Spectral Unmixing Through Gaussian Synapse ANNs in Hyperspectral Images View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

J. L. Crespo , R. J. Duro , F. López Peña

ABSTRACT

The work presented here is concerned with the application of Gaussian Synapse based Artificial Neural Networks to the spectral unmixing process when analyzing hyperspectral images. This type of networks and their training algorithm will be shown to be very efficient in the determination of the abundances of the different endmembers present in the image using a very small training set that can be obtained without any knowledge on the proportions of endmembers present. The Networks are tested using a benchmark set of artificially generated hyperspectral images containing five endmembers with spatially diverse abundances and finally verified on a real image. More... »

PAGES

661-668

References to SciGraph publications

  • 1999. Training higher order Gaussian synapses in FOUNDATIONS AND TOOLS FOR NEURAL MODELING
  • 2002-11-05. On Endmember Detection in Hyperspectral Images with Morphological Associative Memories in ADVANCES IN ARTIFICIAL INTELLIGENCE — IBERAMIA 2002
  • Book

    TITLE

    Knowledge-Based Intelligent Information and Engineering Systems

    ISBN

    978-3-540-23318-3
    978-3-540-30132-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-30132-5_91

    DOI

    http://dx.doi.org/10.1007/978-3-540-30132-5_91

    DIMENSIONS

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