27 August 2001 Coding-theoretic approach to SAR image segmentation
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Abstract
In this paper, a coding theoretic approach is presented for the unsupervised segmentation of SAR images. The approach implements Rissanen's concept of Minimum Description Length (MDL) for estimating piecewise homogeneous regions. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant across the image. The model is intended to capture variations in both mean value (intensity) and variance (texture). We adopt a multiresolution/progressive encoding approach to this segmentation problem and use MDL to penalize overly complex segmentations. We develop two different approaches both of which achieve fast unsupervised segmentation. One algorithm is based on an adaptive (greedy) rectangular recursive partitioning scheme. The second algorithm is based on an optimally-pruned wedgelet-decorated dyadic partition. We present simulation results on SAR data to illustrate the performance obtained with these segmentation techniques.
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Unoma I. Ndili, Robert D. Nowak, Richard G. Baraniuk, Hyeokho Choi, Mario Figueiredo, "Coding-theoretic approach to SAR image segmentation", Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438200; https://doi.org/10.1117/12.438200
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