This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework. A simplified radar system is utilized by transmitting the sparse probing frequency signal, where the ISAR imaging problem can be converted to deal with underdetermined linear inverse scattering. Following the Bayesian compressive sensing (BCS) theory, a hierarchical Bayesian prior is employed to model the scatterers in the range-Doppler plane. In contrast to the independent prior on each scatterer in the conventional BCS, a correlated prior is proposed to statistically encourage the continuity structure of the scatterers in the target region. To overcome the intractability of the posterior distribution, the Gibbs sampling strategy is used for Bayesian inference. The parameters of the signal model are inferred efficiently from samples obtained by the Gibbs sampler. Because the proposed method is a data-driven learning process, the tedious parameter tuning process required by the convex optimization-based approaches can be avoided. Both the synthetic and the experimental results demonstrate that the proposed algorithm can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems.