Shape classification of ground penetrating radar using discrete wavelet transform and principle component analysis

Abstract

Ground penetrating radar is one of the promising non-destructive investigation for shallow subsurface exploration in locating buried utilities. However, interpreting hyperbolic signature of buried objects in GPR images remains a challenging task since the GPR signals are easily corrupted by environmental noise and cause misinterpretation of the size and geometry of subsurface object from the GPR raw profile. Therefore, this paper proposes Discrete Wavelet Transform (DWT) and principal component analysis (PCA) to classify geometry of buried object using k-nearest neighbour. (k-NN). The GPR images firstly being pre-processed. Then, the GPR images are decomposed using DWT into four sub-bands which are LL (Low-Low), LH (Low-High), HL (High-Low) and HH (High-High). The sub-bands LL or a coarse approximation coefficients was extracted as DWT features in order to classify the shape of buried objects. Since DWT features do contain high dimensional data, thus PCA is used to reduce the dimensional features from higher to lower space by linear transformation. The new projected features were then classify using k-NN classifier into four shapes which is cubic, cylinder, disc and sphere. A series of experiments have been conducted on extracted DWT and PCA features from hyperbolic signature of buried objects having different shapes. Based on the results, the proposed method had achieved the average recognition rate of 99.41%.

An Intelligent Detection Method for Obstacles in Agricultural Soil with FDTD Modeling and MSVMs

“…(4) Image binary coding. (5) End the image processing flow and output local coordinates. (6) Convert position coordinates to geospatial coordinates.…”

“…The classification or analysis of radar signals in terms of differences in electromagnetic properties has become a key research issue [2][3][4], because the modeling of agricultural soil requires efficiency and precision in order to provide the elusive physical parameters needed to meet the requirements of artificial intelligence algorithms that require multi-sample supervised learning. Artificial intelligence methods can quickly locate objects through GPR image processing, but it is very difficult to collect unknown sample characteristics, such as the electrical conductivity and water content of an object in actual farmland soil [5]. Our proposed solution to address this issue involves utilizing the FDTD simulation method to replicate the actual physical conditions of the soil environment.…”