The Pytorch implementation for Deep Multiscale Siamese Network with Parallel Convolutional Structure and Self-Attention for Change Detection
Qingle Guo, Junping Zhang, Shengyu Zhu, Chongxiao Zhong and Ye Zhang
[04 Dec. 2021] Release the first version of the MSPSNet
Dataset Download
LEVIR-CD：https://justchenhao.github.io/LEVIR/
SYSU：https://drive.google.com/drive/folders/1ALb8rzw9zEMSxwNTvIrIaA83zjjs04CE
Note: Please crop the LEVIR dataset to a slice of 256×256 before training with it.
Dataset Path Setteing
LEVIR_CD or SYSU
|—train
| |—A
| |—B
| |—OUT
|—val
| |—A
| |—B
| |—OUT
|—test
| |—A
| |—B
| |—OUT
Where A contains images of first temporal image, B contains images of second temporal images, and OUT contains groundtruth maps.
Traing and test Process
python train.py
python test.py
Revised parameters
You can revised related parameters in the "metadata.json" file.
Requirement
-Pytorch 1.8.0
-torchvision 0.9.0
-python 3.8
-opencv-python 4.5.3.56
-tensorboardx 2.4
-Cuda 11.3.1
-Cudnn 11.3
Citation
If you use this code for your research, please cite our papers.
@Article{
AUTHOR = {Qingle, Guo, Junping Zhang, Shengyu Zhu, Chongxiao Zhong and Ye Zhang},
TITLE = {Deep Multiscale Siamese Network with Parallel Convolutional Structure and Self-Attention for Change Detection },
JOURNAL = {IEEE Transactions on Geoscience and Remote Sensing},
VOLUME = {},
YEAR = {2022},
ISSN = {1558-0644},
}
Acknowledgments
Our code is inspired and revised by [pytorch-SNUNet], Thanks Sheng Fang for his great work!!
Reference
[1] H. Chen and Z. Shi, “A Spatial-temporal Attention-based Method and a New Dataset for Remote Sensing Image Change Detection,” Remote Sens., vol. 12, no. 10, pp. 1662, May 2020.
[2] S. Fang, K. Li, J. Shao and Z. Li, “SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images,” IEEE Geosci.and Remote Sens. Lett., 2021.
[3] Q. Shi, M. Liu, S. Li, X. Liu, F. Wang, L. Zhang, "A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection," IEEE Trans. Geosci. Remote Sens., 2021