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Content Based Retrieval of Hyperspectral Images Using AMM Induced Endmembers

  • Orlando Maldonado
  • David Vicente
  • Manuel Graña
  • Alicia d’Anjou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3681)

Abstract

Indexing hyperspectral images is a special case of content based image retrieval (CBIR) systems, with the added complexity of the high dimensionality of the pixels. We propose the use of endmembers as the hyperspectral image characterization. We thus define a similarity measure between hyperspectral images based on these image endmembers. The endmembers must be induced from the image data in order to automate the process. For this induction we use Associative Morphological Memories (AMM) and the notion of Morphological Independence.

Keywords

Hyperspectral Image Content Base Image Retrieval Spectral Unmixing Content Base Image Retrieval System Seed Pixel 
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 2005

Authors and Affiliations

  • Orlando Maldonado
    • 1
  • David Vicente
    • 1
  • Manuel Graña
    • 1
  • Alicia d’Anjou
    • 1
  1. 1.Dept. CCIAUPV/EHUSan SebastianSpain

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