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STANet for remote sensing image change detection

It is the implementation of the paper: A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection.

Here, we provide the pytorch implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection.

image-20200601213320103

Prerequisites

  • windows or Linux
  • Python 3.6+
  • CPU or NVIDIA GPU
  • CUDA 9.0+
  • PyTorch > 1.0
  • visdom

Installation

Clone this repo:

git clone https://github.com/justchenhao/STANet
cd STANet

Install PyTorch 1.0+ and other dependencies (e.g., torchvision, visdom and dominate)

Prepare Datasets

download the change detection dataset

You could download the LEVIR-CD at https://justchenhao.github.io/LEVIR/;

The path list in the downloaded folder is as follows:

path to LEVIR-CD:
                ├─train
                │  ├─A
                │  ├─B
                │  ├─label
                ├─val
                │  ├─A
                │  ├─B
                │  ├─label
                ├─test
                │  ├─A
                │  ├─B
                │  ├─label

where A contains images of pre-phase, B contains images of post-phase, and label contains label maps.

cut bitemporal image pairs

The original image in LEVIR-CD has a size of 1024 * 1024, which will consume too much memory when training. Therefore, we can cut the origin images into smaller patches (e.g., 256 * 256, or 512 * 512). In our paper, we cut the original image into patches of 256 * 256 size without overlapping.

Make sure that the corresponding patch samples in the A, B, and label subfolders have the same name.

Train

Monitor training status

To view training results and loss plots, run this script and click the URL http://localhost:8097.

python -m visdom.server

train with our base method

Run the following script:

python ./train.py --save_epoch_freq 1 --angle 15 --dataroot path-to-LEVIR-CD-train --val_dataroot path-to-LEVIR-CD-val --name LEVIR-CDF0 --lr 0.001 --model CDF0 --batch_size 8 --load_size 256 --crop_size 256 --preprocess rotate_and_crop

Once finished, you could find the best model and the log files in the project folder.

train with Basic spatial-temporal Attention Module (BAM) method

python ./train.py --save_epoch_freq 1 --angle 15 --dataroot path-to-LEVIR-CD-train --val_dataroot path-to-LEVIR-CD-val --name LEVIR-CDFA0 --lr 0.001 --model CDFA --SA_mode BAM --batch_size 8 --load_size 256 --crop_size 256 --preprocess rotate_and_crop

train with Pyramid spatial-temporal Attention Module (PAM) method

python ./train.py --save_epoch_freq 1 --angle 15 --dataroot path-to-LEVIR-CD-train --val_dataroot path-to-LEVIR-CD-val --name LEVIR-CDFAp0 --lr 0.001 --model --SA_mode PAM CDFA --batch_size 8 --load_size 256 --crop_size 256 --preprocess rotate_and_crop

Test

You could edit the file val.py, for example:

if __name__ == '__main__':
    opt = TestOptions().parse()   # get training options
    opt = make_val_opt(opt)
    opt.phase = 'val'
    opt.dataroot = 'path-to-LEVIR-CD-test' # data root 
    opt.dataset_mode = 'changedetection'
    opt.n_class = 2
    opt.SA_mode = 'PAM' # BAM | PAM 
    opt.arch = 'mynet3'
    opt.model = 'CDFA' # model type
    opt.name = 'LEVIR-CDFAp0' # project name
    opt.results_dir = './results/' # save predicted images 
    opt.epoch = 'best-epoch-in-val' # which epoch to test
    opt.num_test = np.inf
    val(opt)

then run the script: python val.py. Once finished, you can find the prediction log file in the project directory and predicted image files in the result directory.

Pre-train Module

Here, we provide the PAM pre-train model: baidu-download-link, code:l3o7, google-download-link.

Other TIPS

For more Training/Testing guides, you could see the option files in the ./options/ folder.

Citation

If you use this code for your research, please cite our papers.

@Article{chen2020,
AUTHOR = {Chen, Hao and Shi, Zhenwei},
TITLE = {A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection},
JOURNAL = {Remote Sensing},
VOLUME = {12},
YEAR = {2020},
NUMBER = {10},
ARTICLE-NUMBER = {1662},
URL = {https://www.mdpi.com/2072-4292/12/10/1662},
ISSN = {2072-4292},
DOI = {10.3390/rs12101662}
}

Acknowledgments

Our code is inspired by pytorch-CycleGAN.