Generalization ability of deep learning algorithms trained using SEM data for objects classification

This paper proposes an efficient method to determine the material of spherical objects and the location of the receiving antenna relative to the object in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra-Wide Band scattered field in time and frequency domains and pre-processed data from the singularity expansion method (SEM) which has seldom been used in classification because it is considered to be noise sensitive. We selected a robust SEM technique which is vector fitting to decompose the frequency response into complex natural resonances (CNRs) and residues. Indeed, CNRs are aspect independent and therefore, can be used to discriminate the objects. However, the residues associated to each pole depend upon the aspect angle, and hence, they were never exploited. In this paper, we propose a novel use of those residues. Additionally, we construct an original data set using SEM data in order to further improve the robustness to noise and the generalization capacity of the learning algorithms. The advantages of using SEM data for object classification are highlighted by comparing it with raw scattered field data in time and frequency domains where the classification algorithms are optimized in each case. The results are very promising, especially in terms of generalization, robustness to noise, and computation time, which are all reasons to take an interest in SEM for these purposes.