Non-Gaussian stochastic model for analysis of automatic detection/recognition

  • Philip B. Chapple
  • Derek C. Bertilone
  • Steven Angeli
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


We demonstrate that a very simple stochastic model based on nonlinear transformation of Gaussian random fields can be successfully used to model homogeneous non-Gaussian natural backgrounds observed in a wide range of airborne and spacebome sensor imagery. We use this model to simulate backgrounds ranging from IR forest terrain to SAR woodland and SAR sea surface imagery. The model reproduces the histogram, second-order correlations, and third-order correlations measured in the real imagery. We discuss applications in the design and analysis of algorithms for automatic detection and recognition of objects embedded in natural imagery.


Synthetic Aperture Radar Synthetic Aperture Radar Image Gaussian Random Field Simple Stochastic Model Small Target Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Philip B. Chapple
    • 1
  • Derek C. Bertilone
    • 1
  • Steven Angeli
    • 1
  1. 1.Wide Area Surveillance DivisionDefence Science and Technology OrganisationSalisburyAustralia

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