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Information Processing with Evolutionary Algorithms pp 45–59Cite as

An Evolutionary Algorithm Based on Morphological Associative Memories for Endmember Selection in Hyperspectral Images

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Summary

In previous works we have introduced Morphological Autoassociative Memories (MAM) as detectors of morphologically independent patterns and their application to the task of endmember determination in hyperspectral images. After shifting the hyperspectral image data to the mean and taking the signs of the resulting hyperspectral patterns, we obtain a binary representation of the image pixels. Morphologically independent binary patterns can be seen as representations of the vertices of a convex region that covers most of the data. The MAMs are used as detectors of morphologically independent binary patterns and the selected binary patterns are taken as the guides for the selection of endmembers for spectral unmixing between the image pixels. This process was defined in a greedy suboptimal fashion, whose results depend largely on the initial conditions. We define an Evolutionary Algorithm for the search of the set of endmembers based on the morphological independence condition and we compare it with a conventional Evolutionary Strategy tailored to the endmember detection task over a multispectral image.

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Author information

Authors and Affiliations

  1. Dept. CCIA, Universidad Pais Vasco, Spain

    M. Graña, C. Hernandez & A. d’Anjou

Authors
  1. M. Graña
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  2. C. Hernandez
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  3. A. d’Anjou
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Editor information

Editors and Affiliations

  1. Dept. CCIA, Universidad Pais Vasco, Spain

    Manuel Graña BSc, MSc, PhD  & Alicia d’Anjou BSc, MSc, PhD  & 

  2. Grupo de Sistemas Autónomos, Universidade da Coruña, Spain

    Richard J. Duro BSc, MSc, PhD

  3. Duke University, Durham, North Carolina, USA

    Paul P. Wang PhD

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© 2005 Springer-Verlag London Limited

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Graña, M., Hernandez, C., d’Anjou, A. (2005). An Evolutionary Algorithm Based on Morphological Associative Memories for Endmember Selection in Hyperspectral Images. In: Wu, X., Jain, L., Graña, M., Duro, R.J., d’Anjou, A., Wang, P.P. (eds) Information Processing with Evolutionary Algorithms. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-117-2_4

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  • DOI: https://doi.org/10.1007/1-84628-117-2_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-866-4

  • Online ISBN: 978-1-84628-117-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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