Sensor feature fusion for detecting buried objects

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Abstract
Given multiple registered images of the earth's surface from dual-band infrared sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two infrared sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised learning pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: preprocessing, feature extraction, feature selection, and classification. We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.

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