@InProceedings{BittencourtMoreSantSant:2019:EvClMo,
author = "Bittencourt, Olga Oliveira and Morelli, Fabiano and Santos
J{\'u}nior, C{\'{\i}}cero Alves dos and Santos, Rafael Duarte
Coelho dos",
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)}",
title = "Evaluating classification models in a burning areas' detection
approach",
booktitle = "Proceedings...",
year = "2019",
editor = "Misra, Sanjay and Gervasi, Osvaldo and Murgante, Beniamino and
Stankova, Elena and Korkhov, Vladimir and Torre, Carmelo and
Rocha, Ana Maria A. C. and Taniar, David and Apduhan, Bernady O.
and Tarantino, Eufemia",
organization = "International Conference on Computational Science and its
Applications",
publisher = "Springer",
keywords = "Burned areas, Classification models, Remote sensing data.",
abstract = "We present a study to improve automation and accuracy on a Woody
Savannah burned areas classification process through the use of
Machine Learning (ML) classification models. The reference method
for this is to extract polygons from images through segmentation
and identify changes in polygons extracted from images taken from
the same area but in different times through manual labeling.
However, not all differences correspond to burned areas: there are
also deforestation, change in crops, and clouds. Our objective is
to identify the changed areas caused by fire. We propose an
approach that employs polygons attributes for classification and
evaluation in order to identify changes caused by fire. This paper
presents the more relevant classifier models to the problem,
highlighting Random Forest and an Ensemble model, that achieved
better results. The developed approach is validated over a study
area in the Brazilian Woody Savannah against reference data
derived from classifications manually done by experts. The results
indicate enhancement of the methods used so far, and will
eventually be applied to more data from different areas and
biomes.",
conference-location = "Saint Petersburg, Russia",
conference-year = "01-04 July",
doi = "10.1007/978-3-030-24305-0_43",
url = "http://dx.doi.org/10.1007/978-3-030-24305-0_43",
isbn = "978-303024304-3",
issn = "03029743",
language = "en",
urlaccessdate = "29 mar. 2024"
}