A machine learning approach for finding hyperspectral endmembers

Amit Banerjee, Philippe Burlina, Joshua Broadwater

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

Abstract

A support vector algorithm for detecting endmembers in a hyperspectral image is introduced. It is a novel method for finding the spectral convexities in a high-dimensional space which addresses several limitations of previous endmember methods. A new approach for estimating the number of endmembers using rate-distortion theory is also presented. It is based upon the observation that the endmembers form a set of basis vectors for the hyperspectral datacube using the linear mixture model. The result is a fully-automatic method for endmember detection. Experimental results using the Cuprite datacube are presented.

Original languageEnglish (US)
Title of host publication2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Pages3817-3820
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

  • Endmember extraction
  • Hyperspectral processing
  • Support vector methods

ASJC Scopus subject areas

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

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  • Cite this

    Banerjee, A., Burlina, P., & Broadwater, J. (2007). A machine learning approach for finding hyperspectral endmembers. In 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 (pp. 3817-3820). [4423675] (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2007.4423675