  Ground Penetrating Radar (GPR) has been widely used in pipeline detection and
underground diagnosis. In practical applications, the characteristics of the
GPR data of the detected area and the likely underground anomalous structures
could be rarely acknowledged before fully analyzing the obtained GPR data,
causing challenges to identify the underground structures or abnormals
automatically. In this paper, a GPR B-scan image diagnosis method based on
learning in the model space is proposed. The idea of learning in the model
space is to use models fitted on parts of data as more stable and parsimonious
representations of the data. For the GPR image, 2-Direction Echo State Network
(2D-ESN) is proposed to fit the image segments through the next item
prediction. By building the connections between the points on the image in both
the horizontal and vertical directions, the 2D-ESN regards the GPR image
segment as a whole and could effectively capture the dynamic characteristics of
the GPR image. And then, semi-supervised and supervised learning methods could
be further implemented on the 2D-ESN models for underground diagnosis.
Experiments on real-world datasets are conducted, and the results demonstrate
the effectiveness of the proposed model.
