Segmentation of textured polarimetric SAR scenes by likelihood approximation

  • Beaulieu J
  • Touzi R
  • 29

    Readers

    Mendeley users who have this article in their library.
  • 75

    Citations

    Citations of this article.

Abstract

A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined.

Author-supplied keywords

  • Hierarchical image segmentation
  • Maximum-likelihood estimation
  • Polarimetric synthetic aperture radar (SAR) image
  • Texture
  • Wishart and K-distributions

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Jean Marie Beaulieu

  • Ridha Touzi

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free