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Content Based Retrieval of Hyperspectral Images Using AMM Induced Endmembers

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Author information

Authors and Affiliations

  1. Dept. CCIA, UPV/EHU, Apdo 649, 20080, San Sebastian, Spain

    Orlando Maldonado, David Vicente, Manuel Graña & Alicia d’Anjou

Authors
  1. Orlando Maldonado
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  2. David Vicente
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  3. Manuel Graña
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  4. Alicia d’Anjou
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, Moulsecoomb, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Maldonado, O., Vicente, D., Graña, M., d’Anjou, A. (2005). Content Based Retrieval of Hyperspectral Images Using AMM Induced Endmembers. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_118

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  • DOI: https://doi.org/10.1007/11552413_118

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28894-7

  • Online ISBN: 978-3-540-31983-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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