In this paper, we propose an efficient multiscale approach for the segmentation of natural clutter, specifically grass and forest, in synthetic aperture radar (SAR) imagery. This method exploits the coherent nature of SAR sensors. In particular, we exploit the characteristic statistical differences in imagery of different clutter types, as a function of scale, due to radar speckle. We employ a recently introduced class of multiscale stochastic processes that provide a powerful framework for describing random processes and fields that evolve in scale. We build models representative of each category of clutter of interest (i.e., grass and forest), and use these models to segment the imagery into these two clutter classes. The scale- autoregressive nature of the models allows extremely efficient calculation of the relative likelihoods of different clutter classifications for windows of SAR imagery, and we use these likelihoods as the basis for classifying image pixels and for accurately estimating forest-grass boundaries. We evaluate the performance of the technique by testing it on 0.3 meter SAR data gathered with the Lincoln Laboratory millimeter-wave SAR.