This lab was designed to introduce the class to geometric correction. Geometric correction is performed when an image is distorted from what it should be in reality. There are many different kinds of geometric correction, most of which require the use of a reference image and ground control points (GCPs) to correct the geometry of the distorted image. In this lab two types of geometric corrections were run that are typically performed on satellite images as part of pre-processing to prepare an image for analysis. These two types are image-to-map rectification and image-to-image rectification.
Methods:
Image-to-Map Rectification:
The first portion of the lab involved bring two images of the Chicago area into ERDAS (Figure 1). One of which was a slightly distorted satellite image, while the other was a reference map of the same area. The image-to-map rectification method involves using GCPs to correct geometric errors in the image. GCPs are locations on the Earth's surface which can be identified both accurately in imagery and on a map as well. A GCP is placed on the distorted image, and then subsequently a GCP is placed in the same location on a map. It's important to spread the GCPs throughout the image in order to best apply the geometric correction.
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| This is a view of the two images of the Chicago area. The image on the left is an aerial image with slight distortion; while the image on the right is a reference map of the same area which was used to geometrically correct the image on the right. As can be seen in this picture, GCPs have already been set at this point. (Figure 1) |
The tool to place GCPs is under the Multispectral menu under the option "Control Points". From here the geometric model needs to be selected. This lab required the class to use the polynomial method in order to perform the geometric correction. The aerial image of Chicago only is slightly distorted so only a first order polynomial was required to perform the geometric correction. Images with larger distortion require larger order polynomials to perform the correction properly.
At this point GCPs were set going back and forth between the distorted image and the reference image. The GCPs were set in areas that could be easily identified in both images (Figure 2). After the GCPs were set it was important to check the RMS error (Root Mean Square). This is a value that represents the distance between the input location of a GCP and the re-transformed location for that same GCP in the rectified image. Typically an RMS error of less than .5 is desired but in this case only a GCP less than 2 was required. Once the RMS error is less than 2 (Figure 3), the image can be resampled. This resampled image is a geometrically corrected image of the original distorted image (Figure 4).
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| The GCPs were set in locations that were easily identifiable in both images so they could be set in the same geographic locations. In this case GCP #1 was set in the corner of what appears to be a harbor area as the feature stood out well in both images. (Figure 2) |
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| The GCP properties are shown below the two images. The RMS error of each GCP is displayed here as well. Though the toal RMS error is displayed in a different location at the bottom right of the screen. A minimum of only three control points are required for first order polynomials. (Figure 3) |
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| The final, resampled image should be geometrically correct if GCPs are placed well and the RMS error is low. This is the geometrically corrected aerial image of Chicago which was the output of the first part of the lab. (Figure 4) |
Image-to-Image Rectification:
The second portion of the lab required going through the same process as the first portion. However, this time the image was much more distorted and instead of using a reference map to perform the geometric correction, a previously corrected aerial image was used. The two aerial images given were of an area in Sierra Leone. When placed in the same viewer and viewed using the Swipe tool it can be seen that the image that hasn't been geometrically corrected is extremely distorted in comparison with the corrected image (Figure 5). Due to this a higher order polynomial was required when performing the geometric correction, which in turn, required more GCPs.
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| The two images viewed together using the swipe tool to see them both clearly shows that the images don't match up geometrically. One of the images is distorted by a large amount and needed to be geometrically rectified using the previously rectified image. (Figure 5) |
As the distorted image had a large amount of distortion, as previously mentioned, a third order polynomial was required to rectify the image. A third order polynomial requires a minimum of ten GCPs, however, it took twelve GCPs until ERDAS would allow the resampling tool to be run in this case. These twelve GCPs were placed all over the two images in strategic places that were spaced out far enough and were easily seen in both images (Figure 6). After the GCPs were placed, they all had to be tweaked slightly to get the total RMS error down to below .5. This was a painstaking process that required precision with GCP placement. After this, the resampling was run and a rectified, output image was given (Figure 7).
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| The GCPs can be seen scattered throughout the images, and their properties can be seen below the images. The total RMS error can be seen in the bottom right hand corner. Slight re-positioning of the GCPs had to be performed in order to achieve a total RMS error of less than .5. At this point the image is ready to be resampled and have a rectified image output. (Figure 6) |
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| The original rectified image and the output rectified image are shown together here using the swipe tool. These images are almost geometrically exact unlike the images in Figure 5. (Figure 7) |
Conclusion:
Geometric correction is a majorly important aspect of learning how to properly use remote sensing in almost any situation. If an image isn't geometrically sound, analysis and data extraction can't be properly performed. The first step in many cases is to ensure the images that are being analyzed are geometrically accurate or have been geometrically rectified. This lab introduced the class to two common methods of geometrically correcting images: Image-to-map rectification and image-to-image rectification. It also taught the class how to properly place ground control points to ensure the most accurate rectification possible.
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