@Article{NakamuraFoSaToYaPa:2014:NaFrHy,
author = "Nakamura, Rodrigo Y. M. and Fonseca, Leila Maria Garcia and
Santos, Jefersson Alex dos and Torres, Ricardo da S. and Yang,
Xin-She and Papa, Joao Papa",
affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Universidade Estadual de
Campinas (UNICAMP)} and {Universidade Estadual de Campinas
(UNICAMP)} and {Middlesex University} and {Universidade Estadual
Paulista (UNESP)}",
title = "Nature-Inspired Framework for Hyperspectral Band Selection",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
year = "2014",
volume = "52",
number = "4",
pages = "2126--2137",
month = "Apr.",
keywords = "Evolutionary computation, heuristic algorithms, hyperspectral
imaging, image classification, pattern recognition.",
abstract = "Although hyperspectral images acquired by on-board satellites
provide information from a wide range of wavelengths in the
spectrum, the obtained information is usually highly correlated.
This paper proposes a novel framework to reduce the computation
cost for large amounts of data based on the efficiency of the
optimum-path forest (OPF) classifier and the power of
metaheuristic algorithms to solve combinatorial optimizations.
Simulations on two public data sets have shown that the proposed
framework can indeed improve the effectiveness of the OPF and
considerably reduce data storage costs.",
doi = "10.1109/TGRS.2013.2258351",
url = "http://dx.doi.org/10.1109/TGRS.2013.2258351",
issn = "0196-2892",
label = "isi 2014-05 NakamuraGaSaToYaPa:2014:NaFrHy",
language = "en",
urlaccessdate = "25 abr. 2024"
}