In this study, a linear approach, linear sampling method (LSM) is used to reconstruct the shape of perfectly electric conductors (PEC) with the help of deep learning as a post-processing technique. In microwave imaging, the LSM is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. However, the output of this method depends on the frequency of operation, the choice of regularization parameter,and it is unable to produce satisfactory results for objects with complex shapes. To overcome this drawback, a deep learning approach is used in this work, which can produce a better output in terms of accuracy, resolution. Here, the rough estimate of the PEC scatterer obtained using LSM is used to train the U-Net based convolutional neural network, which maps this output with the corresponding ground truth profiles. The proposed hybrid model is validated using several examples of synthetic and experimental data
