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Application of Neural Networks and Order Statistics Filters to Speckle Noise Reduction in Remote Sensing Imaging

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Book cover Neurocomputation in Remote Sensing Data Analysis

Summary

A novel approach to suppression of speckle noise in remote sensing imaging based on a combination of segmentation and optimum L-filtering is presented. With the aid of a suitable modification of the Learning Vector Quantizer (LVQ) neural network, the image is segmented in regions of (approximately) homogeneous statistics. For each of the regions a minimum mean-squared-error (MMSE) L-filter is designed, by using the histogram of grey levels as an estimate of the parent distribution of the noisy observations and a suitable estimate of the (assumed constant) original signal in the corresponding region. Thus, a bank of L-filters results, with each of them corresponding to and operating on a different image region. Simulation results are presented, which verify the (qualitative and quantitative) superiority of our technique over a number of commonly used speckle filters.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Patras, 265 00, Patras, Greece

    E. Kofidis

  2. Department of Informatics, University of Athens, 157 71, Athens, Greece

    S. Theodoridis

  3. Department of Informatics, University of Thessaloniki, 540 06, Thessaloniki, Greece

    C. Kotropoulos & I. Pitas

Authors
  1. E. Kofidis
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  2. S. Theodoridis
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  3. C. Kotropoulos
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  4. I. Pitas
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Editor information

Editors and Affiliations

  1. Environmental Mapping and Modelling Unit, Joint Research Centre, Commission of the European Communities Space Applications Institute, I-21020, Ispra (Varese), Italy

    Ioannis Kanellopoulos

  2. School of Computer Science and Electronic Systems, Kingston University, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK

    Graeme G. Wilkinson (Head) (Head)

  3. Electrical and Electronic Engineering Department, University of Cagliari, Piazza d’Armi, I-09123, Cagliari, Italy

    Fabio Roli

  4. Department of Computer Science Advanced Computer Architecture Group, University of York, Y01 5DD, York, UK

    James Austin

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© 1997 Springer-Verlag Berlin Heidelberg

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Kofidis, E., Theodoridis, S., Kotropoulos, C., Pitas, I. (1997). Application of Neural Networks and Order Statistics Filters to Speckle Noise Reduction in Remote Sensing Imaging. In: Kanellopoulos, I., Wilkinson, G.G., Roli, F., Austin, J. (eds) Neurocomputation in Remote Sensing Data Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59041-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-59041-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-63828-2

  • Online ISBN: 978-3-642-59041-2

  • eBook Packages: Springer Book Archive

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