SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data

14 Feb 2020  ·  Onur Tasar, S. L. Happy, Yuliya Tarabalka, Pierre Alliez ·

Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data. To address this issue, we propose a new data augmentation approach that transfers the style of test data to training data using generative adversarial networks. Our semantic segmentation framework consists in first training a U-net from the real training data and then fine-tuning it on the test stylized fake training data generated by the proposed approach. Our experimental results prove that our framework outperforms the existing domain adaptation methods.

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