SSLChange

November 17, 2024 · View on GitHub

This is a PyTorch implementation of the paper SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation

🖊 Citation

If you find our project useful in you own research, please consider cite our paper below.

@ARTICLE{zhap2024sslchange,
  author={Zhao, Yitao and Celik, Turgay and Liu, Nanqing and Gao, Feng and Li, Heng-Chao},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={SSLChange: A Self-Supervised Change Detection Framework Based on Domain Adaptation}, 
  year={2024},
  volume={62},
  number={},
  pages={1-14},
  doi={10.1109/TGRS.2024.3489615}}

📩 11/1/2024 Our manuscript has been accepted by IEEE TGRS.

🌏 Architecture Overview

The overview of our proposed SSLChange pre-training framework for Remote Sensing Change Decetion tasks.

📗 Catalog

🎨 Visualization Demo

The visualization results of baselines w/o and w/ SSLChange on CDD-series dataset.

💼 Dependencies

Linux (Recommended) or Windows
Python 3.8+
Pytorch 1.8.0 or higher
CUDA 10.1 or higher

🕹 Code Usage

`1. Domain Adapter Training`

A Domain Adapter needs to be trained to serve as an auto-augmenter in the subsequent SSLChange Pre-training.
The training target of Domain Adapter is to project the T1 samples into T2 domain style without change image content.
The architecture could be ANY Image-to-Image Translation Algorithms.

Here we take CycleGAN with stable performance as an example to train the Domain Adapter.

📂 Step 1. Dataset Preparation for DA Training.
Only the training set of CDD dataset is used for DA training, and no label images are involved in the training.

CDD
├── /train/
│  ├── /A/
│  │  ├── 00000.jpg
│  │  └── 00001.jpg
│  │  └── ......
│  ├── /B/
│  │  ├── 00000.jpg
│  │  └── 00001.jpg
│  │  └── ......

🔥 Step 2. Train the Domain Adapter. (train.py file in CycleGAN)

python train.py --dataroot datasets/CDD/train/ --name YOUR_PROJECT

🎞 Step 3. SSLChange Pre-training Dataset Generation. (test.py file in CycleGAN)

python test.py --dataroot datasets/CDD/train/ --name YOUR_PROJECT --model cycle_gan --direction AtoB

⭐️Some generated samples of GenCDD dataset:

Original T1 images:

Generated Pseudo T2 images in GenCDD dataset:

`2. SSLChange Pre-training`

Perform the SSLChange Pre-training with the Generated GenCDD dataset.

📂 Step 1. Dataset Preparation for SSLChange Pre-training.
Only the training set of GenCDD dataset is used for SSLChange Pre-training.

GenCDD
├── /train/
│  ├── /A/
│  │  ├── 00000.jpg
│  │  └── 00001.jpg
│  │  └── ......
│  ├── /B/
│  │  ├── 00000.jpg
│  │  └── 00001.jpg
│  │  └── ......

🔥 Step 2. Label-free Pre-training of SSLChange Framework.
Only the training set of GenCDD dataset is used for SSLChange Pre-training.

cd SSLChange
python train.py --dataroot ./datasets/GenCCD/train --name YOUR_PROJECT --model sslchange --gpu_ids 0 --simsiam_aug \
                --batch_size 8 --contrastive_head sslchange_head

We release our pre-trained SSLChange weights on GenCDD dataset in Google Drive, and BaiduYunPan (code: scpt).

`3. Downstream Finetuning`

📂 Step 1. Dataset Preparation for SSLChange Pre-training.
The whole portion of CDD dataset is used for downstream supervised finetuning.

CDD
├── /train/
│  ├── /A/
│  ├── /B/
│  ├── /OUT/
├── /test/
│  ├── /A/
│  ├── /B/
│  ├── /OUT/
├── /val/
│  ├── /A/
│  ├── /B/
│  ├── /OUT/

🎮 Step 2. Pre-trained Weight Transferring.
Create a new dir to store the pre-trained SSLChange weights file.

cd Transfer-Model
mkdir pretrained_models
mkdir pretrained_models/PRETRAINED_PROJECT
cp -r ../SSLChange/checkpoint/YOUR_PROJECT/ ../Transfer-Model/pretrained_models/PRETRAINED_PROJECT/

🔥 Step 3. Downstream Finetuning.
Take the finetuning for SNUNet-CD as an example.

python main_finetune.py --dataset_dir datasets/CDD --name YOUR_FTINETUNE_PROJECT \
                        --pretrained_model PRETRAINED_PROJECT/latest_net_SimSiam.pth \
                        --gpu_ids 0 --head_type sslchange_head --classifier_name SNUNet --batch_size 4

✔ Step 4. Testing.

python eval.py --dataset_dir datasets/CDD --name YOUR_FTINETUNE_PROJECT --classifier_name SNUNet --gpu_ids 0

💡 Acknowledgement

We are grateful to those who kindly share their codes, which we referenced in our implementation.