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Unsupervised Single-Scene Semantic Segmentation for Earth Observation
Instructions for Vaihingen dataset
Code can be run using following two commands:
For training the model on single scene (after running this command the model will be saved to ./trainedModels/)
$ python trainVaihingen.py --manualSeed 85 --nFeaturesIntermediateLayers 64 --nFeaturesFinalLayer 8 --numTrainingEpochs 2 --modelName Model5ChannelInitialToMiddleLayersDifferent
For obtaining segmentation maps from the test scenes (after running this command the model will be saved to ./results/vaihingen/)
$ python obtainSegMapVaihingen.py
Different manual seeds can be set in the above command.
Please download the Vaihingen dataset from appropriate source and save it in the directory (w.r.t the code) "../data/Vaihingen/"
Citation
If you find this code or the multi-season dataset useful, please consider citing:
@article{saha2022unsupervised,
title={Unsupervised Single-Scene Semantic Segmentation for Earth Observation},
author={Saha, Sudipan and Shahzad, Muhammad and Mou, Lichao and Song, Qian and Zhu, Xiao Xiang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2022},
publisher={IEEE}
}