Image Rectificatio

Georeferencing vs registering Georeferencing – using raw coordinates and tying to locations Registering – matching one image to another. Typically what we do these days.

Why? Correct for some sort of distortion Variations in altitude and velocity of sensor platform Radial distortion (from a camera lens) Curvature of the earth Atmospheric refraction Relief displacement Sensor sweep irregularities Both systematic and random errors.

Use ground control points to correct (GCPs) Locations with exact known coordinates – or that you can find on a registered image.

GCP distributions Spread out all over the map area as best as possible. Mathematical minimum = 3 (for a first order polynomial fit) Really, about 30 If referencing multiple photos/images, be sure to have GCPs shared by multiple photos/images

Good GCP locations Road intersections, shoreline features, houses. Big rocks, etc. Things that don’t move over the time covered by the images. Strategy for digitizing – work around the outside of the image, then toward the middle. Always keeping earlier points visible (as the pattern on both images should be almost identical)

OK. You now have a bunch of GCPs.. What now? Math. Need to think about the polynomial order and your residuals

Polynomial order

Bare min GCPs First order = 3 Second order = 6 Third order = 10

Root Mean Square (RMS) Errors Get x and y residuals – the are in the units of your unregistered image. Your goal is to get them to less than the resolution of a single pixel (good luck….).

Basically Residuals are a measure of how close you are to getting it right.

OK You have an image, a polynomial order, a bunch of GCPs, and some residual numbers. What now? First, examine the residuals and carefully examine the points with the highest residuals. Either correct the gcps or delete them. Unless you’re really sure they’re good, in which case some other point is causing the problem. Repeat until your residuals are less than half the pixel size (ideally). Do this one at a time, rerunning the residual calcs. Note, it’s a good idea to save points, etc just in case you delete one you should keep.

Another test Rectify the image and draw it on top of the rectified image. You might be able to see exactly where the problem is!

New Pixel Values Nearest Neighbor (usually the best option, as it smooths things the least) Bilinear Interpolation Cubic Convolution These you get to look up as part of lab!

Final note Resampling uses these three, and we change spatial resolution on rasters fairly frequently. So know them!