Abstract: A novel super-resolution imaging algorithm for full polarized inverse synthetic aperture radar (ISAR) is addressed. Based on the distributed compressive sensing (DCS) theory a joint processing of polarization and super-resolution is realized. The fully polarized signal model is established, based on which the super-resolution dictionary is formed. By exploiting the joint sparsity between polarimetric channel signals, the fully polarized super-resolution imaging problem can be mathematically converted into a L₂, ₁ norm optimization question. The optimization problem can be solved via fast optimization algorithm. Comparing with the single-polarization imaging, the jointly multi-polarization imaging performs better on super-resolution and noise suppression by utilizing joint sparsity. Besides, the efficiency of the proposed algorithm can be improved by fast Fourier transform (FFT). Simulated experiments of the backhoe data verify the effectiveness of the proposed algorithm.