@InProceedings{OliveiraCarBueRodMar:2019:ExPaGe,
author = "Oliveira, S{\'a}vio S. T. de and Cardoso, Marcelo de C. and
Bueno, Elivelton and Rodrigues, Vagner J. S. and Martins,
Wellington S.",
affiliation = "{Universidade Federal de Goi{\'a}s (UFG)} and {Universidade
Federal de Goi{\'a}s (UFG)} and {Universidade Federal de
Goi{\'a}s (UFG)} and {Universidade Federal de Goi{\'a}s (UFG)}
and {Universidade Federal de Goi{\'a}s (UFG)}",
title = "Exploiting parallelism to generate meta-features for land use and
land cover classification with remote sensing time series",
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 = "The automatic classification of remote sensing time series has
become essential to identify the rapid and frequent changes that
the earths surface has been undergoing. This work investigates the
accuracy of land use and land cover classification with remote
sensing time series when distance based metafeatures are added to
existing features of some classifiers. The distance based
meta-features presented are generated by comparing all time series
of the region being studied to every time series patterns
previously calculated for that region. This is a very costly
operation that was made viable through the use of parallel
processing. Although expensive, this operation is advantageous
because the meta-features generated can be later used as input to
any classifier. The experimental work conducted showed promising
results when using the distance based meta-feature strategy. The
proposed strategy was able to increase from 78% to 93,8% the
classification accuracy of the KNN algorithm, and from 92,3% to
93,8% the accuracy of a state-of-art SVM-based algorithm proposed
recently. These results indicate that distance-based meta-features
allow revealing unknown data characteristics, potentially
increasing classification accuracy.",
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/3UFDG6B",
url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3UFDG6B",
targetfile = "135-146.pdf",
urlaccessdate = "26 abr. 2024"
}