Automatic Classification of Ground Penetrating Radar Images Based on Unsupervised Learning
Ground penetrating radar can investigate hidden dangers such as cavities,leaks,and looseness in underground spaces,enjoying such advantages as convenience,flexibility,and simple operation.However,the amount of data obtained through large-scale ground penetrating radar detection is very large,and the processed image features are relatively com-plex.Automatically processing large-scale data and quickly screening out existing abnormal features is of great significance for real-time and rapid hidden danger investigation.This study employs the K-means clustering algorithm in unsupervised learning to filter images of common road anomalies and diseases,thereby facilitating rapid classification of anomalous data.In this experiment,the K-means algorithm demonstrated a recognition accuracy of 97.59％for samples generated by forward simulation,and reach 94.51％in the verification of actual data.Experimental results show that this method can be directly classified without labeling data features,providing a certain theoretical basis for supervised learning,and pro-viding reference value for rapid identification of underground space hidden dangers,urban road quality detection,etc.
万方数据
维普
