Shadow Detection in High-Resolution Multispectral Satellite Imagery Using Generative Adversarial Networks

@article{Morales2018ShadowDI,
  title={Shadow Detection in High-Resolution Multispectral Satellite Imagery Using Generative Adversarial Networks},
  author={Giorgio Morales and Daniel Arteaga and Samuel G. Huam{\'a}n and Joel Telles and Walther Palomino},
  journal={2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)},
  year={2018},
  pages={1-4}
}
Detecting shadows in high-resolution satellite images is a challenging task due to the fact that shadows can easily be mistaken for low reflectance soil or water and that such images have limited spectral bands. In this work, we propose a semantic level shadow segmentation by using generative adversarial networks and created a dataset of pre-processed images for training, validation and test. In this way, we trained a generator network that produces shadow masks with condition on a satellite… CONTINUE READING

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