Awesome

ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning

https://www.sciencedirect.com/science/article/pii/S0924271624000042

You can install the environment through environment.yml and requirements.txt.

1. In order to facilitate the use of relative paths, CDPATH is set in the ~/.bashrc file. Here is how to add this setting in the ~/.bashrc。

After adding CDPATH as mentioned above, you can quickly navigate to the respective data path in the following way:

import os  
data_root = os.path.join(os.environ.get("CDPATH"), 'SYSU-CD')

2. I will use the SYSU-CD dataset as an example to introduce the usage of the code. First, use tools/general/write_path.py to generate a txt file for the dataset path. The format is as follows (for details, please refer to the code):

/home/user/dsj_files/CDdata/SYSU-CD/test/time1/03414.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/03414.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/03414.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/00708.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/00708.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/00708.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/03907.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/03907.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/03907.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/03107.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/03107.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/03107.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/02776.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/02776.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/02776.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/01468.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/01468.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/01468.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/00026.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/00026.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/00026.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/02498.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/02498.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/02498.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/02439.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/02439.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/02439.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/01057.png  /home/user/dsj_files/CDdata/SYSU-CD/test/time2/01057.png  /home/user/dsj_files/CDdata/SYSU-CD/test/label/01057.png

3.Use the CLIP model to perform inference on the SYSU-CD dataset. https://github.com/openai/CLIP, Generate a confidence JSON file.

3.1 First, it is necessary to install the CLIP project. Run the following command:

conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git

3.2 Then run the following command:

cd tools
bash clip_infer_sysu.sh

3.3 After running the command, the following files will be generated:

/home/user/dsj_files/CDdata/SYSU-CD/train/time1_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/train/time2_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/val/time1_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/val/time2_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/test/time1_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/test/time2_clipcls_56_vit16.json

To facilitate debugging code, the following is the download method of the SYSU-CD dataset.
https://pan.baidu.com/s/1E2Q0BrnWqR2Fkxj5LRRU7A passwd: qyvg
https://drive.google.com/file/d/1MYEf67kO72avJWik1Dtlm3h9RWrSQQLo/view?usp=sharing

4.For training and testing, You can view the contents of the tools/train.sh file and set the training plan yourself.

5.We have made the weights and log files of the training process public. If you cannot download the files of Baidu Netdisk, you can send me an email and I will reply in time and provide download links from other sources.

ChangeCLIP_best_weights, 提取码: rscd

6. The following is the comparison between ChangeCLIP and advanced algorithms.

Acknowledgements

This repo benefits from awesome works of mmsegmentation, DenseCLIP, CLIP. Please also consider citing them.

Cite

@article{DONG202453,
title = {ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {208},
pages = {53-69},
year = {2024},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2024.01.004},
url = {https://www.sciencedirect.com/science/article/pii/S0924271624000042},
author = {Sijun Dong and Libo Wang and Bo Du and Xiaoliang Meng}
}