Fully Convolutional Siamese Networks for Change Detection

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This repo has been deprecated. Please see CDLab, which includes more architectures and datasets.

This is an unofficial implementation of the paper

Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch. (2018, October). Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 4063-4067). IEEE.

as the official repo does not provide the training code.

paper link

Dependencies

opencv-python==4.1.1
pytorch==1.3.1
torchvision==0.4.2
pyyaml==5.1.2
scikit-image==0.15.0
scikit-learn==0.21.3
scipy==1.3.1
tqdm==4.35.0

Tested using Python 3.7.4 on Ubuntu 16.04 and Python 3.6.8 on Windows 10.

Basic usage

# The network definition scripts are from the original repo
git clone --recurse-submodules git@github.com:Bobholamovic/FCN-CD-PyTorch.git
cd FCN-CD-PyTorch
mkdir exp
cd src

In src/constants.py, change the dataset locations to your own. In config_base.yaml, set specific configurations.

For training, try

python train.py train --exp_config ../configs/config_base.yaml

For evaluation, try

python train.py eval --exp_config ../configs/config_base.yaml --resume path_to_checkpoint --save-on

You can check the model weight files in exp/base/weights/, the log files in exp/base/logs, and the output change maps in exp/base/out.

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Changed

• 2020.3.14 Add configuration files.
• 2020.4.14 Detail README.md.
• 2020.12.8 Update framework.