Unmixing hyperspectral data

Lucas Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

115 Citations (Scopus)


In hyperspectral imagery one pixel typically consists of a mixture of the reflectance spectra of several materials' where the mixture coefficients correspond to the abundances of the constituting materials. We assume linear combinations of reflectance spectra with some additive normal sensor noise and derive a probabilistic MAP framework for analyzing hyperspectral data. As the material reflectance characteristics are not know a priori' we face the problem of unsupervised linear unmixing. The incorporation of different prior information (e.g. positivity and normalization of the abundances) naturally leads to a family of interesting algorithms' for case yielding an algorithm that can be understood as constrained independent component analysis (ICA). Simulations underline the usefulness of our theory.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262194503, 9780262194501
Publication statusPublished - 2000
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: 1999 Nov 291999 Dec 4

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other13th Annual Neural Information Processing Systems Conference, NIPS 1999
Country/TerritoryUnited States
CityDenver, CO

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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