@InProceedings{BendiniFonKorSanMar:2017:EvSmMe,
author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and
Korting, Thales Sehn and Sanches, Ieda Del Arco and Marujo, Rennan
de Freitas Bezerra",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Evaluation of smoothing methods on Landsat-8 EVI time series for
crop classification based on phenological parameters",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "4267--4274",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "This study aims to evaluate three different time series smoothing
methods, combined or not with filtering techniques, and their
impact on the agricultural land use classification, in a region of
the Brazilian Cerrado, using phenological parameters extracted
from Enhanced Vegetation Index (EVI) Landsat-8 image time series.
We extracted the time series from pixels located on well know
polygons delimited on agricultural lands and monitored on a field
campaign during August 2015 and August 2016. For the
classification we considered the following classes: Annual
Agriculture, Natural Forest, Perennial Agriculture, Semi-Perennial
Agriculture and Grassland. The three smoothing algorithms were
implemented through the TIMESAT software package including the:
Savitzky-Golay (SG), asymmetric Gaussian function (AG) and
double-logistic function (DL), and then the phenological
attributes were extracted. For each method the phenological
attributes were subjected to data mining using the Random Forest
(RF) algorithm. The results were evaluated by the confusion matrix
analysis, including global accuracy, producerīs accuracy and
kappa. The intra-class variability was measured by calculating the
mean standard deviation for samples within the different classes.
The best classification accuracy with the different smoothing
methods was the SG applied to the raw time series, with a global
accuracy of 86% and kappa of 0.82.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59300",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSM2PS",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM2PS",
targetfile = "59300.pdf",
type = "An{\'a}lise de s{\'e}ries temporais de imagens de
sat{\'e}lite",
urlaccessdate = "16 jun. 2024"
}