MemoryAdaptNet-master:Unsupervised Domain Adaptation Semantic Segmentation of High Resolution Remote Sensing Imagery with Invariant Domain-level Context memory
Pytorch implementation of our method for cross-domain semantic segmentation of the high-resolution remote sensing imagery.
Contact: Jingru Zhu (zhujingru@csu.edu.cn)
Unsupervised Domain Adaptation Semantic Segmentation of HRS Imagery with Invariant Domain-level Context memory
Jingru Zhu, Ya Guo , Geng Sun, Lobo Yang, Min Deng and Jie Chen, Member, IEEE,
submit to IEEE Transactions on Geoscience and Remote Sensing.
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Install PyTorch from http://pytorch.org with Python 3.6 and PyTorch 1.8.0
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Clone this repo
git clone https://github.com/RS-CSU/MemoryAdaptNet-master
cd MemoryAdaptNet-master
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Download the Potsdam Dataset as the source domain, and put it in the
dataset/Potsdamfolder -
Download the Vaihingen Dataset as the target domain, and put it in the
data/Vaihingenfolder
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Download the pre-trained p2v_best_m.pth and put it in the
snapshotsfolder -
Download the pre-trained p2v_best.pth and put it in the
snapshotsfolder -
Test the model and results will be saved in the
resultsfolder
python test_p2v_v3_1.py
- Train the Potsdam(IR-R-G) to Vaihingen(IR-R-G) model
python train_p2v_v3_1.py
This code is heavily borrowed from Pytorch-AdaptSegNet and DeepLabv3Plus-Pytorch.
The model and code are available for non-commercial research purposes only.
- 07/2022: code released