Computer vision for locating buried objects

Given two registered images of the Earth, measured with aerial dual-band infrared (IR) sensors, the authors use advanced computer vision/automatic target recognition techniques to estimate the positions of buried land mines. The images are very difficult to interpret, because of large amounts of clutter. Conventional techniques use single-band imagery and simple correlations. They rely heavily on the judgment of the human doing the interpretation, and give unsatisfactory results with difficult data sets of the type analyzed here. The automatic algorithms used by the authors are able to eliminate most of the clutter and give greatly improved indications of regions in the image that could be interpreted as mines. The novelty of the present approach lies in the following aspects: (1) a patented data fusion technique using two IR images and physical principles based on Planck's law; (2) a new region-based texture segmentation algorithm using Gabor transform features and a clustering/thresholding algorithm based on a neural network (self-organizing feature map); (3) prior knowledge of measured feasible temperatures and emissivities; and (4) results with real data using buried surrogate mines.<
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