Adaptive difference of Gaussians to improve subsurface imagery

Abstract

In detection of landmines using ground penetrating radar (GPR), the most significant interference is the specular reflection. Compared with the specular reflection, landmine scattered signals are of small amplitude and difficult to observe. Better detection results can be obtained if the specular reflection can be well separated from the landmine scattered signals. Difference of Gaussians (DOG) is an operation that generates a sharp image from an original image, i.e. in the case of a GPR image, it keeps the specular reflection intact. Therefore by subtracting the DOG output from an original GPR image, we are able to remove the specular reflection and enhance the landmine scattered signals. The DOG takes the difference between two Gaussian curves of zero means and different standard deviations and convolves with the original image. One advantage of the DOG is that the two standard deviations can be chosen properly to suit different applications. We develop an adaptive DOG (ADOG) to process GPR images to improve detection of buried landmines. In the ADOG, the two standard deviations of Gaussians are computed adaptively as a GPR image is scanned from the top to the bottom row. At each row, two windows are used to calculate the two standard deviations and the current row is convolved with the DOG. The output has an unchanged specular reflection while there is a reduced landmine scattered signal. The final image is obtained by subtracting the ADOG output from the original image to remove the specular reflection. The landmine scattered signal is greatly enhanced, allowing more accurate detection.