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11 September 2003 Process for the development of image quality metrics for underwater electro-optic sensors
James Samuel Taylor Jr., Brett Cordes
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Proceedings Volume 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII; (2003) https://doi.org/10.1117/12.501572
Event: AeroSense 2003, 2003, Orlando, Florida, United States
Abstract
Electro-optic identification (EOID) sensors have been demonstrated as an important tool in the identification of bottom sea mines and are transitioning to the fleet. These sensors produce two and three-dimensional images that will be used by operators and algorithms to make the all-important decision regarding use of neutralization systems against sonar contacts classified as mine-like. The quality of EOID images produced can vary dramatically depending on system design, operating parameters, and ocean environment, necessitating the need for a common scale of image quality or interpretability as a basic measure of the information content of the output images and the expected performance that they provide. Two candidate approaches have been identified for the development of an image quality metric. The first approach is the development of a modified National Imagery Interpretability Rating Scale (NIIRS) based on the EOID tasks. Coupled with this new scale would be a modified form of the General Image Quality Equation (GIQE) to provide a bridge from the system parameters to the NIIRS scale. The other approach is based on the Target Acquisition Model (TAM) that has foundations in Johnson’s criteria and a set of tasks. The following paper presents these two approaches along with an explanation of the application to the EOID problem.
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James Samuel Taylor Jr. and Brett Cordes "Process for the development of image quality metrics for underwater electro-optic sensors", Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); https://doi.org/10.1117/12.501572
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KEYWORDS
Sensors
Image quality
Imaging systems
Target acquisition
Electro optical modeling
Image sensors
Image processing
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