Optimal Iterated Two-Class Separation in Hyperspectral Data |
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| Abstract | This 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'. |
| Type | Conference paper [Abstract] |
| Conference | 9th EARSeL SIG Imaging Spectroscopy workshop |
| Year | 2015 Month April |
| Note | |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics, Geoinformatics |