Coding theoretic approach to segmentation and robust CFAR-detection for LADAR images

Unoma Ndili, Richard Baraniuk, Hyeokho Choi, Robert Nowak, Mário Figueiredo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


In this paper, we present an unsupervised scheme aimed at segmentation of laser radar (LADAR) imagery for Automatic Target Detection. A coding theoretic approach implements Rissanen's concept of Minimum Description Length (MDL) for estimating piecewise homogeneous regions. MDL is used to penalize overly complex segmentations. The intensity data is modeled as a Gaussian random field whose mean and variance functions are piecewise constant across the image. This model is intended to capture variations in both mean value (intensity) and variance (texture). The segmentation algorithm is based on an adaptive rectangular recursive partitioning scheme. We implement a robust constant false alarm rate (CFAR) detector on the segmented intensity image for target detection and compare our results with the conventional cell averaging (CA) CFAR detector.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsF.A. Sadjadi
Number of pages9
StatePublished - 2001
EventAutomatic Target Recognition XI - Orlando, FL, United States
Duration: Apr 17 2001Apr 20 2001


OtherAutomatic Target Recognition XI
Country/TerritoryUnited States
CityOrlando, FL


  • Adaptive Recursive Partitioning
  • Automatic Target Detection
  • CFAR
  • Laser Radar
  • MDL
  • Segmentation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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