Résumé
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, during the campaign in December 2016. This
dataset were processed in order to produce an intensity radar data stack from
October 2016 to February 2017. Two deep Recurrent Neural Network (RNN) based
classifier methods were employed. We found that the results of RNNs clearly
outperformed the classical machine learning approaches (Support Vector Machine
and Random Forest). This study confirms that the time series radar Sentinel-1
and RNNs could be exploited for winter vegetation quality cover mapping.