PCLUDA: A Pseudo-Label Consistency Learning- Based Unsupervised Domain Adaptation Method for Cross-Domain Optical Remote Sensing Image Retrieval

This repository is the official implementation of PCLUDA: A Pseudo-Label Consistency Learning-Based Unsupervised Domain Adaptation Method for Cross-Domain Optical Remote Sensing Image Retrieval (IEEE TGRS 2023).

Authors: Dongyang Hou, Siyuan Wang, Xueqing Tian and Huaqiao Xing

Requirements

• This code is written for python3
• pytorch >= 1.7.0
• torchvision
• numpy, prettytable, tqdm, scikit-learn, matplotlib, argparse, h5py

Data Preparing

Download dataset from the following link (code is chk8):

BaiduYun

Training and Evaluating

The pipeline for training with PCLUDA is the following:

1. Train the model. For example, to run an experiment for UCM_LandUse dataset (source domain) and AID dataset (target domain), run:

• python pcluda.py /your_path/PCLUDA_dataset/ -s UCMD -t AID -a resnet50 --epochs 30 --seed 1 --log logs/pcluda/ucmd_aid

2. Evaluate the classification performance of the model.

• python pcluda.py /your_path/PCLUDA_dataset/ -s UCMD -t AID -a resnet50 --epochs 30 --seed 1 --log logs/pcluda/ucmd_aid --phase test

3. Test the remote sensing image retrieval performance. The retrieval code is under continuous optimization.

• python pcluda.py /your_path/PCLUDA_dataset/ -s UCMD -t AID -a resnet50 --epochs 30 --seed 1 --log logs/pcluda/ucmd_aid --phase retrieval

Acknowledgment

This code is heavily borrowed from Transfer-Learning-Library

Citation

If you find our work useful in your research, please consider citing our paper:

@article{hou2023pcluda,
  title={PCLUDA: A Pseudo-label Consistency Learning-Based Unsupervised Domain Adaptation Method for Cross-domain Optical Remote Sensing Image Retrieval},
  author={Hou, Dongyang and Wang, Siyuan and Tian, Xueqing and Xing, Huaqiao},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2023},
  publisher={IEEE}
}

Contact

Please contact [email protected] if you have any question on the codes.