Deep Learning-Enhanced Qualitative Microwave Imaging: Rationale and Initial Assessment

In this paper, an innovative approach to microwave imaging that combines qualitative imaging and deep learning is presented. The goal is to set a framework for a reliable and user-independent retrieval of the shapes of unknown targets. To this end, the proposed approach exploits an inversion technique known as orthogonality sampling method, which is capable of providing a qualitative estimation of the shape of targets in real-time. The output of the qualitative inversion is processed by a deep learning fully convolutional network called U-Net. U-Net automatically generates binary masks depicting the geometrical properties of the targets, i.e., separates the scattering objects (foreground) from the background. A quantitative assessment of the performance of the processing framework is provided with simulated data to demonstrate the capabilities of the proposed approach.