- Compact Intertemporal Coupling Network for Remote Sensing Change Detection (ICME 2023)
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
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.
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.
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 ${lr}You can find the evaluation script eval.sh in the folder scripts. You can run the script file by sh scripts/eval.sh in the command environment.
The detailed script file eval.sh is as follows:
gpus=0
data_name=LEVIR # dataset name
net_G=CICNet # model name
split=test # test.txt
project_name=${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}
Code is released for non-commercial and research purposes only. For commercial purposes, please contact the authors.
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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.