Different definitions of the signal-to-noise ratio (SNR) are being used as metrics to describe the image quality of remote sensing
systems. It is usually not clear which SNR definition is being used and what the image quality of the system is when an SNR value is quoted.
This paper looks at several SNR metrics used in the remote sensing community. Image simulations of the Kodak Space Remote Sensing
Camera, Model 1000, were produced at different signal levels to give insight into the image quality that corresponds with the different SNR
metric values. The change in image quality of each simulation at different signal levels is also quantified using the National Imagery Interpretability
Rating Scale (NIIRS) and related to the SNR metrics to better understand the relationship between the metric and image interpretability. An
analysis shows that the loss in image interpretability, measured as ?NIIRS, can be modeled as a linear relationship with the noise-equivalent
change in reflection (NE??). This relationship is used to predict the values that the various SNR metrics must exceed to prevent a loss in the
interpretability of the image from the noise.