Using a coding theoretic approach, we achieve unsupervised image segmentation by implementing Rissanen's concept of Minimum Description Length (MDL) for estimating piecewise homogeneous regions in images. MDL offers a mathematical foundation for balancing brevity of descriptions against their fidelity to the data by penalizing overly complex representations. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant. The image pixels are conditionally independent and Gaussian, given the mean and variance functions. Our model is aimed at identifying regions of constant intensity (mean) and texture (variance). We adopt a multi-scale encoding approach to the segmentation problem, and develop two different schemes. One algorithm is based on an adaptive (greedy) rectangular partitioning, while the second algorithm is an optimally-pruned wedgelet-decorated dyadic partitioning scheme. We compare the two algorithms with the more common signal plus constant noise schemes, which account for variations in mean only. We explore applications of our algorithms on Synthetic Aperture Radar (SAR) imagery. Based on our segmentation scheme, we implement a robust Constant False alarm Rate (CFAR) detector towards Automatic Target Recognition (ATR) on Laser Radar (LADAR) and Infra-Red (IR) images.