Pixel- and Model-Based Microwave Inversion With Supervised Descent Method for Dielectric Targets

Abstract

We present a general framework of applying supervised descent method (SDM) to the pixel- and model-based full-wave inversion of microwave data for dielectric targets. SDM is a machine learning approach that utilizes the learned descent directions to reconstruct models. In this article, we study the pixel-based inversion with an online regularization scheme, and the model-based inversion with the parametric level set approach. Furthermore, an online restart scheme is studied to further reduce the data residual. In addition, we investigate the generalization ability of this machine-learning algorithm. In the numerical test, the pixel-based SDM inversion are used to process both single- or multi-frequency data, and the model-based inversion are performed on data with limited observations. Both synthetic and experimental results show good accuracy, speed, and generalization ability of this algorithm. SDM may provide us a potential way to improve reconstruction quality in nonlinear inversion through information fusion of measured data, microwave physics, and various prior information.