Regularization of Building Boundaries in Satellite Images Using Adversarial and Regularized Losses

@article{Zorzi2019RegularizationOB,
  title={Regularization of Building Boundaries in Satellite Images Using Adversarial and Regularized Losses},
  author={Stefano Zorzi and Friedrich Fraundorfer},
  journal={IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},
  year={2019},
  pages={5140-5143}
}
  • Stefano ZorziF. Fraundorfer
  • Published 1 July 2019
  • Computer Science
  • IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
In this paper we present a method for building boundary refinement and regularization in satellite images using a fully convolutional neural network trained with a combination of adversarial and regularized losses. Compared to a pure Mask R-CNN model, the overall algorithm can achieve equivalent performance in terms of accuracy and completeness. However, unlike Mask R-CNN that produces irregular footprints, our framework generates regularized and visually pleasing building boundaries which are… 
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