Thermal Infrared (TIR) techniques have some interesting capabilities that may assist in the detection of shallowly buried objects, in particular to help in the identification of landmine contaminated areas. The working principle of the sensor is the measurement of the thermal contrast on the soil surface, caused by the disturbance of the thermal flow due to the presence of the buried object with respect to the surroundings. This paper presents some preliminary results for the detection of buried antipersonnel landmines (APLs) with a thermal infrared imaging system. We describe an algorithm for the detection of landmine candidates by exploiting features in the image associated with the observed thermal contrast. Different threshold levels are applied to select groups of pixels that correspond to hot formations in the image, and are the ones that could indicate a target position. A logical AND combination that is then applied to the produced binary images, and can deliver an acceptable performance for landmine detection. However the method cannot distinguish landmine candidates from background variations sharing similar spatial patterns. Since the performance of the method depends strongly on the environmental conditions, a time series measurement is potentially a more promising approach to the whole problem of thermal IR measurement of buried objects. The time series of the IR data set presented in this paper was collected from the test lanes of JRC in Ispra, Italy, in the framework of the Multi-sensor Mine-signature (MsMs) measurement project.
Infrared imagers are capable of the detection of surface laid mines. Several sensor fused land mine detection systems make use of metal detectors, ground penetrating radar and infrared imagers. Infrared detection systems are sensitive to apparent temperature contrasts and their detection capabilities are inversely proportional to the amount of background clutter generated by local surface non- uniformities. This may result in spurious detections, or even cancellation of true detections in a post classification process. Sub-surface mines can be detected when buried not too deeply. Furthermore, soil type and soil water content will influence the detection result. For this reason experiments in various soil types, including vegetation, and soil circumstances are essential for understanding and improving the infrared detection capabilities. We have performed outdoor experiments with different types of soil and weather conditions. Several examples are described and analyzed. Data analysis shows the strong correlation of apparent temperature with thermocouple gradients and solar energy, as well as a correlation of local standard deviation with these parameters. Model based temperature contrasts are predicted for several mines in sandy soils, and these are compared with infrared imaging apparent temperature measurements and thermocouple data. The comparison results are quite good but also show the complexity of the thermal infrared data, in particular due to infrared clutter, diurnal variations, and sky reflectance contributions. Model predictions are made for the application of active heating methods also. Limitations of the model and potential future expansions based on evaluation of experiments are discussed. We discuss the potential use of modeling for thermal infrared detection and sensor fusion applications.