Precise Aerial Image Matching based on Deep Homography Estimation

TL;DR

This paper introduces a deep homography alignment network that improves the precision of aerial image matching by progressively estimating transformation parameters, effectively handling environmental distortions and lacking real-world training data.

Contribution

It presents a novel deep learning approach for aerial image registration that progressively estimates homography parameters and learns from synthetically transformed image pairs.

Findings

High-precision matching performance demonstrated

Effective learning from synthetic data without real-world datasets

Outperforms conventional methods in accuracy

Abstract

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.