Aerial image registration or matching is a geometric process of aligning two aerial images captured in different environments. Estimating the precise transformation parameters is hindered by various environments such as time, weather, and viewpoints. The characteristics of the aerial images are mainly composed of a straight line owing to building and road. Therefore, the straight lines are distorted when estimating homography parameters directly between two images. In this paper, we propose a deep homography alignment network to precisely match two aerial images by progressively estimating the various transformation parameters. The proposed network is possible to train the matching network with a higher degree of freedom by progressively analyzing the transformation parameters. The precision matching performances have been increased by applying homography transformation. In addition, we introduce a method that can effectively learn the difficult-to-learn homography estimation network. Since there is no published learning data for aerial image registration, in this paper, a pair of images to which random homography transformation is applied within a certain range is used for learning. Hence, we could confirm that the deep homography alignment network shows high precision matching performance compared with conventional works.

翻译：空中图像登记或匹配是一个对齐在不同环境中捕获的两张航空图像的几何过程。 估计精确的转换参数受到时间、 天气和视图等各种环境的阻碍。 航空图像的特征主要由建筑和道路的直线组成。 因此, 在估算两个图像之间的同影参数时, 直线被扭曲。 在本文中, 我们提出一个深同质校正网络, 通过逐步估计各种转换参数来精确匹配两张航空图像。 提议的网络可以通过逐步分析转换参数来对匹配网络进行更高程度的自由培训。 精确的匹配性能通过应用同质转换而提高。 此外, 我们引入了一种方法, 能够有效地学习难以读取的同质估计网络。 由于本文没有公布用于航空图像注册的学习数据, 因此, 在一定范围内随机应用同影转换的一副图像被使用来学习。 因此, 我们可以确认深同质校对网络显示与常规工程的高度精确匹配性。