用二阶深度学习模型提升地下目标雷达图像分类精度
Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models
- 将雷达回波图转为协方差矩阵，输入专用于SPD矩阵的网络
- 在小样本和标签错误场景下，性能显著优于传统CNN与浅层模型
- 适用于不同天气条件下数据分布变化，鲁棒性强
本文提出一种基于协方差矩阵的新分类模型，用于地下目标识别。输入为经典地面穿透雷达（GPR）系统生成的双曲线缩略图，经传统CNN提取特征后生成协方差矩阵，再送入专门处理对称正定（SPD）矩阵的网络进行分类。在大规模数据库上验证表明，该方法在训练数据少、标签存在误差的情况下，优于为GPR设计的浅层网络及通用计算机视觉中的经典CNN。此外，当训练与测试数据来自不同气象条件时，模型仍保持良好表现，显示其强适应性。
原文摘要 · Abstract (English)
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.
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