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Remote Sensing Image Change Detection with Transformers

Here, we provide the pytorch implementation of the paper: Remote Sensing Image Change Detection with Transformers.

For more ore information, please see our published paper at IEEE TGRS or arxiv.

image-20210228153142126

Requirements

Python 3.6
pytorch 1.6.0
torchvision 0.7.0
einops  0.3.0

Installation

Clone this repo:

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

Quick Start

We have some samples from the LEVIR-CD dataset in the folder samples for a quick start.

Firstly, you can download our BIT pretrained model——by baidu drive, code: 2lyz or google drive. After downloaded the pretrained model, you can put it in checkpoints/BIT_LEVIR/.

Then, run a demo to get started as follows:

python demo.py

After that, you can find the prediction results in samples/predict.

Train

You can find the training script run_cd.sh in the folder scripts. You can run the script file by sh scripts/run_cd.sh in the command environment.

The detailed script file run_cd.sh is as follows:

gpus=0
checkpoint_root=checkpoints 
data_name=LEVIR  # dataset name 

img_size=256
batch_size=8
lr=0.01
max_epochs=200  #training epochs
net_G=base_transformer_pos_s4_dd8 # model name
#base_resnet18
#base_transformer_pos_s4_dd8
#base_transformer_pos_s4_dd8_dedim8
lr_policy=linear

split=train  # training txt
split_val=val  #validation txt
project_name=CD_${net_G}_${data_name}_b${batch_size}_lr${lr}_${split}_${split_val}_${max_epochs}_${lr_policy}

python main_cd.py --img_size ${img_size} --checkpoint_root ${checkpoint_root} --lr_policy ${lr_policy} --split ${split} --split_val ${split_val} --net_G ${net_G} --gpu_ids ${gpus} --max_epochs ${max_epochs} --project_name ${project_name} --batch_size ${batch_size} --data_name ${data_name}  --lr ${lr}

Evaluate

You can find the evaluation script eval.sh in the folder scripts. You can run the script file by sh scripts/eval.sh in the command environment.

The detailed script file eval.sh is as follows:

gpus=0
data_name=LEVIR # dataset name
net_G=base_transformer_pos_s4_dd8_dedim8 # model name 
split=test # test.txt
project_name=BIT_LEVIR # the name of the subfolder in the checkpoints folder 
checkpoint_name=best_ckpt.pt # the name of evaluated model file 

python eval_cd.py --split ${split} --net_G ${net_G} --checkpoint_name ${checkpoint_name} --gpu_ids ${gpus} --project_name ${project_name} --data_name ${data_name}

Dataset Preparation

Data structure

"""
Change detection data set with pixel-level binary labels；
├─A
├─B
├─label
└─list
"""

A: images of t1 phase;

B:images of t2 phase;

label: label maps;

list: contains train.txt, val.txt and test.txt, each file records the image names (XXX.png) in the change detection dataset.

Data Download

LEVIR-CD: https://justchenhao.github.io/LEVIR/

WHU-CD: https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html

DSIFN-CD: https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset

License

Code is released for non-commercial and research purposes only. For commercial purposes, please contact the authors.

Citation

If you use this code for your research, please cite our paper:

@Article{chen2021a,
    title={Remote Sensing Image Change Detection with Transformers},
    author={Hao Chen, Zipeng Qi and Zhenwei Shi},
    year={2021},
    journal={IEEE Transactions on Geoscience and Remote Sensing},
    volume={},
    number={},
    pages={1-14},
    doi={10.1109/TGRS.2021.3095166}
}