Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1

@article{Dinh2017DeepRN,
  title={Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1},
  author={Ho Tong Minh Dinh and Dino Ienco and Raffaele Gaetano and Nathalie Lalande and Emile Ndikumana and Faycal Osman and Pierre Maurel},
  journal={CoRR},
  year={2017},
  volume={abs/1708.03694}
}
Mapping winter vegetation quality coverage is a challenge problem of remote sensing. This is due to the cloud coverage in winter period, leading to use radar rather than optical images. The objective of this paper is to provide a better understanding of the capabilities of radar Sentinel-1 and deep learning concerning about mapping winter vegetation quality coverage. The analysis presented in this paper is carried out on multi-temporal Sentinel-1 data over the site of La Rochelle, France… CONTINUE READING
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