Awesome
SRCDNet
The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions " 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
- Download the CDD Dataset
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}}