Lab 6 - Geometric Correction

In this lab, I explored different types of Geometric correction.  The first task deals with image-to-map rectification, using a USGS survey digital raster graphic image of Metropolitan Chicago to correct a Landsat TM image.  I also preformed map-to-map rectification on a distorted Landsat TM image, using a correctet Landsat TM image. 

In part one of the Lab I was given a distorted image that needed to be rectified using a reference map image.  I used a USGS 7.5 minute digital raster graphic map of the Chicago Metropolitan area, Chicago_drg.img, to correct the distortion on a Landsat TM image, Chicago_2000.img.  I rectified the Landsat TM image using Image-to-map rectification, through the placement of ground control points.

I preformed the image-to-map rectification using a first order polynomial model. This was done using the multipoint correction window in Erdas Imagine.  Below is a screan shot of the multipoint correction window with all of the GCPs placed on the distorted image and the reference image.  I was able to get the RMS error down below 2.0 to ensure that the correction was done properly.

The Chicago_drg.img serves as a reference map for preforming image-to-map rectification geometrically correcting the Chicago_2000.img to produce a planimetric image.  Ground control points were placed on both images in the same geographic positions in order to preform spatial interpolation, which uses the GCPs to rectify the pixel placement in the output image.  Chicago_drg.img is a digital raster graphic, which is a scanned topographic map used as a reference to the Landsat image Chicago_2000.img.The resampling dialog window is preforming spatial interpolation, which means it is using the ground control points found on both images to perform a geometric coordinate transformation used to rectify pixel location in the output image with the input image.  The resampling method is nearest neighbor.   The four points are spread across the image in order to make an accurate geometric correction.  The distortion in the image is not only in one specific area, it is spread throughout, so the ground control points need to be spread apart.    The first order polynomial model is used in this geometric correction because there were 4 ground control points collected.  A second order of transformation would require a minimum of 6 ground control points and a first order polynomial only requires 3. The minimum number of ground control points needed to perform a 1^st order polynomial transformation is 3.

In the second part of the lab I preformed image-to-image rectification.  I was given a distorted image of Sierra Leone, sierra_leone_east1991.img, and a correct Landsat TM image, sierra_leone_east1991grf.img, to use to geometrically correct the distorted image.  I used a third order polynomial to rectify the distorted image.  Below is a screen shot of the of the Multipoint geometric correction window showing the 12 GCPs that I placed throughout the image. I was able to get the RMS error below 1.0 to ensure proper correction.

   This reference image is in a horizontal coordinate system.  The projection is UTM (zone 29) and the datum is WGS 84. The minimum number of ground control points needed to perform a 3^rd order polynomial transformation.  The polynomial order was set to three in the polynomial model properties and the minimum ground control points needed to perform a third order polynomial transformation is 10, so this minimum must be reached in order for the model solution to be current.  Part one had a first order polynomial transformation, so it only required a minimum of 3 points.   The rectified image is far more geometrically correct compared to the reference image.  It is very apparent that there is much less distortion in the rectified image.  In this correction I used bilinear interpolation instead of nearest neighbor, which is the default setting. Bilinear interpolation is more spatially accurate than nearest neighbor and the output image appears smoother because bilinear interpolation uses the brightness values of the four closest pixels to calculate an output pixel and nearest neighbor only uses the brightness value of the closest input pixel.

Works Cited

 NASA Landsat Program, 2000, Landsat TM scene Chicago_2000.img, SLC-Off, USGS, Sioux Falls, 2000.

NASA Landsat Program, 1991, Landsat TM scene sierra_leone_east1991.img, SLC-Off, USGS, Sioux Falls, 2000.


NASA Landsat Program, 1991, Landsat TM scene Sierra_leone_east1991grf.img, SLC-Off, USGS, Sioux Falls, 2000.

 United States Geological Survey, 2000, digital raster graphic scene Chicago_drg.img, SLC-Off, USGS, Sioux Falls, 2000.