Abstract: In the application of ground penetrating radar (GPR), the inversion imaging is the key technology for interpreting the data information of GPR. Existing GPR image inversion techniques based on deep learning are mostly applied to the ideal environment of underground homogeneous media. However, the data collected in real environments usually contain complex noise and clutter signals, which greatly affect the accuracy of the inversion. To address this issue, this paper proposes a two-stage GPR image inversion network based on Generative Adversarial Network (GAN), named TSInvNet, to reconstruct the spatial distribution of underground targets in real environments. This method processes the GPR B-scan images through a denoising network, TSInvNet1, using an improved Spatially-Adaptive Normalization (SPADE) generator, and then inputs the processed images into an inversion network, TSInvNet2, which introduces a Shuffle Attention (SA) model for inversion. Experimental results on simulated and real data demonstrate that TSInvNet can accurately invert the underground target positions based on GPR B-scan images, showing strong robustness and precise inversion performance in applications involving complex noise and multiple targets.