Adaptive target detection in foliage-penetrating SAR images using alpha-stable models

A. Banerjee, Philippe Burlina, R. Chellappa

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (UWB SAR) images is an important and challenging problem. Given the different nature of target returns in foliage and nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, conventional detection algorithms fail to yield robust target detection results. A new target detection algorithm is proposed that 1) incorporates symmetric alpha-stable (SαS) distributions for accurate clutter modeling, 2) constructs a two-dimensional (2-D) site model for deriving local context, and 3) exploits the site model for region-adaptive target detection. Theoretical and empirical evidence is given to support the use of the SαS model for image segmentation and constant false alarm rate (CFAR) detection. Results of our algorithm on real FOPEN images collected by the Army Research Laboratory are provided.

Original languageEnglish (US)
Pages (from-to)1823-1831
Number of pages9
JournalIEEE Transactions on Image Processing
Issue number12
StatePublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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