Precise Aerial Image Matching based on Deep Homography Estimation
Myeong-Seok Oh, Yong-Ju Lee, Seong-Whan Lee

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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · 3D Surveying and Cultural Heritage
