Keypoint Based Image Processing for Landmine Detection in GPR Data

Image keypoints are widely used in computer vision for object matching and recognition, where they provide the best solution for matching and instance recognition of complex objects within cluttered images. Most matching algorithms operate by first finding interest points, or keypoints, that are expected to be common across multiple views of the same object. A small area, or patch, around each keypoint can be represented by a numerical descriptor that describes the structure of the patch. By matching descriptors from keypoints found in 2-D data to keypoints of known origin, matching algorithms can determine the likelihood that any particular patch matches a pre-existing template. The objective in this research is to apply these methods to two-dimensional slices of Ground Penetrating Radar (GPR) data in order to distinguish between landmine and non-landmine responses. In this work, a variety of established object matching algorithms have been tested and evaluated to examine their application to GPR data. In addition, GPR specific keypoint and descriptor methods have been developed which better suit the landmine detection task within GPR data. These methods improve on the performance of standard image processing techniques, and show promise for future work involving translations of technologies from the computer vision field to landmine detection in GPR data.