Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR sentinel-1
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).
| Main Authors: | , , , , , , |
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | U30 - Méthodes de recherche, F40 - Écologie végétale, U10 - Informatique, mathématiques et statistiques, télédétection, imagerie par satellite, indice de végétation, cartographie de l'occupation du sol, traitement des données, radar, réseau de neurones, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_36761, http://aims.fao.org/aos/agrovoc/c_9000171, http://aims.fao.org/aos/agrovoc/c_9000094, http://aims.fao.org/aos/agrovoc/c_10289, http://aims.fao.org/aos/agrovoc/c_24071, http://aims.fao.org/aos/agrovoc/c_37467, http://aims.fao.org/aos/agrovoc/c_563, http://aims.fao.org/aos/agrovoc/c_3081, |
| Online Access: | http://agritrop.cirad.fr/592815/ http://agritrop.cirad.fr/592815/1/Minh2018.pdf |
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