CICNet

Papers

• Compact Intertemporal Coupling Network for Remote Sensing Change Detection (ICME 2023)

1. Environment setup

This code has been tested on on the workstation with Intel Xeon CPU E5-2690 v4 cores and two GPUs of NVIDIA TITAN V with a single 12G of video memory, Python 3.6, pytorch 1.9, CUDA 10.0, cuDNN 7.6. Please install related libraries before running this code:

pip install -r requirements.txt

2. Download the datesets:

• LEVIR-CD: LEVIR-CD
• WHU-CD: WHU-CD
• GZ-CD: GZ-CD
• SYSU-CD: SYSU-CD

and put them into datasets directory. The directory should be organized as follows:

"""
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.

And change the root_dir in the data_config.py file.

3. Download the models (loading models):

Download the pretrained 'ResNet18' model and put it into pretrained directory.

And the pretrained models of CICNet on four CD datasets are as follows:

• models code: loph

and put them into checkpoints directory.

4. 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=CICNet # model name
lr_policy=linear

split=train  # training txt
split_val=val  # validation txt
project_name=${net_G}-${data_name}

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}

5. 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=CICNet # model name 
split=test # test.txt
project_name=${net_G}-${data_name} # 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}

6. Results

License

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

• Plane Text：

  Y. Feng, H. Xu, J. Jiang and J. Zheng, "Compact Intertemporal Coupling Network for Remote Sensing Change Detection," in IEEE International Conference on Multimedia & Expo (ICME 2023).

  Y. Feng, J. Jiang, H. Xu and J. Zheng, "Change Detection on Remote Sensing Images using Dual-branch Multi-level Inter-temporal Network," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3241257.

  Y. Feng, H. Xu, J. Jiang, H. Liu and J. Zheng, "ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 4410213, doi: 10.1109/TGRS.2022.3168331.