Adaptive difference of Gaussians to improve subsurface object detection using GPR imagery

We present an adaptive difference of Gaussians (ADOG) method to improve landmine detection from ground penetrating radar (GPR) imagery. GPR is widely used for a variety of subsurface sensing problems including landmine detection and localization. In most all applications, specular reflection from the air-ground interface is the most significant source of interference often eclipsing the returns from the buried object. From an image processing viewpoint, the specular reflection constitutes a sharp edge and the landmine reflected signals are more smooth and of low spatial frequency. As an edge-preserving method, the ADOG keeps the specular reflection intact and removes the object reflected signals. Then by subtracting the ADOG output from the original image, we eliminate the specular reflection and obtain an image with enhanced object reflected signals. Using the difference image, we can obtain better result in detecting buried objects. Results from applying our algorithm on a landmine detection application are used to demonstrate the performance of the method.