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, 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:

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

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

Quick Start

You can run a demo to get started.

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.

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 = '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

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)

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.

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.

justchenhao/STANet

justchenhao

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