Abstract: ‍ ‍Three dimensional （3-D） Synthetic Aperture Radar （SAR） imaging is an important development of traditional two dimensional （2-D） SAR imaging in the field of radar precision information acquisition and perception， which can distinguish multiple targets overlapping in the same pixel of 2-D SAR images. Sparse signal processing is an effective method for 3-D SAR imaging. However， due to the nonlinear characteristics of sparse signal processing， it often needs iterative operation and has low efficiency. Researchers have proposed the idea of using deep learning technology to solve nonlinear signal processing problems quickly， which has been initially applied in 3-D imaging field. However， due to the scarcity of SAR 3-D real data， the training of 3-D imaging network can only rely on simulation data， and there is a difference between simulation data and real data， which results in limited accuracy of SAR 3-D imaging based on deep learning method. To this end， this paper proposes a signal space normalization super-resolution network based on singular value decomposition （SVD Signal-Space Normalization Super-Resolution Net， SVD-SRNet）. The proposed method can solve the problem of low robustness of 3-D imaging network method due to the large difference between simulation data and real data. Compared with traditional methods， the proposed method has better imaging accuracy. The computer simulation test and the UAV SAR measured data test prove the effectiveness of the method proposed in this paper.