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Speckle Reduction Using Stochastic Distances

  • Leonardo Torres
  • Tamer Cavalcante
  • Alejandro C. Frery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity Synthetic Aperture Radar (SAR) data, using the Gamma model with varying number of looks allowing, thus, changes in heterogeneity. Modified Nagao-Matsuyama windows are used to define the samples. The proposal is compared with the Lee’s filter which is considered a standard, using a protocol based on simulation. Among the criteria used to quantify the quality of filters, we employ the equivalent number of looks (related to the signal-to-noise ratio), line contrast, and edge preservation. Moreover, we also assessed the filters by the Universal Image Quality Index and the Pearson’s correlation between edges.

Keywords

information theory SAR speckle reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leonardo Torres
    • 1
  • Tamer Cavalcante
    • 1
  • Alejandro C. Frery
    • 1
  1. 1.Laboratório de Computação Científica e Análise Numérca – LaCCANUniversidade Federal de Alagoas – UFALMaceióBrazil

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