@incollection{fdi:010084424,
  title = {{D}eep convolutional neural network for mangrove mapping},
  author = {{I}ovan, {C}orina and {K}ulbicki, {M}. and {M}ermet, {E}.},
  editor = {},
  language = {{ENG}},
  abstract = {{U}pdated information on the spatial distribution of mangrove forests is of high importance for management plans. {Y}et, 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. {T}he methodologies developed in this paper are based on a deep convolutional neural network and have been tested on {W}orld{V}iew 2 and {S}entinel-2 images. {T}he obtained results are highly satisfactory and open perspectives for automatically mapping mangrove distribution over large areas.},
  keywords = {{PACIFIQUE} ; {FIDJI}},
  booktitle = {{IGARSS} 2020 - 2020 {IEEE} {I}nternational {G}eoscience and {R}emote {S}ensing {S}ymposium},
  numero = {},
  pages = {1969--1972},
  address = {{P}iscataway},
  publisher = {{IEEE}},
  series = {},
  year = {2020},
  DOI = {10.1109/{IGARSS}39084.2020.9323802},
  ISBN = {978-1-7281-6375-8},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084424},
}