SPECTRICAL MIXTURE: (II) SPECTRICAL CLASSIFIERS FOR IDENTIFICATION
DOI:
https://doi.org/10.26512/2236-56562003e39723Keywords:
spectral mixture, spectral classification, remote sensingAbstract
This present work has as objective to present a review about the methods developed for spectral classification. The spectral classifiers that focus in the identification perform a comparison of spectrum (EI) of the image with a reference spectrum (ER), obtained from the spectral library or the image endmembers. The main differences between these methods are basically two: (a) the option to employ continuum removal, and (b) the similarity criterion based on linear fitting. Continuum removal has as purpose to emphasize the absorption features from the background removal utilizing a mathematical function, generally, the cubic spline. The similarity criteria are originated from linear regression and by minimum squares fitting so utilizing formulations of the correlation coefficient and the standard error. It will be described the algorithms of the main existing methods: Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), Spectral Correlation Mapper (SCM), and coefficient of determination utilized by Tricorder and Tetracorder
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