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Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR sentinel-1

Ho Tong Minh D., Ienco D., Gaetano R., Lalande N., Ndikumana E., Osman F., Maurel P.. 2018. IEEE Geoscience and Remote Sensing Letters, 15 (3) : p. 464-468.

DOI: 10.1109/LGRS.2018.2794581

Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).

Mots-clés : télédétection; imagerie par satellite; index de végétation; cartographie de l'occupation du sol; traitement des données; radar; réseau de neurones; aquitaine; france; deep learning

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