Publication Date

2015

Document Type

Thesis

Committee Members

Leslie Blaha (Committee Member), Joseph Houpt (Advisor), Ion Juvina (Committee Member)

Degree Name

Master of Science (MS)

Abstract

Multispectral imagery can supply an observer with different components of information to, in combination, lead to critical decisions. Human observers can be presented with two fusion techniques: 1) cognitive fusion presents the two sensor images within 5 degrees of visual angle and 2) algorithmic fusion aims to enhance image quality by combining relevant information from two individual sensor images into one composite image. Researchers have used methods such as comparing performance across different algorithms or comparing algorithmic fusion to a single-sensor image. However, cognitive fusion is a technique that provides all of the sensor information and, if utilized efficiently, may yield better performance than algorithmic fusion. I used a cognitive framework, systems factorial technology (Townsend & Nowaza, 1995) to test specific underlying mechanisms of information processing across both fusion techniques in two discrimination tasks. The results of my Experiments demonstrate that the efficiency of processing sensor information is just as good for cognitive fusion as algorithmic fusion across both discrimination tasks. Future research with multi-sensor displays should not disregard the potential benefits that displaying all of the available information may have over the algorithmic interpretations of important information.

Page Count

126

Department or Program

Department of Psychology


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