Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images-Reference-Cited by-同舟云学术

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Published:2019-03-05 Issue:4 Volume:5 Page:360-375
ISSN:2056-3485
Container-title:Remote Sensing in Ecology and Conservation
language:en
Short-container-title:Remote Sens Ecol Conserv

Author:

Wagner Fabien H.¹,Sanchez Alber¹,Tarabalka Yuliya²³,Lotte Rodolfo G.¹,Ferreira Matheus P.¹⁴,Aidar Marcos P. M.⁵,Gloor Emanuel⁶,Phillips Oliver L.⁶,Aragão Luiz E. O. C.¹⁷

Affiliation:

1. Remote Sensing Division National Institute for Space Research ‐ INPE São José dos Campos SP 12227‐010 Brazil

2. Inria Sophia Antipolis Cedex Sophia Antipolis 06902 France

3. Luxcarta Technology Parc d'Activité l'Argile, Lot 119b Mouans Sartoux 06370 France

4. Cartography Engineering Section Military Institute of Engineering ‐ IME Praça Gen. Tibúrcio 80 Rio de Janeiro RJ 22290‐270 Brazil

5. Department of Plant Physiology and Biochemistry Institute of Botany PB 4005 São Paulo CEP 01061‐970 Brazil

6. Ecology and Global Change School of Geography University of Leeds Leeds LS2 9JT UK

7. College of Life and Environmental Sciences University of Exeter Exeter EX4 4RJ UK

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Natural Environment Research Council

Agence Nationale de la Recherche

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Wiley

Subject

Nature and Landscape Conservation,Computers in Earth Sciences,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.111

Reference62 articles.

1. Abadi M. A.Agarwal P.Barham E.Brevdo Z.Chen C.Citro et al.2015.TensorFlow: large‐scale machine learning on heterogeneous systems. Available fromhttps://www.tensorflow.org/ software available from tensorflow.org (Accessed 11‐07‐2018).

2. Allaire J. andF.Chollet.keras: R interface to ‘Keras’.2016.https://keras.rstudio.com r package version 2.1.4 (Accessed 11‐07‐2018).

3. Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network

4. Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy

5. Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion

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