An Adaptive Non-Reference Approach for Characterizing and Assessing Image Quality in Multichannel GPR for Automatic Hyperbola Detection

The automation of the detection infrastructure in GPR imagery is a key issue, particularly in the context of the non-invasive acquisition of radargrams with a multi-antenna ground-penetrating radar. Due to the fact that the dataset acquired with a multi-antenna GPR is very large, in the context of automating the process of detecting hyperbolas, the authors have proposed an adaptive approach to the selection of GPR images. The aim of this project was to develop a method for the selection of GPR images by means of applying the appropriate quality indicators. The authors propose a new, adaptive approach to the selection of radargrams that were recorded during the route of a GPR in a single profile, where several radargrams were recorded. Depending on the obtained initial values of the standard indicators for the assessment of the quality and quality maps of the radargrams, those images from selected channels that will ensure the highest possible quality and efficiency of hyperbola detection were selected. The stage of image quality assessment is essential in the context of improving the effectiveness of the automated detection of underground infrastructure. The quality assessment was performed based on the entropy indicator, PIQE, and Laplacian variance. The selected quality indicators allowed the authors to assess the degree of blurring, noise, and the number of details representing the underground structures that are present in GPR images. An additional product of the quality assessment were the generated maps that present the distribution of entropy in the analyzed images. The image selection was verified based on the results of the parameters that assess the effectiveness of the detection of hyperbolas that represent underground networks. The proposed innovative adaptive approach to the selection of images acquired by GPR enabled a significant improvement in the efficiency of the detection of hyperbolas representing underground utility networks, by 15–40%, shortening data processing and infrastructure detection times.