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

• URL: http://arxiv.org/abs/2002.05925v2
• Date: Fri, 21 Feb 2020 09:21:35 GMT
• Title: SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data
• Authors: Onur Tasar, S L Happy, Yuliya Tarabalka, Pierre Alliez
• Abstract summary: 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.
• Score: 7.577893526158495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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