Dual-Band Land Mine Detection Using a Bayesian Approach

Paper in proceeding, 2002

The main purpose of the paper is to show that significant improvements in infrared land mine detectors can be achieved, by also considering visual wavelength images. A Bayesian approach, based on dual-band data, is presented that incorporates prior knowledge regarding external parameters such as recent weather, burial depth and soil moisture. By noting that most relevant backgrounds render rotationally invariant statistics, a low dimensional parameterization of the noise space is derived. Simulations show the performance of three different detectors; first the standard detector used, the matchedfilter which correlates the infrared image with the known mine shape; secondly a detector which models the spatial statistics of the infrared background while neglecting the visual wavelength data, and thirdly the proposed detector that exploits the full dual-band space. The second detector outperforms the matched filter, and is significantly improved by also utilizing the visual wavelength image.