Image Quality Predictions for Space ISR

NIIRS Imagery Sample

Challenge

The space-mission users of the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) space model required improved prediction of image quality for creating realistic Space Intelligence, Surveillance, and Reconnaissance (ISR) simulations with realistic constraints. The previous method used a slant-range approach for estimating the satellite ground resolution which was then related to the National Imagery Interpretability Rating Scale (NIIRS) standard via a look-up table.  This provided coarse results to predict the image quality since it did not include image attributes of the satellite sensor.  ARA provided a solution that benefited the simulation capability and allowed the mission controller to accurately predict the image quality influenced by image attributes.

Solution

ARA improved measurement of key image quality components by implementing the General Image Quality Equation (GIQE) method, increasing the number of key image quality assessments accomplished. Combining that information to leverage strengths in areas such as image sharpness and scale, challenge areas such as image noise become less important to determining the overall quality.  Actual imagery from ISR satellite sensors were analyzed to estimate the parameters pertinent to the GIQE method to yield higher fidelity and more accurate metric of image quality.

Impact

Live ISR exercises were modeled in AFSIM for ISR collection planning cycles to promptly identify satellite passes that meet the mission criteria.  Satellites selected within each live pass was based upon timelines of the pass and image the quality prediction.  Mission controllers in the field could reject poor quality passes, adapt their asset tasking, and concentrate on mission priorities.

Modeling and Simulation

Modeling and Simulation

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