Kernel fully constrained least squares abundance estimates

Joshua Broadwater, Rama Chellappa, Amit Banerjee, Philippe Burlina

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

Abstract

A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each endmember within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.

Original languageEnglish (US)
Title of host publication2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Pages4041-4044
Number of pages4
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain
Duration: Jun 23 2007Jun 28 2007

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
CountrySpain
CityBarcelona
Period6/23/076/28/07

Keywords

  • Abundance estimates
  • Hyperspectral imagery
  • Kernel functions
  • Spectral unmixing

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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    Broadwater, J., Chellappa, R., Banerjee, A., & Burlina, P. (2007). Kernel fully constrained least squares abundance estimates. In 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 (pp. 4041-4044). [4423736] (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2007.4423736