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https://github.com/liumency/SRCDNet
The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ".
https://github.com/liumency/SRCDNet
Last synced: about 2 months ago
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The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ".
- Host: GitHub
- URL: https://github.com/liumency/SRCDNet
- Owner: liumency
- Created: 2021-02-10T13:42:03.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-14T05:13:49.000Z (over 2 years ago)
- Last Synced: 2024-07-23T03:40:51.556Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 40 KB
- Stars: 62
- Watchers: 1
- Forks: 12
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-remote-sensing-change-detection - Liu M, Shi Q, Marinoni A, et al. Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
README
## SRCDNet
The pytorch implementation for "[Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions](https://ieeexplore.ieee.org/document/9472869) " on IEEE Transactions on Geoscience and Remote Sensing.
The **SRCDNet** is designed to learn and predict change maps from bi-temporal images with different resolutions, which can be neatly turned into a **CDNet** and applied to images without any resolution difference.
## Requirements
- Python 3.6
- Pytorch 1.2.0
## Datasets
- Download the [BCDD Dataset](http://gpcv.whu.edu.cn/data/building_dataset.html)
- Download the [CDD Dataset](https://drive.google.com/file/d/1GX656JqqOyBi_Ef0w65kDGVto-nHrNs9/edit)
Since the intial BCDD and CDD dataset only contains bi-temporal images with the same resolution, in our experiment, images of the latter phase are down-samplinged by 4 and 8 times to simulate resolution difference of 4 and 8 times (4x and 8x), respectively.
The data folder is structured as follows:
```
├── data/
│ ├── CDD/ # CDD dataset
| | ├── train/ # traning set
| | | ├── time1/ #images of time t1
| | | ├── time2/ #images of time t2
| | | ├── time2_lr/ #lower resolution images of time t2
| | | | ├── X4.00/ #4 times resolution difference
| | | | ├── X8.00/ #8 times resolution difference
| | | ├── label/ #ground truth
| | ├── val/ # validation set, have the same structure of the training set
│ ├── BCDD/ # BCDD dataset, have the same structure of the CDD dataset
│ └──
└── epochs/ # path to save the model
│ ├── CD/
│ ├── SR/
│ └──
...
```
## Train Examples
- Train **SRCDNet** on **CDD** with **4x** resolution difference
```
python train_srcd.py --scale 4
--hr1_train '../data/CDD/train/time1'
--lr2_train '../data/CDD/train/time2_lr/X4.00'
--hr2_train '../data/CDD/train/time2'
--lab_train '../data/CDD/train/label'
--hr1_val '../data/CDD/val/time1'
--lr2_val '../data/CDD/val/time2_lr/X4.00'
--hr2_val '../data/CDD/val/time2'
--lab_val '../data/CDD/val/label'
--model_dir 'epochs/X4.00/CD/'
--sr_dir 'epochs/X4.00/SR/'
--sta_dir 'statistics/CDD_4x.csv'
```
***Note** that more optional arguments could be found and retified in **configures.py**, including: num_epochs, gpu_id, batchsize, lr, etc.
- Train **SRCDNet** on **BCDD** with **8x** resolution difference
```
python train_srcd.py --scale 8
--hr1_train '../data/BCDD/train/time1'
--lr2_train '../data/BCDD/train/time2_lr/X4.00'
--hr2_train '../data/BCDD/train/time2'
--lab_train '../data/BCDD/train/label'
--hr1_val '../data/BCDD/val/time1'
--lr2_val '../data/BCDD/val/time2_lr/X4.00'
--hr2_val '../data/BCDD/val/time2'
--lab_val '../data/BCDD/val/label'
--model_dir 'epochs/X8.00/CD/'
--sr_dir 'epochs/X8.00/SR/'
--sta_dir 'statistics/BCDD_8x.csv'
```
- Train **CDNet** on **CDD** with **no** resolution difference
```
python train_cd.py
--hr1_train '../data/BCDD/train/time1'
--hr2_train '../data/BCDD/train/time2'
--lab_train '../data/BCDD/train/label'
--hr1_val '../data/BCDD/val/time1'
--hr2_val '../data/BCDD/val/time2'
--lab_val '../data/BCDD/val/label'
--model_dir 'epochs/X0.00/CD/'
--sta_dir 'statistics/CDD_0x.csv'
```
## Citation
Please cite our paper if you use this code in your work:
```
@ARTICLE{liu2021super,
author={Liu, Mengxi and Shi, Qian and Marinoni, Andrea and He, Da and Liu, Xiaoping and Zhang, Liangpei},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Super-Resolution-Based Change Detection Network With Stacked Attention Module for Images With Different Resolutions},
year={2021},
volume={},
number={},
pages={1-18},
doi={10.1109/TGRS.2021.3091758}}
```
## Acknowledgment
This code is heavily borrowed from the [SRGAN](https://github.com/leftthomas/SRGAN) and [STANet](https://github.com/justchenhao/STANet).