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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Gaussian Process Approach to Buried Object Size Estimation in GPR Images
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Gaussian Process Approach to Buried Object Size Estimation in GPR Images

机译：GPR图像中埋藏物体尺寸估计的高斯过程方法

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摘要

Recently, a promising pattern-recognition system has been presented to deal with the extraction of buried-object characteristics in ground-penetrating-radar images. In particular, it allows the detecting of buried objects by means of a search method based on genetic algorithms and the recognizing of the material type of the identified objects through a classification approach based on support vector machines. In this letter, we propose to extend the processing capabilities of this system by addressing the issue of the detected buried-object size estimation. This problem is viewed as a regression issue where it is aimed at reproducing the relationship between a set of opportunely extracted features and the object size. For such purpose, it is formulated within a Gaussian process (GP) regression approach. A detailed experimental study is reported, showing encouraging object-size-estimation accuracies even when buried objects are close to each other.
机译：最近，提出了一种有前途的模式识别系统，用于处理探地雷达图像中掩埋物体特征的提取。特别是，它允许通过基于遗传算法的搜索方法检测掩埋物体，并通过基于支持向量机的分类方法识别所识别物体的材料类型。在这封信中，我们建议通过解决检测到的掩埋物体尺寸估算的问题来扩展该系统的处理能力。该问题被视为回归问题，其目的在于再现一组适当提取的特征与对象大小之间的关系。为此，它是在高斯过程（GP）回归方法中制定的。据报道，进行了详细的实验研究，结果表明，即使埋藏的物体彼此靠近，物体的尺寸估算精度也令人鼓舞。

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