Share Email Print

Proceedings Paper

Multisensor Information Fusion For Target Detection And Classification
Author(s): Michael C Roggemann; James P Mills; Steven K Rogers; Matthew Kabrisky
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Merging information available from multisensor views of a scene is a useful approach to target detection and classification. Development of multisensor information fusion techniques using a data base of real imagery from an absolute range laser radar and a corresponding forward looking infrared (FLIR) sensor is underway. Our conceptual approach to multisensor target detection and classification uses senor-dependent segmentation and feature extraction. Information is fused first at the detection level and then within the classifier. We hypothesize that an approach to information fusion based on the mathematical theory of evidence (i.e., evidential reasoning) is a useful method for multisensor object classification. In this paper we summarize an approach to a multisensor object classification system, discuss results of multisensor segmentation algorithm, and present an evidential reasoning-based approach to a multisensor classifier.

Paper Details

Date Published: 9 August 1988
PDF: 6 pages
Proc. SPIE 0931, Sensor Fusion, (9 August 1988); doi: 10.1117/12.946641
Show Author Affiliations
Michael C Roggemann, Air Force Institute of Technology (United States)
James P Mills, Air Force Institute of Technology (United States)
Steven K Rogers, 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)

© SPIE. Terms of Use
Back to Top