Fully Convolutional Siamese Networks for Change Detection

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

Prerequisites

opencv-python==4.1.1
pytorch==1.2.0
pyyaml==5.1.2
scikit-image==0.15.0
scikit-learn==0.21.3
scipy==1.3.1
tqdm==4.35.0

Tested on Python 3.7.4, Ubuntu 16.04

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 directories to your own. In config_base.yaml, feel free to modify the configurations.

For training, try

python train.py train --exp-config ../config_base.yaml

For evaluation, try

python train.py val --exp-config ../config_base.yaml --resume path_to_checkpoint

You can find the checkpoints in exp/base/weights/, the log files in exp/base/logs, and the output change maps in exp/outs.