@InProceedings{SoaresBVUNLKF:2020:PhPaSa,
author = "Soares, Anderson Reis and Bendini, Hugo do Nascimento and Vaz,
Daiane Vieira and Uehara, Tatiana Dias Tardelli and Neves, Alana
Kasahara and Lechler, Sarah 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)} and {University of Muenster} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Stmetrics: a phython package for satellite image time-series
featrue extraction",
year = "2020",
organization = "IEEE International Geoscience and Remote Sensing Symposium
(IGARSS)",
keywords = "time-series, python, multi-temporal features, remote sensing.",
abstract = "Producing reliable land use and land cover maps to support the
deployment and operation of public policies is a necessity,
especially when environmental management and economic development
are considered. To increase the accuracy of these maps, satellite
image time-series have been used, as they allow the understanding
of land cover dynamics through the time. This paper presents the
stmetrics, a python package that provides the extraction of
state-of-the-art time-series features. These features can be used
for remote sensing time-series image classification and analysis.
stmetrics aims to be an easy-to-use package. The package is
available under the GNU GPL software license, and the full source
code is available for download at:
github.com/andersonreisoares/stmetrics.",
conference-location = "Virtual Symposium",
conference-year = "26 Sept. - 02 Oct.",
language = "en",
targetfile = "soares_stmetrics.pdf",
urlaccessdate = "28 mar. 2024"
}