swjtulinxi / ChangeFormer

A Transformer-Based Siamese Network for Change Detection

ChangeFormer: A Transformer-Based Siamese Network for Change Detection (Under review at IGARSS-2022)

Wele Gedara Chaminda Bandara, Vishal M. Patel

Here, we provide the pytorch implementation of the paper: A Transformer-Based Siamese Network for Change Detection.

For more information, please see our paper at arxiv.

Requirements

Python 3.8.0
pytorch 1.10.1
torchvision 0.11.2
einops  0.3.2

Please see requirements.txt for all the other requirements.

Installation

Clone this repo:

git clone https://github.com/wgcban/ChangeFormer.git
cd ChangeFormer

Quick Start on LEVIR dataset

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

Firstly, you can download our ChangeFormerV6 pretrained model——by DropBox. After downloaded the pretrained model, you can put it in checkpoints/ChangeFormer_LEVIR/.

Then, run a demo to get started as follows:

python demo_LEVIR.py

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

Quick Start on DSFIN dataset

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

Firstly, you can download our ChangeFormerV6 pretrained model——by DropBox. After downloaded the pretrained model, you can put it in checkpoints/ChangeFormer_LEVIR/.

Then, run a demo to get started as follows:

python demo_DSFIN.py

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

Train on LEVIR-CD

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

The detailed script file run_ChangeFormer_LEVIR.sh is as follows:

#!/usr/bin/env bash

#GPUs
gpus=0

#Set paths
checkpoint_root=/media/lidan/ssd2/ChangeFormer/checkpoints
vis_root=/media/lidan/ssd2/ChangeFormer/vis
data_name=LEVIR


img_size=256    
batch_size=16   
lr=0.0001         
max_epochs=200
embed_dim=256

net_G=ChangeFormerV6        #ChangeFormerV6 is the finalized verion

lr_policy=linear
optimizer=adamw                 #Choices: sgd (set lr to 0.01), adam, adamw
loss=ce                         #Choices: ce, fl (Focal Loss), miou
multi_scale_train=True
multi_scale_infer=False
shuffle_AB=False

#Initializing from pretrained weights
pretrain=/media/lidan/ssd2/ChangeFormer/pretrained_segformer/segformer.b2.512x512.ade.160k.pth

#Train and Validation splits
split=train         #trainval
split_val=test      #test
project_name=CD_${net_G}_${data_name}_b${batch_size}_lr${lr}_${optimizer}_${split}_${split_val}_${max_epochs}_${lr_policy}_${loss}_multi_train_${multi_scale_train}_multi_infer_${multi_scale_infer}_shuffle_AB_${shuffle_AB}_embed_dim_${embed_dim}

CUDA_VISIBLE_DEVICES=1 python main_cd.py --img_size ${img_size} --loss ${loss} --checkpoint_root ${checkpoint_root} --vis_root ${vis_root} --lr_policy ${lr_policy} --optimizer ${optimizer} --pretrain ${pretrain} --split ${split} --split_val ${split_val} --net_G ${net_G} --multi_scale_train ${multi_scale_train} --multi_scale_infer ${multi_scale_infer} --gpu_ids ${gpus} --max_epochs ${max_epochs} --project_name ${project_name} --batch_size ${batch_size} --shuffle_AB ${shuffle_AB} --data_name ${data_name}  --lr ${lr} --embed_dim ${embed_dim}

Train on DSFIN-CD

Follow the similar procedure mentioned for LEVIR-CD. Use run_ChangeFormer_DSFIN.sh in scripts folder to train on DSFIN-CD.

Evaluate on LEVIR

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

The detailed script file eval_ChangeFormer_LEVIR.sh is as follows:

#!/usr/bin/env bash

gpus=0

data_name=LEVIR
net_G=ChangeFormerV6 #This is the best version
split=test
vis_root=/media/lidan/ssd2/ChangeFormer/vis
project_name=CD_ChangeFormerV6_LEVIR_b16_lr0.0001_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256
checkpoints_root=/media/lidan/ssd2/ChangeFormer/checkpoints
checkpoint_name=best_ckpt.pt
img_size=256
embed_dim=256 #Make sure to change the embedding dim (best and default = 256)

CUDA_VISIBLE_DEVICES=0 python eval_cd.py --split ${split} --net_G ${net_G} --embed_dim ${embed_dim} --img_size ${img_size} --vis_root ${vis_root} --checkpoints_root ${checkpoints_root} --checkpoint_name ${checkpoint_name} --gpu_ids ${gpus} --project_name ${project_name} --data_name ${data_name}

Evaluate on LEVIR

Follow the same evaluation procedure mentioned for LEVIR-CD. You can find the evaluation script eval_ChangeFormer_DSFIN.sh in the folder scripts. You can run the script file by sh scripts/eval_ChangeFormer_DSFIN.sh in the command environment.

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:

@misc{bandara2022transformerbased,
      title={A Transformer-Based Siamese Network for Change Detection}, 
      author={Wele Gedara Chaminda Bandara and Vishal M. Patel},
      year={2022},
      eprint={2201.01293},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

References

Appreciate the work from the following repositories:

• https://github.com/justchenhao/BIT_CD (Our ChangeFormer is implemented on the code provided in this repository)