Optimal Iterated Two-Class Separation in Hyperspectral Data

AbstractThis paper gives an iterated extension of canonical discriminant analysis for separation between two groups or classes in multi- or hypervariate data. We show that the iterative extension greatly enhances the separation between classes in a case with 110-band HyMap data covering part of the Sokolov mining area in the Czech Republic where we separate water from "every thing else'.
TypeConference paper [Abstract]
Conference9th EARSeL SIG Imaging Spectroscopy workshop
Year2015    Month April
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics, Geoinformatics