  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.
