An adaptive framework is presented for frequency-stepped ground-penetrating radar (GPR) imaging of low-contrast buried objects in the presence of a moderately rough air--soil interface, with potential applications intended in the area of humanitarian demining. The proposed approach, so far restricted to two-dimeansional (2-D) geometries, works with sparse data and relies on recently developed problem-matched narrow-waisted Gaussian beam (GB) algorithms as fast forward scattering predictive models to estimate and compensate for the effects of the coarse-scale roughness profile. Possible targets are subsequently imaged by inverting the Born-linearized subsurface scattering model via object-based curve evolution (CE) techniques. This frequency domain (FD) strategy implements a further step in our planned sequential approach toward a physics based, robust, and numerically efficient framework for rough surface underground imaging in both FD and time domain (TD). Numerical experiments indicate that the proposed framework is attractive from both computational and robustness viewpoints. The results in this paper could also be used for synthesis of TD illumination (in a previous study <Galdi et al., 2001b>, we have dealt with wideband illumination directly in the TD).