Abstract: For ISAR imaging, the range and cross-range resolutions are constrained by the bandwidth of transmitted signal and Coherent Processing Interval (CPI). In this paper, a novel algorithm of Two-Dimension(2D) joint super-resolution ISAR imaging is addressed based on Compressive Sensing (CS) theory. The ISAR observation signal model is established, where the 2D super-resolution dictionary is formed. By exploiting the sparse prior information of ISAR image, 2D super-resolution imaging is mathematically converted into thel1 norm optimization. The super-resolution ISAR imaging can be realized with accuracy via fast optimization algorithm. In the proposed algorithm, the 2D coupling information of the echo can be effectively utilized through the joint processing of range and azimuth dimension. Besides, the efficiency of the proposed algorithm is improved by using the Conjugate Gradient (CG) algorithm, Fast Fourier Transform (FFT) and Hadamard multiplication operations. Simulation and real-data experiments verify the effectiveness of the proposed algorithm.