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9 August 1988 Multisensor Target Detection And Classification
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Abstract
In this paper a new approach to the detection and classification of tactical targets using a multifunction laser radar sensor is developed. Targets of interest are tanks, jeeps, trucks, and other vehicles. Doppler images are segmented by developing a new technique which compensates for spurious doppler returns. Relative range images are segmented using an approach based on range gradients. The resultant shapes in the segmented images are then classified using Zernike moment invariants as shape descriptors. Two classification decision rules are implemented: a classical statistical nearest-neighbor approach and a multilayer perceptron architecture. The doppler segmentation algorithm was applied to a set of 180 real sensor images. An accurate segmentation was obtained for 89 percent of the images. The new doppler segmentation proved to be a robust method, and the moment invariants were effective in discriminating the tactical targets. Tanks were classified correctly 86 percent of the time. The most important result of this research is the demonstration of the use of a new information processing architecture for image processing applications.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dennis W Ruck, Steven K Rogers, James P Mills, and Matthew Kabrisky "Multisensor Target Detection And Classification", Proc. SPIE 0931, Sensor Fusion, (9 August 1988); https://doi.org/10.1117/12.946642
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