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Type: Artigo de periódico
Title: Nature-Inspired Framework for Hyperspectral Band Selection
Author: Nakamura, RYM
Fonseca, LMG
dos Santos, JA
Torres, RD
Yang, XS
Papa, JP
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.
Subject: Evolutionary computation
heuristic algorithms
hyperspectral imaging
image classification
pattern recognition
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Citation: Ieee Transactions On Geoscience And Remote Sensing. Ieee-inst Electrical Electronics Engineers Inc, v. 52, n. 4, n. 2126, n. 2137, 2014.
Rights: fechado
Identifier DOI: 10.1109/TGRS.2013.2258351
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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