Detection of buried objects in ground penetrating radar data using incremental nonnegative matrix factorization

Ground penetrating radar (GPR) is a popular tool for subsurface sensing and it is widely used for buried object detection. In this study, a new buried object detection method based on the modelling of A-scans by incremental nonnegative matrix factorization (INMF) is presented. The existing clutter in the GPR image is learned via nonnegative matrix factorization (NMF) and the resulting basis and encoding matrices are used in the initialization of the INMF method. Since clutter is learned by NMF in the initialization, the target is considered as an anomaly and a new A-scan containing target signal results in an increase in the error signal level permitting the detection of the target at this antenna location. The proposed method is applied to an original and noisy simulated dataset constructed by the electromagnetic simulation software gprMax as well as to a real dataset. The quantitative results based on receiver operating characteristic (ROC) curves and area under curves (AUC) obtained for the simulated dataset with different SNR levels show that there is an improvement around 5–7% in the detection rate.