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.

翻译：本文构建了一种基于协方差矩阵的新型分类模型，用于对埋藏物体进行分类。所提模型的输入是通过经典探地雷达系统获取的双曲线缩略图。这些缩略图随后输入到经典CNN的初始层，该网络利用卷积滤波器的输出生成协方差矩阵。接着，将该协方差矩阵馈入一个由特定层构成的网络，用于对对称正定矩阵进行分类。我们在一个大型数据库上证明，相较于为GPR数据设计的浅层网络以及计算机视觉应用中常用的传统CNN，我们的方法表现更优，尤其是在训练数据量减少和存在错误标记数据的情况下。我们还展示了当训练数据和测试集来自不同天气模式或考虑因素时，我们模型的价值所在。