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

Unoma Ndili; Richard G. Baraniuk; Hyeokho Choi; Robert D. Nowak; Mario A. T. Figueiredo

doi:10.1117/12.445354

22 October 2001 Coding theoretic approach to segmentation and robust CFAR-detection for ladar images

Unoma Ndili, Richard G. Baraniuk, Hyeokho Choi, Robert D. Nowak, Mario A. T. Figueiredo

Proceedings Volume 4379, Automatic Target Recognition XI; (2001) https://doi.org/10.1117/12.445354
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Unoma Ndili, Richard G. Baraniuk, Hyeokho Choi, Robert D. Nowak, and Mario A. T. Figueiredo "Coding theoretic approach to segmentation and robust CFAR-detection for ladar images", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); https://doi.org/10.1117/12.445354

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