@InProceedings{SotheAlScLiLiMiTo:2019:SiSpMa,
author = "Sothe, Camile and Almeida, Cl{\'a}udia Maria de and Schimalski,
Marcos Benedito and Liesenberg, Veraldo and Lima, Carla Luciane
and Miyoshi, Gabriela Takahashi and Tommaselli, Antonio Maria
Garcia",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade do
Estado de Santa Catarina (UDESC)} and {Universidade do Estado de
Santa Catarina (UDESC)} and {Universidade Estadual Paulista
(UNESP)} and {Universidade Estadual Paulista (UNESP)} and
{Universidade Estadual Paulista (UNESP)}",
title = "Single-tree species mapping using one-class classification methods
and UAV hyperspectral images",
booktitle = "Anais...",
year = "2019",
editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco
and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
pages = "343--346",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "endangered tree species, Support Vector Machine, Random Forest,
Principal Component Analysis, Minimum Noise Fraction.",
abstract = "Progress in tree species mapping with hyperspectral data usually
is limited by the multi-class classification framework, which
imposes the requirement of exhaustively defining all species
encountered in a landscape. As the research objective may be to
map only one or a few species of interest, it is necessary to
explore alternative classification methods that may be used to
more efficiently detect a single species. In this study, we used
UAV hyperspectral data to detect one endangered tree species,
Araucaria angustifolia, in a subtropical forest area comparing the
performance of two one-class classifiers (OCC): OCSVM and OCRF.
Besides the 25 spectral bands (SB), we also tested two other
datasets: one comprising the first five MNF components, and the
other one comprising the first five PCA. Both algorithms and all
the datasets reached good results, with F-score varying from 0.81
for OCRF and SB dataset, to 1 for OCSVM associated with the PCA
dataset.",
conference-location = "Santos",
conference-year = "14-17 abril 2019",
isbn = "978-85-17-00097-3",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3UAM9T8",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3UAM9T8",
targetfile = "97927.pdf",
type = "Sensoriamento remoto hiperespectral",
urlaccessdate = "27 abr. 2024"
}