Buried and Surface Mine Detection From Thermal Image Time Series

Millions of buried and surface landmines throughout the world will continue to kill and injure people for many decades. Reliable landmine detection systems, especially those that do not require that an operator enters a minefield, are demanded. In such systems, data are captured by sensors and processed with signal-processing techniques. In recent studies, ground-penetrating radars, ultrasound transducers, metal detectors, and infrared sensors have been used to capture data as individual images or image time series. Bare soil and mines have different thermal characteristics, and this difference can be observed on the soil surface with thermal sensors. Since this is a dynamic behavior driven by radiation from the sun and changes with temperature during the day, it can be observed better in thermal image time series. In the proposed method, a two-step approach is adopted: pixelwise classification followed by mine detection. In the first step, a specific spatial filter is applied on images and the filtered image series is classified pixelwise using supervised classification. In the second step, the classified pixels are combined after a smoothing operation to detect mines. The types of the detected mines can also be determined. The method is tested on real data to quantify its classification and detection performance. The results are quite promising. Among the classifiers tested in this paper, the quadratic discriminate function classifier produces the best results.