rschange is an open-source change detection toolbox, which is dedicated to reproducing and developing advanced methods for change detection of remote sensing images.
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Supported Methods
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STNet (ICME2023)
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DDLNet (ICME2024 oral)
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CDMask (Under review)
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STeinFormer (JSTARS2025)
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CD-Lamba (Under review)
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CDxLSTM (GRSL2025)
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Other popular methods including
BIT (TGRS2021), SNUNet (GRSL2021), ChangeFormer (IGARSS2022),
LGPNet(TGRS2021), SARAS-Net (AAAI2023), USSFCNet (TGRS2023), AFCF3DNet (TGRS2023), RSMamba (GRSL2024), ChangeMamba (TGRS2024)
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Supported Datasets
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Supported Tools
- Training
- Testing
- Params and FLOPs counting
- Class activation maps
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2025/04/16: CDxLSTM has been accepted by GRSL2025. -
2025/03/13: The official files of the environment preparation are now available in rscd_mamba. -
2025/02/11: The official implementation of CD-Lamba, CDXLSTM and some other popular methods (RSMamba, ChangeMamba) are now available. -
2025/01/02: STeInFormer has been accepted by JSTARS2025. -
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!
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Environment preparation
conda create --name rscd python=3.8 conda activate rscd conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia pip install pytorch-lightning==2.0.5 pip install scikit-image==0.19.3 numpy==1.24.4 pip install torchmetrics==1.0.1 pip install -U catalyst==20.09 pip install albumentations==1.3.1 pip install einops==0.6.1 pip install timm==0.6.7 pip install addict==2.4.0 pip install soundfile==0.12.1 pip install ttach==0.0.3 pip install prettytable==3.8.0 pip install -U openmim pip install triton==2.0.0 mim install mmcv pip install -U fvcore
If you need to run a model based on Mamba, please additionally download the releases and then perform the following installation for the Mamba environment.
pip install causal_conv1d-1.2.0.post1+cu118torch2.0cxx11abiFALSE-cp38-cp38-linux_x86_64.whl pip install mamba_ssm-1.2.0.post1+cu118torch2.0cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
[Optional] We have also prepared compressed files rscd_mamba for the CD-Lamba's environment, which you can download directly and install according to the following instructions.
// Firstly, you must be in a Linux environment (Ubuntu in Linux or WSL2 in windows). // Then, place this compressed file in the folder of \home\xxx\anaconda3\envs\ // Finally, mkdir -p rscd_mamba && tar -xzf rscd_mamba.tar.gz -C rscd_mamba conda activate rscd_mamba
Note: same as rsseg. If you have already installed the environment of rsseg, use it directly.
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Dataset preprocessing
LEVIR-CD๏ผThe 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 ร 15354. These images are cropped into patches of 256 ร 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: The A434 y 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
Prepare the following folders to organize this repo:
rschangedetection
โโโ rscd (code)
โโโ work_dirs (save the model weights and training logs)
โ โโCLCD_BS4_epoch200 (dataset)
โ โโstnet (model)
โ โโversion_0 (version)
โ โ โโckpts
โ โ โโtest (the best ckpts in test set)
โ โ โโval (the best ckpts in validation set)
โ โโlog (tensorboard logs)
โ โโtrain_metrics.txt (train & val results per epoch)
โ โโtest_metrics_max.txt (the best test results)
โ โโtest_metrics_rest.txt (other test results)
โโโ data
โโโ LEVIR_CD
โ โโโ train
โ โ โโโ A
โ โ โ โโโ images1.png
โ โ โโโ B
โ โ โ โโโ images2.png
โ โ โโโ label
โ โ โโโ label.png
โ โโโ val (the same with train)
โ โโโ test(the same with train)
โโโ DSIFN
โ โโโ train
โ โ โโโ t1
โ โ โ โโโ images1.jpg
โ โ โโโ t2
โ โ โ โโโ images2.jpg
โ โ โโโ mask
โ โ โโโ mask.png
โ โโโ val (the same with train)
โ โโโ test
โ โโโ t1
โ โ โโโ images1.jpg
โ โโโ t2
โ โ โโโ images2.jpg
โ โโโ mask
โ โโโ mask.tif
โโโ WHU_CD
โ โโโ train
โ โ โโโ image1
โ โ โ โโโ images1.png
โ โ โโโ image2
โ โ โ โโโ images2.png
โ โ โโโ label
โ โ โโโ label.png
โ โโโ val (the same with train)
โ โโโ test(the same with train)
โโโ CLCD (the same with WHU_CD)
โโโ SYSU_CD
โโโ train
โ โโโ time1
โ โ โโโ images1.png
โ โโโ time2
โ โ โโโ images2.png
โ โโโ label
โ โโโ label.png
โโโ val (the same with train)
โโโ test(the same with train)
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Training
python train.py -c configs/STNet.py
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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 \
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Count params and flops
python tools/params_flops.py --size 256
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Class activation maps
python tools/grad_cam_CNN.py -c configs/cdxformer.py --layer=model.net.decoderhead.LHBlock2.mlp_l
If you are interested in our work, please consider giving a ๐ 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}
}
@INPROCEEDINGS{ddlnet,
author={Ma, Xiaowen and Yang, Jiawei and Che, Rui and Zhang, Huanting and Zhang, Wei},
booktitle={2024 IEEE International Conference on Multimedia and Expo (ICME)},
title={DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning},
year={2024},
volume={},
number={},
pages={1-6},
doi={10.1109/ICME57554.2024.10688140}}
@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}
}
@ARTICLE{steinformer,
author={Ma, Xiaowen and Wu, Zhenkai and Ma, Mengting and Zhao, Mengjiao and Yang, Fan and Du, Zhenhong and Zhang, Wei},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={STeInFormer: SpatialโTemporal Interaction Transformer Architecture for Remote Sensing Change Detection},
year={2025},
volume={18},
number={},
pages={3735-3745},
doi={10.1109/JSTARS.2024.3522329}}
@ARTICLE{cdxlstm,
author={Wu, Zhenkai and Ma, Xiaowen and Lian, Rongrong and Zheng, Kai and Zhang, Wei},
journal={IEEE Geoscience and Remote Sensing Letters},
title={CDxLSTM: Boosting Remote Sensing Change Detection With Extended Long Short-Term Memory},
year={2025},
volume={22},
number={},
pages={1-5},
keywords={Feature extraction;Semantics;Transformers;Sensors;Remote sensing;Graphics processing units;Correlation;Convolutional codes;Computational modeling;Computational complexity;Extended long short-term memory (xLSTM);remote sensing change detection (RS-CD);spatiotemporal interaction},
doi={10.1109/LGRS.2025.3562480}}
If you are confused about the content of our paper or look forward to further academic exchanges and cooperation, please do not hesitate to contact us. The e-mail address is xwma@zju.edu.cn. We look forward to hearing from you!
Thanks to previous open-sourced repo:
Thanks to the main contributor Zhenkai Wu