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Proceedings Paper

Model-based multisource fusion for exploitation, classification, and recognition
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Paper Abstract

A model-based multi-sensor fusion framework has previously been developed that supports improved target recognition by fusing target signature information obtained from sensor imagery [1], [2]. Image- based signature features, however, are not the only source of information that may be exploited to advantage by a target recognition system. This paper presents a review of the key features of the model-based fusion framework and shows how it can be expanded to support information derived from imaging sensors as well as data from other non-imaging sources. The expanded model-based multi-source framework supports not only the combination of image data, such as Synthetic Aperture Radar (SAR) and electro-optical (EO), but also various types of non-image data that may be derived from those, or other sensor measurements. The paper illustrates the flexibility of the model-based framework by describing the combination of spatial information from an imaging sensor with scattering characteristics derived from polarimetric phase history data. The multi-source fusion is achieved by relating signature features to specific structural elements on the 3-D target geometry. The 3-D model is used as a sensor neutral, view independent, common reference for the combination of multi-source information.

Paper Details

Date Published: 17 May 2006
PDF: 10 pages
Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350X (17 May 2006); doi: 10.1117/12.665309
Show Author Affiliations
Wayne Williams, SAIC (United States)
Eric Keydel, SAIC (United States)
Sean McCarty, SAIC (United States)


Published in SPIE Proceedings Vol. 6235:
Signal Processing, Sensor Fusion, and Target Recognition XV
Ivan Kadar, Editor(s)

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