Improvement of Laplacian Edge Detection Algorithm and Its Application on GPR

Edge detection is an important mean to extract image features, and it has been widely used in many fields. Unlike Sobel and Prewitt operator, the traditional Laplacian edge detection operator does not have the function of smoothing image, and it is sensitive to the noise. Thus the noise may be mistakenly considered as edge. By solving the Laplacian transform of second spatial gradient, an improved Laplacian edge detection operator is obtained. The traditional Laplacian edge detection is combined with two-dimension Gaussian function, and the improved Laplacian edge detection optimization algorithm is carried out by the one-dimension convolution. At the same time, the improved Laplace edge detection module is developed based on the QT-CUDA parallel platform, and integrated into the ground penetrating radar (GPR) fine interpretation software system. The experimental results by processing the GPR field data show that the algorithm is not only efficient, but also effective in highlighting effective anomalies, thus improving the recognition ability of GPR targets.