Recognition For Underground Voids in C-SCANS Based On LSTM Using Ground Penetrating Radar

Ground Penetrating Radar, as a non-invasive detection instrument, is widely used for shallow underground environment exploring. However, as the interpretation for underground voids is still manually performed, the process is inefficient. Aiming at the challenges of automatic recognition for underground voids, we proposed a recognition algorithm based on Long Short-Term Memory(LSTM), which can recognize 3D underground voids automatically. First, we perform energy detection on the images to obtain the preprocessed and prescreened data. Next, EHD, HOG, and Log-Gabor filters are used to extract features. As the traditional methods can only be applied to 2D images, preprocessing for C-scans is employed. Finally, the aggregated features are fed into LSTM for classification. In the experiment, the method is evaluated on simulation data sets, and obtaining a 90% accuracy, which proved the effectiveness and efficiency of our method.