Underground Pipeline and Void Recognition in GPR Data: A Nonlearning Method Based on Slope Domain Transformation and Sparse Encoding

Pipeline and void recognition are two key tasks in the underground monitoring of urban roads. Ground penetrating radar (GPR), as an effective geophysical method, plays an important role in this area. With the increasing amount of GPR data, automatic recognition has become a research hotspot. However, existing automated recognition methods still suffer from low accuracy or high dependence on datasets. In this paper, a non-learning method for both pipeline and void recognition is proposed. In this method, the B-scan is preprocessed by removing the direct coupled wave and multiple echoes.Then, the binarized image is converted to sparse image using non-zero interval sparse coding (NISE). Next, the slope distribution of sparse images is extracted using column offset coding (COE). And clustering is carried out according to the corresponding relationship between the image slope and its original position. Finally, the decision is made according to the slope distribution characteristics of each cluster. The proposed method was tested on both simulated and field data. Experimental results show that the method not only has the advantage of being training-free but also exhibits excellent recognition accuracy.