Lab 6: Geometric Correction

Goal and Background

The purpose of this lab was to introduce us to geometric correction. Geometric correction is performed on satellite images before they can be utilized for data interpretation. This is done to ensure that spatial errors are kept to a minimum. We explored two major types of geometric correction during this exercise.

Part 1: Image-to-Map Rectification

Method



Figure 1 - Uncorrected Landsat TM image of Chicago on right,
USGS 7.5 minute DRG of Chicago on left

Image-to-map rectification involves geometrically correcting an aerial photo using a scanned topographic map of the same area as a reference. These digital maps, called digital raster graphics (DRG), have a coordinate system while the uncorrected aerial photo does not yet.

To practice this technique, I opened a Landsat TM satallite image of the Chicago area as well as a USGS 7.5 minute DRG of the same area (Figure 1) that I could use as reference. The tool for doing this is found in the "Multispectral" tab by clicking on the "Control Points" button. I chose to use a first order polynomial equation because the image was not distorted enough to justify using a higher order polynomial. I also chose to use the Chicago DRG as my reference layer.



Figure 2 - Multipoint Geometric Correction window.
First set of GPCs placed, but RMS error too high.

Once I was in the Multipoint Geometric Correction window, I was able to start correction. This window had my image to be corrected in the pane on the left and my reference image in a pane on the right. Both panes also contained smaller windows with the full image and the zoomed Inquire box images (Figure 2).



 Next, I began the process of adding ground control points (GCPs) on each image. This involved adding pairs of points on each image as geographicly close as possible. This can be time consuming, as the points in each image must be very close to reduce error. Since I was using a first order polynomial, I only needed three pairs of control points (though I used four to be more precise). The higher the order of polynomial, the more GPCs are required. Figure 2 shows my original GPCs, however the RMS (root mean square) error is still too high. I took additional time to perfect the GPCs.

Results

Idealy, the total RMS error should be below .05. For the purposes of this lab, however, we were only required to have an RMS below 2.0. Once this was accomplished, I ran the tool and created a geometrically corrected image.