@InProceedings{MatosakRodUehKörFon:2019:FiAlMO,
author = "Matosak, Bruno Menini and Rodrigues, Marcos Ant{\^o}nio de
Almeida and Uehara, Tatiana Dias Tardelli and K{\"o}rting, Thales
Sehn and Fonseca, Leila Maria Garcia",
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 = "Filtering algorithm for MODIS time series data",
booktitle = "Anais... do 20º Simp{\'o}sio Brasileiro de Geoinform{\'a}tica",
year = "2019",
editor = "Lisboa Filho, Jugurta and Monteiro, Antonio Miguel Vieira",
organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 20. (GEOINFO)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "geoinformatica.",
abstract = "This paper describes an easy to use and friendly Graphical User
Interface (GUI) of a smoothing tool for remote sensing time
series, focused in MODIS data. This tool is developed in Python
environment and thus uses packages, libraries, modules and
functions to retrieve and filter time series data, and display
temporal information based on user defined parameters. The GUI
allows users to choose MODIS products, different noise-removal
filters, parameters for outlier removal, and also creating
animations based on the time series, of predefined areas. Time
series processed by our system can be downloaded in the well known
CSV format, to be used in other applications.",
conference-location = "S{\~a}o Jos{\'e} dos Campos",
conference-year = "11 -13 nov. 2019",
issn = "2179-4847",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGPDW34R/3UFEGQ2",
url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3UFEGQ2",
targetfile = "304-306.pdf",
urlaccessdate = "21 maio 2024"
}