Lab 6: Geometric Correction

 

 

For all activiaties done in this lab, the images used were from The United States Geological Survey 7.5 minute digital raster graphic data collection.

Part1: Image-to-map rectification

In the first part of the lab, students worked with image-to-map rectification operations. In this case, the image and map in use were covering Chicago and surrounding areas near the Wisconsin and Illinois border.  
students conducted a first order polynomial special interpolation technique. to do this students placed ground control points (GCP's) on the same locations on the image (distorted Image) and the map (reference source).  The computer than uses these GCP's in algorithms that adjust the image to the points on the map, creating an image that is now has accurate geographic properties, closer to their real world location. The number GCP's needed for a given operation depends on the degree of the polynomial operation being done. Since the adjustment is only a minor first order polynomial (linear adjustment) its only necessary to use 3.  With that being said, its advised by the lab to use 4 for the sake of maximized accuracy.  Since the adjustment being made are relatively minor, it is appropriate to just use a nearest neighbor resampling method.

I was able to reach RMS error values lower than the requested 2 % for all four of my GCS points. One should always strive to have the lowest values possible for RMS error in order to ensure an accurate image .

Part 2: Image-to-image rectification

This part of the lab covered a part of Sierra Leone, Africa, and required that I do similar steps as I used above, but this time the components are both images.  There is one image that is geometrically accurate and one that is not.  This can easily bee seen by simply overlaying the two images, and using the swipe tool to see how the features are off from there actual location. Due to the high level of distortion, the operation used was a 3rd order polynomial spatial interpolation. This required that we place a minimum of 10 GCP's on both the reference and distorted image. For the sake of increased accuracy I used 2 extra GCP's, using a total of 12 to correct the distortion.  
Due to the high level of correction being done, the resampling method I used was the Bilinear Method.  This was the selected method because there was more pixel redistribution due to the high level of the original distortion.   

The image produced was still slightly distorted, highlighting the fact that getting the lowest value of RMS error is critical.  even the lab suggested that I get lower than 1.0% for all the points, its advised to get that value as low as possible for the sake of accuracy.

 



PART 1: Image to map rectification: map on the right is the reference source, the image on the left is the distorted image.  The highlighted blue column is the RMS error.

 

 

 

 

PART 2: Image to image rectification: map on left is the distorted image, map on the right is the geometrically accurate Image.

PART 2: This is the corrected image being laid over the already corrected image, Its nearly perfect wit still some distortion visible at the corners.