Gaussian process approach to buried object size estimation in GPR images

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.