<p>Interferometric inverse synthetic aperture radar (InISAR) reconstructs three-dimensional (3-D) target geometry by exploiting the interferometric phase between multi-channel two-dimensional (2-D) images, and plays an important role in space domain awareness (SDA) and maritime surveillance. However, the performance of InISAR is fundamentally constrained by the limited resolution of 2-D images and the difficulty of maintaining inter-channel coherence. Conventional compressed sensing (CS)-based super-resolution methods rely on explicit dictionaries and fragile sparsity priors, which often fail to capture the diverse scattering features of space targets. To overcome these limitations, this paper proposes a novel multi-channel joint super-resolution algorithm of InISAR using gridless Hankel-lift framework. The proposed algorithm dispenses with dictionary design and instead exploits the intrinsic low-rank property of multi-channel Hankel matrices as a higher-order representation, enabling a more general and robust characterization of scattering features. This low-rank prior is embedded into a multi-channel echo extrapolation problem and solved with an iterative re-weighted least squares (IRLS) algorithm. By jointly enhancing resolution and preserving inter-channel coherence, the proposed algorithm produces high-quality 2-D images that retain interferometric phase integrity and lead to accurate, robust, and interpretable 3-D reconstructions. Simulated and measured data experiments demonstrate the improved performance over most of the state-of-the-art CS-based algorithms.</p>