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

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

1. 为了方便使用相对路径，我在~/.bashrc中设置了CDPATH。你可以按照下面的方式在~/.bashrc文件中加入。

CDPATH设置方法
按照上述方法添加CDPATH以后，你可以使用这样的方式快速定位到相应的数据路径：

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

2. 我以SYSU-CD数据集为例介绍代码的使用方法，首先使用tools/general/write_path.py生成数据集路径的txt文件。格式如下(详情可查看代码)

/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.使用CLIP模型对SYSU-CD数据集进行推理，https://github.com/openai/CLIP, 生成置信度json文件

3.1 首先需要安装CLIP项目，运行如下命令

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 然后运行以下命令

cd tools
bash clip_infer_sysu.sh

3.3 运行完成后，会生成如下的文件：

/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

4.可以查看tools/train.sh文件的内容，自行设定训练计划

5.我们对训练过程的权重和log文件进行了公开. 如果你无法下载百度网盘的文件，可以给我发邮件，我会及时回复并提供其他来源的下载链接。

ChangeCLIP_best_weights, passwd：2f0p google drive: https://drive.google.com/file/d/1WEkyyxezXU3bQSjUbffGu5TkmTax8tL2/view?usp=drive_link

致谢

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

引用

@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}
}