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Proceedings Paper

Multisensor Target Detection And Classification
Author(s): Dennis W Ruck; Steven K Rogers; James P Mills; Matthew Kabrisky
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Paper 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.

Paper Details

Date Published: 9 August 1988
PDF: 8 pages
Proc. SPIE 0931, Sensor Fusion, (9 August 1988); doi: 10.1117/12.946642
Show Author Affiliations
Dennis W Ruck, Air Force Institute of Technology (United States)
Steven K Rogers, Air Force Institute of Technology (United States)
James P Mills, Air Force Institute of Technology (United States)
Matthew Kabrisky, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 0931:
Sensor Fusion
Charles B. Weaver, Editor(s)

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