https://github.com/justchenhao/STANet
official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection
https://github.com/justchenhao/STANet
attention-mechanism change-detection contrastive-loss dataset remote-sensing siamese-neural-network
Last synced: about 1 year ago
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official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection
- Host: GitHub
- URL: https://github.com/justchenhao/STANet
- Owner: justchenhao
- License: bsd-2-clause
- Created: 2020-05-22T13:34:25.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-11T13:56:15.000Z (about 3 years ago)
- Last Synced: 2024-02-17T14:35:51.992Z (over 2 years ago)
- Topics: attention-mechanism, change-detection, contrastive-loss, dataset, remote-sensing, siamese-neural-network
- Language: Python
- Size: 1.88 MB
- Stars: 377
- Watchers: 10
- Forks: 107
- Open Issues: 55
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
## Change log
20230311：
- We have supplemented geospatial information (e.g., latitude and longitude coordinates) for each sample in LEVIR_CD. Specifically, we provide a `geojson` file and a `json` file, both of which contains the correspondence of the sample name and the geospatial location. Files and more information can be seen in the `LEVIR_CD_spatial_info` folder.
20210112:
- add the pretraining weight of PAM. [baidupan link](https://pan.baidu.com/s/1O1kg7JWunqd87ajtVMM6pg), code: 2rja
20201105：
- add a demo for quick start.
- add more dataset loader modes.
- enhance the image augmentation module (crop and rotation).
20200601：
- first commit
## Prerequisites
- windows or Linux
- Python 3.6+
- CPU or NVIDIA GPU
- CUDA 9.0+
- PyTorch > 1.0
- visdom
## Installation
Clone this repo:
```bash
git clone https://github.com/justchenhao/STANet
cd STANet
```
Install [PyTorch](http://pytorch.org/) 1.0+ and other dependencies (e.g., torchvision, [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate))
## Quick Start
You can run a demo to get started.
```bash
python demo.py
```
The input samples are in `samples`. After successfully run this script, you can find the predicted results in `samples/output`.
## 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](http://localhost:8097/).
```bash
python -m visdom.server
```
### train with our base method
Run the following script:
```bash
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
```bash
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
```bash
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:
```python
if __name__ == '__main__':
opt = TestOptions().parse() # get training options
opt = make_val_opt(opt)
opt.phase = 'test'
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.
## Using other dataset mode
### List mode
```bash
list=train
lr=0.001
dataset_mode=list
dataroot=path-to-dataroot
name=project_name
python ./train.py --num_threads 4 --display_id 0 --dataroot ${dataroot} --val_dataroot ${dataroot} --save_epoch_freq 1 --niter 100 --angle 15 --niter_decay 100 --display_env FAp0 --SA_mode PAM --name $name --lr $lr --model CDFA --batch_size 4 --dataset_mode $dataset_mode --val_dataset_mode $dataset_mode --split $list --load_size 256 --crop_size 256 --preprocess resize_rotate_and_crop
```
In this case, the data structure should be the following:
```
"""
data structure
-dataroot
├─A
├─train1.png
...
├─B
├─train1.png
...
├─label
├─train1.png
...
└─list
├─val.txt
├─test.txt
└─train.txt
# In list/train.txt, each low writes the filename of each sample,
# for example:
list/train.txt
train1.png
train2.png
...
"""
```
### Concat mode for loading multiple datasets (each default mode is List)
```bash
list=train
lr=0.001
dataset_type=CD_data1,CD_data2,...,
val_dataset_type=CD_data
dataset_mode=concat
name=project_name
python ./train.py --num_threads 4 --display_id 0 --dataset_type $dataset_type --val_dataset_type $val_dataset_type --save_epoch_freq 1 --niter 100 --angle 15 --niter_decay 100 --display_env FAp0 --SA_mode PAM --name $name --lr $lr --model CDFA --batch_size 4 --dataset_mode $dataset_mode --val_dataset_mode $dataset_mode --split $list --load_size 256 --crop_size 256 --preprocess resize_rotate_and_crop
```
Note, in this case, you should modify the `get_dataset_info` in `data/data_config.py` to add the corresponding ` dataset_name` and `dataroot` in it.
```python
if dataset_type == 'LEVIR_CD':
root = 'path-to-LEVIR_CD-dataroot'
elif ...
# add more dataset ...
```
## 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{rs12101662,
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](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).