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GIScience & Remote Sensing Volume 57, 2020 - Issue 3
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Research Article

Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data

C. Sothea Division of Remote Sensing, National Institute for Space Research (INPE), São José dos Campos, BrazilCorrespondence[email protected]
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C. M. De Almeidaa Division of Remote Sensing, National Institute for Space Research (INPE), São José dos Campos, BrazilView further author information
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M. B. Schimalskib Department of Forest Engineering, Santa Catarina State University (UDESC), Lages, BrazilView further author information
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L. E. C. La Rosac Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC), Rio de Janeiro, BrazilView further author information
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J. D. B. Castroc Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC), Rio de Janeiro, BrazilView further author information
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R. Q. Feitosac Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC), Rio de Janeiro, BrazilView further author information
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M. Dalponted Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, ItalyView further author information
,
C. L. Limae Department of Geography, Santa Catarina State University (UDESC), Florianópolis, BrazilView further author information
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V. Liesenbergb Department of Forest Engineering, Santa Catarina State University (UDESC), Lages, BrazilView further author information
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G. T. Miyoshif Department of Cartography, São Paulo State University (UNESP), Presidente Prudente, BrazilView further author information
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A. M. G. Tommasellif Department of Cartography, São Paulo State University (UNESP), Presidente Prudente, BrazilORCID Iconhttps://orcid.org/0000-0003-0483-1103View further author information
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Pages 369-394 | Received 22 Sep 2019, Accepted 02 Jan 2020, Published online: 12 Jan 2020

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