A three-criterion taxonomy of metrics to compute the spatial resolution of satellite imagers is presented and used to classify about thirty spatial resolution metrics found in the literature. A new metric, the “Spatial Resolution Function”, is proposed by applying the two-point source resolution criterion to sampled images. This new metric computes resolution distance as a function of the resolving contrast in the image plane, using the sensor’s Point Spread Function as input, and demanding that the output agrees with the Ground Sampling Distance metric for very low optical factors. Further, the application of the Rose criterion to the Spatial Resolution Function allows the dependence of resolution distance on signal to noise ratio to be considered. The procedure to compute the Spatial Resolution Function is applied to three quality types of generic satellite imaging scanners, computing their resolution distances in the along scan and across scan directions, for a wide range of optical factors encountered in Earth observation satellites. The Spatial Resolution Function is used to assess twelve commonly used spatial resolution metrics, the results indicate that all these metrics are biased, showing significant errors when used to compare different satellite imagers. The overall utility of spatial resolution metrics that use a property of the Point Spread Function to roughly estimate resolution distance is challenged, as by using this same function the Spatial Resolution Function allows its exact calculation.