@InProceedings{BendiniFoKöMaSaAr:2016:AsMuAp,
author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and
K{\"o}rting, Thales Sehn and Marujo, Rennan de Freitas Bezerra
and Sanches, Ieda Del'Arco and Arcanjo, Jeferson de Souza",
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)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Assessment of a multi-sensor approach for noise removal on
Landsat-8 OLI time series using CBERS-4 MUX data to improve crop
classification based on phenological features",
booktitle = "Anais...",
year = "2016",
organization = "Brazilian Symposium on GeoInformatics, 17. (GEOINFO)",
abstract = "We investigated a method for noise removal on Landsat-8 OLI
timeseries using CBERS-4 MUX data to improve crop classification.
An algorithm was built to look to the nearest MUX image for each
Landsat image, based on user defined time span. The algorithm
checks for cloud contaminated pixels on the Landsat time series
using Fmask and replaces them with CBERS-4 MUX to build the
integrated time series (Landsat-8 OLI+CBERS-4 MUX). Phenological
features were extracted from the time series samples for each
method (EVI and NDVI original time series and multi-sensor time
series, with and without filtering) and subjected to data mining
using Random Forest classification. In general, we observed a
slight increase in the classification accuracy when using the
proposed method. The best result was observed with the EVI
integrated filtered time series (78%), followed by the filtered
Landsat EVI time series (76%).",
conference-location = "Campos do Jord{\~a}o, SP",
conference-year = "27-30 nov. 2016",
language = "en",
ibi = "8JMKD3MGP3W34P/3N2UANP",
url = "http://urlib.net/ibi/8JMKD3MGP3W34P/3N2UANP",
targetfile = "bendini_assessment.pdf",
urlaccessdate = "02 maio 2024"
}