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Official Implementation of Deep Aerial Image Matching using PyTorch

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jaehyunnn/DeepAerialMatching_pytorch

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Deep Aerial Image Matching Implementation

This is the official implementation of the paper:

J.-H. Park, W.-J Nam and S.-W Lee, "A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching," Remote Sens., 2020, Vol. 12, No. 6, pp. 465
[Journal][arXiv]

Required package

  • Python 3
  • PyTorch, torchvision
  • pretrainedmodels
  • scikit-image, pandas, opencv
  • termcolor, tqdm
  • googledrivedownloader

Getting started

  • demo.py demonstrates the results on the samples of aerial image dataset
  • train.py is the main training script
  • eval_pck.py evaluates on the aerial image dataset

Trained models

Note that, models must be downloaded to the 'trained_models' folder.

Backbone Network PCK (tau=0.05) PCK (tau=0.03) PCK (tau=0.01) Download Link
ResNet101 93.8 % 82.5 % 35.1 % [here]
ResNeXt101 94.6 % 85.9 % 43.2 % [here]
Densenet169 95.6 % 88.4 % 44.0 % [here]
SE-ResNeXt101 97.1 % 91.1 % 48.0 % [here]

Paper Citation

@misc{park2020aerial,
title={A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching},
author={Jae-Hyun Park and Woo-Jeoung Nam and Seong-Whan Lee},
year={2020},
eprint={2002.01325},
archivePrefix={arXiv},
primaryClass={cs.CV}
}