xwmaxwma/rschange

Change detection of remote sensing images

📷 Introduction

rschange is an open-source change detection toolbox, which is dedicated to reproducing and developing advanced methods for change detection of remote sensing images.

• Supported Methods

  • STNet (ICME2023)

  • DDLNet (ICME2024 oral)

  • CDMask (Under review)

  • CD-Mamba (Under review, updated soon, refer to this)

  • Other popular methods including

    BIT (TGRS2021), SNUNet (GRSL2021), ChangeFormer (IGARSS2022),

    LGPNet(TGRS2021), SARAS-Net (AAAI2023)

• Supported Datasets

  • LEVIR-CD
  • DSIFN-CD
  • WHU-CD
  • CLCD
  • SYSU-CD

• Supported Tools

  • Training
  • Testing
  • Params and FLOPs counting
  • Class activation maps

🔥 News

• 2024/07/14: Class activation maps and some other popular methods (BIT, SNUNet, ChangeFormer, LGPNet, SARAS-Net) are now supported.

• 2024/06/24: CDMask has been submitted to Arxiv, see here, and the official implementation of CDMask is available!

🔐 Preparation

• Environment preparation

  conda create -n rscd python=3.9
  conda activate rscd
  conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
  pip install -r requirements.txt

  Note: same as rsseg. If you have already installed the environment of rsseg, use it directly.

• Dataset preprocessing

  LEVIR-CD：The original images are sized at 1024x1024. Following its original division method, we crop these images into non-overlapping patches of 256x256.

  WHU-CD: It contains a pair of dual-time aerial images measuring 32507 × 15354. These images are cropped into patches of 256 × 256 size. The dataset is then randomly divided into three subsets: the training set, the validation set, and the test set, following a ratio of 8:1:1.

  DSIFN-CD & CLCD & SYSU-CD: They all follow the original image size and dataset division method.

  Note: We also provide the pre-processed data, which can be downloaded at this link

📒 Folder Structure

Prepare the following folders to organize this repo:

  rschangedetection
      ├── rscd (code)
      ├── work_dirs (save the model weights and training logs)
      │   └─CLCD_BS4_epoch200 (dataset)
      │       └─stnet (model)
      │           └─version_0 (version)
      │              │  └─ckpts
      │              │      ├─test (the best ckpts in test set)
      │              │      └─val (the best ckpts in validation set)
      │              ├─log (tensorboard logs)
      │              ├─train_metrics.txt (train & val results per epoch)
      │              ├─test_metrics_max.txt (the best test results)
      │              └─test_metrics_rest.txt (other test results)
      └── data
          ├── LEVIR_CD
          │   ├── train
          │   │   ├── A
          │   │   │   └── images1.png
          │   │   ├── B
          │   │   │   └── images2.png
          │   │   └── label
          │   │       └── label.png
          │   ├── val (the same with train)
          │   └── test(the same with train)
          ├── DSIFN
          │   ├── train
          │   │   ├── t1
          │   │   │   └── images1.jpg
          │   │   ├── t2
          │   │   │   └── images2.jpg
          │   │   └── mask
          │   │       └── mask.png
          │   ├── val (the same with train)
          │   └── test
          │       ├── t1
          │       │   └── images1.jpg
          │       ├── t2
          │       │   └── images2.jpg
          │       └── mask
          │           └── mask.tif
          ├── WHU_CD
          │   ├── train
          │   │   ├── image1
          │   │   │   └── images1.png
          │   │   ├── image2
          │   │   │   └── images2.png
          │   │   └── label
          │   │       └── label.png
          │   ├── val (the same with train)
          │   └── test(the same with train)
          ├── CLCD (the same with WHU_CD)
          └── SYSU_CD
              ├── train
              │   ├── time1
              │   │   └── images1.png
              │   ├── time2
              │   │   └── images2.png
              │   └── label
              │       └── label.png
              ├── val (the same with train)
              └── test(the same with train)

📚 Use example

• Training

  python train.py -c configs/STNet.py

• Testing

  python test.py \
  -c configs/STNet.py \
  --ckpt work_dirs/CLCD_BS4_epoch200/stnet/version_0/ckpts/test/epoch=45.ckpt \
  --output_dir work_dirs/CLCD_BS4_epoch200/stnet/version_0/ckpts/test \

• Count params and flops

  python tools/params_flops.py --size 256

• Class activation maps

  python tools/grad_cam_CNN.py -c configs/cdmask.py

🌟 Citation

If you are interested in our work, please consider giving a 🌟 and citing our work below. We will update rschange regularly.

@inproceedings{stnet,
  title={STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images},
  author={Ma, Xiaowen and Yang, Jiawei and Hong, Tingfeng and Ma, Mengting and Zhao, Ziyan and Feng, Tian and Zhang, Wei},
  booktitle={2023 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={2195--2200},
  year={2023},
  organization={IEEE}
}

@article{ddlnet,
  title={DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning},
  author={Ma, Xiaowen and Yang, Jiawei and Che, Rui and Zhang, Huanting and Zhang, Wei},
  journal={arXiv preprint arXiv:2406.13606},
  year={2024}
}

@article{cdmask,
  title={Rethinking Remote Sensing Change Detection With A Mask View},
  author={Ma, Xiaowen and Wu, Zhenkai and Lian, Rongrong and Zhang, Wei and Song, Siyang},
  journal={arXiv preprint arXiv:2406.15320},
  year={2024}
}

💡 Acknowledgement

Thanks to previous open-sourced repo:

• mmsegmentation
• pytorch lightning
• fvcore

Thanks to the main contributor Zhenkai Wu