A data-driven approach for risk assessment and material identification of buried objects using microwave measurements and neural networks

Buried object detection plays a critical role in a wide range of applications, including defense, security screening, and subsurface sensing. Traditional microwave-based techniques often depend on handcrafted features or manual interpretation of measurement data, which limits their robustness and accuracy in complex or cluttered environments. To address these challenges, this study presents a deep neural network model trained on experimental multi-frequency microwave S-parameter data for the automated detection and classification of buried objects. The proposed framework processes 2121 S-parameter matrices to capture the electromagnetic response of the subsurface medium. It classifies each sample into three distinct categories: safe, plastic-based threat, and metal-based threat. The model achieves an accuracy of 0.9647, precision of 0.9677, recall of 0.9647, and an F1 score of 0.9635, demonstrating consistent performance across all evaluation metrics. By learning directly from real, experimentally measured data, the network effectively models the complex relationship between microwave responses and object characteristics, eliminating the need for simulation-based data or handcrafted features. These findings represent a substantial step toward the realization of practical, data-driven microwave detection systems, offering improved reliability, scalability, and operational potential for future security and defense applications.