Optimal Class Separation in Hyperspectral Image Data: Iterated Canonical Discriminant Analysis

Allan Aasbjerg Nielsen, Andreas Müller

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

This paper describes canonical discriminant analysis and sketches an iterative version which is then applied to obtain optimal separation between a region, here examplified by either “water” or “wood/trees” and the rest of a HyMap image. We show that the iterative version greatly enhances the separation between the regions.
Original languageEnglish
Publication date2012
Number of pages4
Publication statusPublished - 2012
EventThird Annual Hyperspectral Imaging Conference (HSI2012) - , Italy
Duration: 15 May 201216 May 2012
Conference number: 3
http://istituto.ingv.it/l-ingv/convegni-e-seminari/archivio-congressi/convegni-2012/hsi2012-1/hsi2012/view

Conference

ConferenceThird Annual Hyperspectral Imaging Conference (HSI2012)
Number3
CountryItaly
Period15/05/201216/05/2012
Internet address

Keywords

  • Two-class discrimination
  • HyMap

Fingerprint Dive into the research topics of 'Optimal Class Separation in Hyperspectral Image Data: Iterated Canonical Discriminant Analysis'. Together they form a unique fingerprint.

Cite this