# Precise Aerial Image Matching based on Deep Homography Estimation

arXiv: https://arxiv.org/abs/2107.08768 · HTML: https://mlsanity.com/paper/2107.08768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Convolutional neural network architecture for geometric matching](https://mlsanity.com/paper/1703.05593/) (2017)
- [End-to-end weakly-supervised semantic alignment](https://mlsanity.com/paper/1712.06861/) (2017)
- [Attentive Semantic Alignment with Offset-Aware Correlation Kernels](https://mlsanity.com/paper/1808.02128/) (2018)
- [An Artificial Agent for Robust Image Registration](https://mlsanity.com/paper/1611.10336/) (2016)
- [A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching](https://mlsanity.com/paper/2002.01325/) (2020)

## Built on by (most cited first, 1 of 1)

- [LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching](https://mlsanity.com/paper/2608.04106/) (2026)

## Abstract (arXiv)

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.