Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models

TL;DR

This paper introduces a novel deep learning approach using covariance matrices and SPD matrix classification to improve buried object detection from GPR images, especially with limited or noisy data.

Contribution

It presents a new deep learning model that combines CNN features with covariance matrices and SPD matrix classification for GPR data analysis.

Findings

Outperforms shallow networks and conventional CNNs on large GPR datasets.

Maintains high accuracy with limited training data and mislabeled samples.

Effective across different weather conditions and data collection scenarios.

Abstract

In this paper, a new classification model based on covariance matrices is built in order to classify buried objects. The inputs of the proposed models are the hyperbola thumbnails obtained with a classical Ground Penetrating Radar (GPR) system. These thumbnails are then inputs to the first layers of a classical CNN, which then produces a covariance matrix using the outputs of the convolutional filters. Next, the covariance matrix is given to a network composed of specific layers to classify Symmetric Positive Definite (SPD) matrices. We show in a large database that our approach outperform shallow networks designed for GPR data and conventional CNNs typically used in computer vision applications, particularly when the number of training data decreases and in the presence of mislabeled data. We also illustrate the interest of our models when training data and test sets are obtained from different weather modes or considerations.