Multisensor Information Fusion For Target Detection And Classification

Michael C Roggemann; James P Mills; Steven K Rogers; Matthew Kabrisky

doi:10.1117/12.946641

9 August 1988 Multisensor Information Fusion For Target Detection And Classification

Michael C Roggemann, James P Mills, Steven K Rogers, Matthew Kabrisky

Proceedings Volume 0931, Sensor Fusion; (1988) https://doi.org/10.1117/12.946641
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

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.

Citation Download Citation

Michael C Roggemann, James P Mills, Steven K Rogers, and Matthew Kabrisky "Multisensor Information Fusion For Target Detection And Classification", Proc. SPIE 0931, Sensor Fusion, (9 August 1988); https://doi.org/10.1117/12.946641

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