@InProceedings{GlerianiFerrSoar:2013:CoMoLi,
author = "Gleriani, Jos{\'e} Marinaldo and Ferreira, Pedro Henrique Silva
and Soares, Vicente Paulo",
title = "Compara{\c{c}}{\~a}o de Modelo Linear de Mistura Espectral
(MLME) e Modelo N{\~a}o Linear de Mistura Espectral (MNLME)
aplicados {\`a} dados TM/Landsat-5 com dados subpixel do sensor
RapidEye",
booktitle = "Anais...",
year = "2013",
editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio
Soares",
pages = "1298--1305",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Linear Spectral Mixture Models (MLME) have been widely used for
equating the proportion of components within a mixed pixel.
However, some authors have investigated using nonlinear Spectral
Mixture (MNLME) in modeling these components. In this study, the
MLME was compared with MNLME which run through a supervised MLP
(Multilayer Perceptrons) network where TM / Landsat (30m) data
were used as input layer. Data used as output layer included the
proportion of the components obtained from a Rapideye scene (5m)
considering two situations: the proportion of components (shade,
vegetation and sand) after MaxVer classification and the
proportion of components after application of the MLME in the
Rapideye image. The choice of the best model was based on the
total mean absolute error, with the MLME having provided a better
fit of the proportions of mixed pixel.",
conference-location = "Foz do Igua{\c{c}}u",
conference-year = "13-18 abr. 2013",
isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
label = "369",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "3ERPFQRTRW34M/3E7GCSM",
url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GCSM",
targetfile = "p0369.pdf",
type = "An{\'a}lise e Aplica{\c{c}}{\~a}o de Dados de Alta e Baixa
Resolu{\c{c}}{\~a}o Espacial",
urlaccessdate = "09 maio 2024"
}