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Lab 6: Geometric Correction

Goals and Background:

The goal of this lab was to practice performing geometric correction on digital images. In this lab two types of geometric correction were performed. The following is terminology used throughout this lab

Image to Map Rectification- This is when an images data pixel coordinates are transformed, or rectified using their counterpart coordinates on a map

Image to Image Rectification- This is similar to Image to Map Rectification, however instead of a map an already corrected reference image is used

Ground Control Points- Ground Control Points, or GCP's are paired measurement points used in geometric correction. The higher the distortion, the more GCPs are needed.

Spatial Interpolation- When GCP pairs are used to establish a transformation that corrects the pixel value in the output image using the value of a pixel in the input image

Intensity Interpolation - When Brightness values are extracted for an X,Y location in the reference image and place in the their approximate X,Y location in the output image

Root Mean Square (RMS) error: The difference in distance between a GCP's input location and location in the output image. Values of 0.5 and below are ideal

Polynomial model: A polynomial equation is fitted to GCPs to model corrections. A higher level of distortion means a higher degree polynomial is used.

 Methods: 

 The first part of the lab involved using a USGS 7.5 minute DRG image of the city of Chicago to correct a Landsat TM image of the same area. This process is called Image to Map Rectification. The image was corrected using a polynomial model. The degree of polynomial used is one because only 4 GCPs were used. The GCPs were placed in the same locations on the DRG and Landsat images and adjusted to get the lowest RMS error value possible. Below is the table showing RMS values of 0.1 and lower with a total value of 0.06


Figure 1: RMS error values
The image was then  resampled using the Display Resample Image tool under the Multipoint Geometric Correction toolbar.  During this resampling, intensity interpolation took place

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The second part of the lab focused on image to image rectification. In this process an extremely distorted image of a region in Sierra Leon, Africa was corrected using a 3rd degree polynomial. The higher degree polynomial is required because of the high level of distortion. In this geometric correction, twelve GCPs were used.

Figure 2: RMS error values for Image to Image rectification
All RMS error values for the GCPs fall within the threshold of 0.5 or less, with an overall error value of 0.2


Results: The results of this lab are two geometrically corrected images. Below are the images before and after geometric correction. 

Original Chicago Image


Geometrically Corrected Image

Original Sierra Leon Image


Geometrically Corrected Image






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