https://github.com/ZhengJianwei2/DMINet

Change Detection on Bi-temporal Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network (TGARS 2023) [paper](https://ieeexplore.ieee.org/document/10034787) and [source_code](https://github.com/ZhengJianwei2/DMINet/)
https://github.com/ZhengJianwei2/DMINet

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Change Detection on Bi-temporal Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network (TGARS 2023) [paper](https://ieeexplore.ieee.org/document/10034787) and [source_code](https://github.com/ZhengJianwei2/DMINet/)

• Host: GitHub
• URL: https://github.com/ZhengJianwei2/DMINet
• Owner: ZhengJianwei2
• Created: 2022-05-06T06:51:33.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-07T07:10:08.000Z (over 1 year ago)
• Last Synced: 2024-07-23T02:41:49.726Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 4.27 MB
• Stars: 51
• Watchers: 1
• Forks: 6
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - Feng Y, Jiang J, Xu H, et al. Change Detection on Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network

README

        
# DMINet

## Papers

* Change Detection on Bi-temporal Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network (TGARS 2023) [paper](https://ieeexplore.ieee.org/document/10034787) and [source_code](https://github.com/ZhengJianwei2/DMINet/)

* ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection (TGARS 2022) [paper](https://ieeexplore.ieee.org/document/9759285) and [source_code](https://github.com/ZhengJianwei2/ICIF-Net/)

## 1. Environment setup

This code has been tested on on the workstation with Intel Xeon CPU E5-2690 v4 cores and GPUs of NVIDIA TITAN V with a single 12G of video memory, Python 3.6, pytorch 1.9, CUDA 10.0, cuDNN 7.6.

## 2. Download the datesets:

* LEVIR-CD:

[LEVIR-CD](https://justchenhao.github.io/LEVIR/)

* WHU-CD:

[WHU-CD](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html)

* GZ-CD:

[GZ-CD](https://github.com/daifeng2016/Change-Detection-Dataset-for-High-Resolution-Satellite-Imagery)

* SYSU-CD:

[SYSU-CD](https://github.com/liumency/SYSU-CD)

and put them into data directory.

## 3. Download the models (loading models):

* [models](https://pan.baidu.com/s/1m2vDFG8-FOCYdeidLeYOsA) code: x1qk

and put them into checkpoints directory.

## 4. Train

    python main_cd.py

    

## 5. Test

    python eval_cd.py

## 6. Cite

If you use DMINet in your work please cite our paper:

* BibTex：

    @ARTICLE{10034787,

      author={Feng, Yuchao and Jiang, Jiawei and Xu, Honghui and Zheng, Jianwei},

      journal={IEEE Transactions on Geoscience and Remote Sensing}, 

      title={Change Detection on Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network}, 

      year={2023},

      volume={},

      number={},

      pages={1-1},

      doi={10.1109/TGRS.2023.3241257}

    }

* Plane Text：

	

    Y. Feng, J. Jiang, H. Xu and J. Zheng, "Change Detection on Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3241257.

    

    

If you use ICIF-Net in your work please cite our paper:

* BibTex：

    @ARTICLE{9759285,

      author={Feng, Yuchao and Xu, Honghui and Jiang, Jiawei and Liu, Hao and Zheng, Jianwei},

      journal={IEEE Transactions on Geoscience and Remote Sensing}, 

      title={ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection}, 

      year={2022},

      volume={60},

      number={},

      pages={1-13},

      doi={10.1109/TGRS.2022.3168331}

    }

* Plane Text：

	

    Y. Feng, H. Xu, J. Jiang, H. Liu and J. Zheng, "ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 4410213, doi: 10.1109/TGRS.2022.3168331.