Having an all-round expertise in GIS is sometimes not enough to fully understand some concepts of GIS Science. To add to this, I am also not a mathematician.
Considering this, would anyone be able to offer a child's explanation of Root-Mean-Square-Error (RMSE) whilst georeferencing an image onto a basemap? Having done this operation a thousand times, my only concern has been to firstly find locations in the target map which are also in the base map. Using common sense as a tool, I would usually find churches, old buildings, and similar objects which are very stable structures and would not have moved in the time difference between the basemap and the target image. After placing as many passpoints as possible, I would then look at the statistics table and either re-do passpoints with a high RMSE or delete them so that the overall RMSE score becomes as low as possible.
Now I know that the rmse is a statistical error calculation, but what has always bugged me, is that sometimes I am 100% sure that the passpoints are placed very accurately on the images...eg. on a church steeple, or another stable structure which is present in both the target image and basemap, but the rmse is still high. Therefore, I would be able to change the passpoints to a location which is further away from the reference structure (ie make the visual transformation less accurate) in order to decrease the rmse! This appears to me to be a paradox, because I would be decreasing the visual accuracy of the operation in order to increase the statistical accuracy.
Sometimes, I ignore the rmse completely because I can SEE that that after the georeferencing operation, the reference map and the target image line up very well...ie all the pass-points are in exactly the right place on both maps.
Could anyone please offer me a better simple explaination as to whether I am doing something fundamentally wrong here?