Lab 6: Geometric Correction

Goals and Background:

This lab is designed to introduce you to a very important image preprocessing exercise known as geometric correction. This lab is structured to develop your skills on the two major types of geometric correction (image to map rectification and image to image registration) that are normally performed on satellite images as part of the preprocessing activities prior to the extraction of biophysical and sociocultural information from satellite images.

Methods:

Image-to-map rectification

This geometric correction uses a map coordinate system to rectify/transform the image data pixel coordinates. This particular imagery is from Chicago, Illinois. In order to rectify the images, one needs to access the Ground Control Points (GCPs) via the Multispectral tab, then Control Points, Select Geometric Model, then Polynomial. The first order polynomial transformation was the model used for this image-to-map-rectification. With the 1st order, only 3 GCPs are required. However, these points need to be spread throughout the image to most accurately adjust the incorrect image (figure 1). It is best to place the GCPs in areas on the imagery that can be easily recognizable, such as an intersection. While placing the GCPs, one is place on the uncorrected image and then another is placed on the map image in the same spot. When placing the GCPs is all said and done, one needs to look at the total root mean square (RMS) error in the bottom right corner of the Multipoint Geometric Correction. The lower the total RMS error number, the more spatially accurate the imagery is. For this particular model, we needed to get under 2 for the total RMS error. The first GCPs total RMS error was not under 2, so I had to go back through each GCP and try to move it so the RMS error was getting smaller, eventually under 2. After correcting the GCP locations, the nearest neighbor interpolation method was run to resample the imagery. The corrected imagery can be viewed in figure 4.    

Figure 1. During the process of assigning GCPs. 

Image-to-image-registration

This geometric correction uses a previously corrected image of the same location to rectify/transform the image data pixel coordinates. This particular imagery is from Sierra Leone in Africa. Like the image-to-map rectification, the image-to-image registration required obtaining GCPs from the imagery. However, instead of using 3 GCPs, 10 were used for this method. The imagery from Chicago was set to be transformed using 1st order polynomial transformation, whereas the Sierra Leone imagery used the 3rd order polynomial transformation. The total RMS error needed to be under 1 for this model, as set by Dr. Cyril Wilson. However, the industry standard is set to .5, or half of a pixel. After tedious work, I was able to achieve a total RMS error of .4381 (figure 2). Once the GCPs were collected, the bilinear interpolation method was run to resample the imagery. The juxtaposition between the pre and post geometrically corrected images can be seen in figure 3. The corrected output imagery can be viewed in figure 5.

Figure 2. Multipoint Geometric Correction interface. Note the .4381 total RMS error.

Figure 3. Pre and post geometric correction for the 3rd order polynomial of Sierra Leone. The corrected image is below the geometrically corrected image.

Results:

Image-to-map rectification:

Below is the result of the image-to-map rectification. This uses a planimetrically correct map as a reference for an uncorrected image. The image is corrected via GCPs to 'match up' the features on the image to the map (figure 4).

Figure 4. The 1st order polynomial corrected image is on the left and the uncorrected image is on the right.

Image-to-image-registration:

Image-to-image registration is similar to image-to-map rectification, except that image-to images uses an image as the 'correcting' mechanism. This 3rd order polynomial used 10 GCPs instead of the 3 that were used for the 1st order image-map rectification. Note that the output image for image-to-image registration is very hazy. Ideally this should be corrected in order to execute a proper analysis (figure 5).

Figure 5. The geometrically corrected image for the 3rd order polynomial.

Conclusion:

Although geometric correction can seem like a hassle at the time, it is vital to accurately analyze imagery. The more GCPs used for a geometric correction, the more accurate the image. It was difficult at first to get the total RMS error under 2 (for image to map rectification) and under 1 (for image-to-image-registration). However, the longer I practiced, the quicker I was able to accurately locate the GCPs. I think this process will go much smoother in the future! It is also important to be aware of the imagery one uses. The output for the image-to-image-registration image's bilinear interpolation is very cloud heavy, which will make analysis very very difficult. It is important to consider both the image to be corrected as well as interpolation method in order to produce the best output image.

Sources: 

Satellite images are from Earth Resources Observation and Science Center, United States Geological Survey.

Digital raster graphic (DRG) is from Illinois Geospatial Data Clearing House.