Effect of magnitude and phase of millimeter-wave images on classification accuracy

Millimetre-wave (mmW) reconstructed images are of complex-valued in nature, suggesting that they contain both magnitude and phase. It is known that from the phase aspect of the reconstructed images, meaningful feature information can be extracted about the imaged objects, which in turn, is beneficial to solve computer vision problems such as classification. To this end, a comparative study is shown in this paper wherein two Convolutional Neural Network (CNN) models are considered: one trained with magnitude aspect of mmW reconstructed images, and the other is trained with both the magnitude and the phase aspects of mmW reconstructed images. After training, when these two models are tested, a higher classification accuracy is obtained in the performance of the classification model trained with both the magnitude and phase information of mmW images, as compared to the other model.