Shape Recognition of GPR Images using Hough Transform and PCA plus LDA

Ground penetrating radar (GPR) is a nondestructive test used for shallow subsurface investigation such as land mine detection, mapping and locating buried utilities. In practical applications, GPR images could be noisy due to system noise, the heterogeneity of the medium, and mutual wave interactions. Hence, it is a complex task to recognize the hyperbolic pattern from GPR B-scan images. Thus, this project proposes combined shape recognition of buried objects using Hough Transform (HT) and PCA plus LDA in GPR images. The use of HT is justified because it has the property of transforming global curve detection into efficient peak detection in the Hough parameter space. Whereas PCA plus LDA tries to maximize between-class scatter while minimizing within-class scatter. In this framework, the preprocessed GPR images were extracted using HT. The extracted HT features were subjected to PCA plus LDA to map them from high into lower dimensional features. Then, the reduced
$\text{PCA}+\text{LDA}$
features were used as input to the k-NN classifier to recognize four geometrical shapes cubic, disc, and spherical of the buried objects. Based on the results obtained, the average recognition rate of reduced HT features using PCA plus LDA was achieved 85.30% thus shows a promising result.