MISTURA ESPECTRAL: (II) CLASSIFICADORES ESPECTRAIS PARA IDENTIFICAÇÃO
DOI:
https://doi.org/10.26512/2236-56562003e39723Palavras-chave:
mistura espectral, classificação espectral, sensoriamento remotoResumo
O presente trabalho possui como objetivo apresentar uma revisão sobre os métodos desenvolvidos para classificação espectral. Os classificadores espectrais que visam à identificação realizam uma comparação do espectro da imagem (EI) com um espectro de referência (ER), proveniente de bibliotecas espectrais ou de membros finais das imagens. As principais diferenças entre os métodos são basicamente duas: (a) opção do emprego da remoção do contínuo e (b) o critério de similaridade a partir do ajuste linear. A remoção do contínuo tem como propósito enfatizar as feições de absorção a partir da retirada do background utilizando uma função matemática, geralmente, o spline cúbico. Os critérios de similaridade são oriundos da regressão linear e do ajuste por mínimos quadrados sendo assim utilizadas formulações do coeficiente de correlação e do erro padrão. Serão descritos os algoritmos dos principais métodos existentes: Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), Spectral Correlation Mapper (SCM) e o coeficiente de determinação utilizado pelo Tricorder e Tetracorder
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