Support vector machine and neural network classification of metallic objects using coefficients of the spheroidal MQS response modes

Two different supervised learning algorithms, support vector machine (SVM) and neural networks (NN), are applied in classifying metallic objects according to size using the expansion coefficients of their magneto-quasistatic response in the spheroidal coordinate system. The classified objects include homogeneous spheroids and composite metallic assemblages meant to resemble unexploded ordnance. An analytical model is used to generate the necessary training data for each learning method. SVM and NN are shown to be successful in classifying three different types of objects on the basis of size. They are capable of fast classification, making them suitable for real-time application. Furthermore, both methods are robust and have a good tolerance of 20-dB SNR additive Gaussian noise. SVM shows promise in dealing with noise due to uncertainty in the object's position and orientation.