%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Iovan, Corina
%A Kulbicki, M.
%A Mermet, E.
%T Deep convolutional neural network for mangrove mapping
%B IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
%C Piscataway
%D 2020
%L fdi:010084424
%G ENG
%I IEEE
%@ 978-1-7281-6375-8
%K PACIFIQUE ; FIDJI
%M ISI:000664335302006
%P 1969-1972
%R 10.1109/IGARSS39084.2020.9323802
%U https://www.documentation.ird.fr/hor/fdi:010084424
%> https://www.documentation.ird.fr/intranet/publi/2023-01/010084424.pdf
%W Horizon (IRD)
%X Updated information on the spatial distribution of mangrove forests is of high importance for management plans. Yet, access to mangrove distribution maps is limited, even-though remote sensing data is currently freely available and deep learning algorithms score high performances in automatic classification tasks. The methodologies developed in this paper are based on a deep convolutional neural network and have been tested on WorldView 2 and Sentinel-2 images. The obtained results are highly satisfactory and open perspectives for automatically mapping mangrove distribution over large areas.
%B IGARSS.International Geoscience and Remote Sensing Symposium
%8 2020/09/26-2020/10/02
%$ 126 ; 128 ; 082