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Orientador(es)
Resumo(s)
Linear spectral mixture analysis, or linear unmixing, has proven to be a useful tool in hyperspectral remote sensing applications. It aims at estimating the number of reference substances, also called endmembers, their spectral signature and abundance fractions, using only the observed data (mixed pixels). This paper presents new method that performs unsupervised endmember extraction from hyperspectral data. The algorithm exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O(n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.
Descrição
Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings
Palavras-chave
Linear spectral mixture analysis Linear unmixing Hyperspectral remote sensing applications
Contexto Educativo
Citação
NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Vertex Component Analysis: A Fast Algorithm to Extract Endmembers Spectra from Hyperspectral Data. Pattern Recognition and Image Analysis. ISBN 978-3-540-40217-6. Vol. 2652 (2003), p. 626-635.