Multisensor Automatic Target Segmentation

Stevens, M. R., Snorrason, M., and Amphay, S.

Frontiers of Remote Sensing Information Processing (C.H. Chen, editor) World Scientific, River Edge, NJ (2003)

Fusing information from sensors with very different phenomenology is an attractive and challenging task for automatic target acquisition (ATA) systems. Sensor fusion improves results when correct target detections correlate between sensors while false alarms do not (due to different properties of targets such as shape and signature of targets). In this paper, we present a series of algorithms for detecting and segmenting targets from their background in passive millimeter wave (PMMW) and laser radar (LADAR) data. PMMW sensors provide a consistent signature for metallic targets, however their angular resolution is too limited to support further target classification. LADAR sensors provide the ATA systems with high angular resolution and 3-dimensional geometric shape information supporting accurate target identification. However, the shape-based segmentation can give very high probability of false alarm under structured clutter scenarios. Sensor fusion techniques are applied with the goal of maintaining high probability of detection while decreasing the false alarm rate.

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