Improving Object Classification Accuracy from Electromagnetic Data Using Attention Mechanisms

Abstract

Object classification using electromagnetic waves iscrucial in various applications, including remote sensing, security screening, and biomedical imaging. However, accurately classifying arbitrarily oriented objects from electromagnetic scatteringdata remains a significant challenge. In this work, we propose an attention-based machine-learning framework designed to improve the robustness and accuracy of electromagnetic object classification. Our model leverages an attention mechanism to focus on the most informative scattering features dynamically, enabling enhanced feature extraction and improved generalization across different object orientations. We demonstrate the effectiveness of attention-based models in enhancing object classification robustness using a numerical dataset, showing that the proposed method outperforms conventional machine learning models regarding classification accuracy.