Nature-Inspired Framework for Hyperspectral Band Selection
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Data
2014-04-01
Autores
Nakamura, Rodrigo Y. M. [UNESP]
Garcia Fonseca, Leila Maria
Santos, Jefersson Alex dos
Torres, Ricardo da S.
Yang, Xin-She
Papa, João Paulo [UNESP]
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Editor
Institute of Electrical and Electronics Engineers (IEEE)
Resumo
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.
Descrição
Palavras-chave
Evolutionary computation, heuristic algorithms, hyperspectral imaging, image classification, pattern recognition
Como citar
Ieee Transactions On Geoscience And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 52, n. 4, p. 2126-2137, 2014.