Techniques for Improving Buried Mine Detection in Thermal IR Imagery

We describe sensor-based and signal-processing-based techniques for improving the detection of buried land mines in thermal infrared imagery. Results of experimental studies using MWIR (2.2-4.6 μm) and LWIR (8-12 μm) imaging systems are reported. Thermal clutter due to surface reßected sunlight and skylight are investigated and shown to be the dominant clutter component for both MWIR and LWIR imagery collected during daylight hours. A sensorbased clutter reduction technique, spectral differencing, was considered and found to provide some beneÞt. The temporal evolution of thermal signatures was investigated. The imagery are found to have near-Gaussian statistics, and therefore the deßection coefficient is a valid measure of detectability. The deßection coefficient for some buried mines was found to improve with time after sunset. In addition, the LWIR band appears to offer some advantages in detection. Clutter mitigation via signal processing is also explored using an “estimator-classiÞer” technique in which target-related parameters (features) are estimated from the data and detected with a classiÞer. The theoretical basis of the method is discussed. MWIR and LWIR imagery are used to illustrate both the sensor-based and signalprocessing-based techniques.