行训练，以解决低空遥感图像中多视点图像不可避免的旋转和非刚性变形问题。只有三张训练图像涉及 4500 个假定匹配，