Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/63448
Type: Artigo
Title: Nature-inspired framework for hyperspectral band selection
Author: Nakamura, Rodrigo Y. M.
Fonseca, Leila Maria Garcia
Santos, Jefersson Alex dos
Torres, Ricardo da S.
Xin-She, Yang
Papa, João Papa
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.
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
Subject: Computação evolutiva
Algoritmos heurísticos
Imagem hiperespectral
Classificação de imagem
Reconhecimento de padrões
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
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
Address: https://ieeexplore.ieee.org/document/6515634
Date Issue: 2014
Appears in Collections:IC - Artigos e Outros Documentos

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