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To effectively address the domain shift problem on cross-satellite images, we develop a novel UDA method based on grouped features alignment (GFA) and entropy minimization (EM) to extract domain invariant representations to improve cloud detection accuracy of cross-satellite images.

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Package pre-requisites

The code runs on Python 2/3 The following packages are required.

pip install scipy tqdm matplotlib numpy opencv-python

Dataset preparation

Download ImageNet pretrained Resnet-101(Link) and place it ./pretrained_models/

python2 train_multi_split_entropy_landsat8_adapt_zy3_layer34.py    

Validation

python evaluate_multi_split_zy3_layer34.py 

Acknowledgement

Parts of the code have been adapted from: DeepLab-Resnet-Pytorch, AdvSemiSeg, PyTorch-Encoding

Citation

@ARTICLE{9387459,  author={Guo, Jianhua and Yang, Jingyu and Yue, Huanjing and Li, Kun},  
journal={IEEE Transactions on Geoscience and Remote Sensing},   
title={Unsupervised Domain Adaptation for Cloud Detection Based on Grouped Features Alignment and Entropy Minimization},   
year={2021},  
volume={},  
number={},  
pages={1-13},  
doi={10.1109/TGRS.2021.3067513}}

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To effectively address the domain shift problem on cross-satellite images, we develop a novel UDA method based on grouped features alignment (GFA) and entropy minimization (EM) to extract domain invariant representations to improve cloud detection accuracy of cross-satellite images.

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