1. Image Rectificatio

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

3. 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.

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

5. 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

6. 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)

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

8. Polynomial order

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

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….).

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

13. 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.

14. 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!

15. 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!

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