Landmine detection based on image segmentation

Forward-Looking Ground Penetrating Radar has the capability of forming two-dimensional high-resolution images of subsurface objects from a standoff distance. Landmine detection using forward-looking ground penetrating radar was a problem of small targets detection. The performance of traditional detection algorithms degraded because of the presence of non-homogenous environment. In this paper, a segmentation-based method was proposed, which was used to estimate the background and detect landmines. Self- signatures were computed by multi frame images. To decrease computing complexity, estimation after imaging was improved to imaging after estimation by echo data. The images eliminating self-signatures were balanced to ensure the gain of images equal everywhere. The images after balanced were segmented by two-dimension Otsu algorithm. Target areas were masked and decreased the influence to the statistic of clutters. The performance of this method was better than traditional algorithms. A fast algorithm was also proposed to the application of real-time system. It is proved by real data that the method can increase the detection performance of forward-looking ground penetrating radar a lot.