@InProceedings{FerreiraZorZanShiSou:2015:MaEsAr,
author = "Ferreira, Matheus Pinheiro and Zortea, Maciel and Zanotta, Daniel
Capella and Shimabukuro, Yosio Edemir and Souza Filho, Carlos
Roberto de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and {}
and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Mapeamento de esp{\'e}cies arb{\'o}reas em floresta tropical
utilizando imagens hiperespectrais",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "1224--1230",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Tree species mapping in tropical forests using remote sensing
imagery is challenging with the benefit to provide valuable
insights for ecologists and land managers. Hyperspectral data
proved to be feasible for this task, but it is still unclear how
different classification methods perform. In this work, we
evaluated Linear Discriminant Analysis (LDA), Radial Basis
Function Support Vector Machines (RBF-SVM) and Random Forest (RF)
for tree species discrimination and mapping in a tropical forest
using airborne hyperspectral data. The effects of dimensionality
reduction on classification performance were also assessed by
selecting sets of 10, 20 and 30 bands. At the pixel level, LDA
performed better than other methods (Average Accuracy (AA) =84.7%)
using all (260) spectral bands for classification. However,
RBF-SVM produced the best map of species using 30 selected bands
(AA =90.4%) in an object based approach (OBIA). OBIA increased the
AA of species mapping for all tested methods and reduced spatial
noise.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "226",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM47SC",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM47SC",
targetfile = "p0226.pdf",
type = "Sensoriamento remoto hiperespectral",
urlaccessdate = "02 maio 2024"
}