On the Use of a Hybrid Approach to Contrast Endmember Induction Algorithms

  • Miguel A. Veganzones
  • Carmen Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)


In remote sensing hyperspectral image processing, identifying the constituent spectra (endmembers) of the materials in the image is a key procedure for further analysis. The contrast between Endmember Inductions Algorithms (EIAs) is a delicate issue, because there is a shortage of validation images with accurate ground truth information, and the induced endmembers may not correspond to any know material, because of illumination and atmospheric effects. In this paper we propose a hybrid validation method, composed on a simulation module which generates the validation images from stochastic models and evaluates the EIA through Content Based Image Retrieval (CBIR) on the database of simulated hyperspectral images. We demonstrate the approach with two EIA selected from the literature.


Ground Truth Hyperspectral Image Dissimilarity Measure Content Base Image Retrieval Dissimilarity Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miguel A. Veganzones
    • 1
  • Carmen Hernández
    • 1
  1. 1.Computational Intelligence GroupUPV/EHU, Facultad Informatica, Paseo Manuel de LardizabalSan SebastianSpain

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