Using Convolutional Network to Identify Tree Species Related to Forest Disturbance in a Neotropical Forest with very high resolution multispectral images

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    W[2018AGUFM.B33N2861W]
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ADS bibcode: 2018AGUFM.B33N2861W
year: 2018
Listed Authors: Wagner, F. H.; Sanchez, A.; Tarabalka, Y.; Lotte, R. G.; Ferreira, M. P.; Aidar, M. P.; Gloor, M.; Phillips, O. L.; Aragão, L. E.
Listed Institutions: INPE National Institute for Space Research, Sao Jose dos Campos, Brazil; DSR, INPE National Institute for Space Research, Sao Jose dos Campos, Brazil; INRIA, Sophia Antipolis, France; DSR, INPE National Institute for Space Research, Sao Jose dos Campos, Brazil; DSR, INPE National Institute for Space Research, Sao Jose dos Campos, Brazil; Department of Plant Physiology and Biochemistry, Institute of Botany, São Paulo, Brazil; School of Geography, University of Leeds, Leeds, United Kingdom; School of Geography, University of Leeds, Leeds, United Kingdom; INPE National Institute for Space Research, Sao Jose dos Campos, Brazil

Using Convolutional Network to Identify Tree Species Related to Forest Disturbance in a Neotropical Forest with very high resolution multispectral images

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