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Proceedings Paper

Correlating military operators' visual demands with multi-spectral image fusion
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Paper Abstract

Multi-spectral image portrayal using several sensors is a revolutionary way to increase the amount of useful visual information to the end user. However, for maximum usability, the information from multiple sensors must be fused into a single image that can be understood. The decisions about which sensors are delivering the most important information for a given viewing situation and what manipulations should be done to the acquired data are complex. To better examine this complexity, information was obtained from aviators about which visual tasks are deemed to be most important. This information was gathered from discussions with pilots and other aircrew members as well as from relevant publications. The important visual task information was then used to develop a matrix that included specific visual aspects of the task (e.g., detection or identification). The matrix also included other parameters that could affect or alter the ability to "see" the desired target or perform the task. These other parameters include ambient lighting, environmental conditions (e.g., clear or hazy atmospheres), man-made impediments to vision (camouflage or smoke), and which image enhancing algorithms should be applied (e.g., contrast enhancement or noise reduction). This top-down evaluation was then used to determine which image enhancement algorithms are most important and which will be employed most often for the identified visual tasks.

Paper Details

Date Published: 17 April 2008
PDF: 7 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69681S (17 April 2008); doi: 10.1117/12.778322
Show Author Affiliations
Gary L. Martinsen, Air Force Research Lab. (United States)
Jonathan S. Hosket, Air Force Research Lab. (United States)
Alan R. Pinkus, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)

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