Goal and Background:
The goal of this exercise is to show my ability to use two different types of geometric correction techniques.
Methods:
Part 1: Image-to-map rectification
First I opened a satellite image of an area of Chicago and a map of the same area in Edras Imagine. I then used the Multispectral Control Points tool, using a polynomial model and collected my GCPs from the map image. I used the Create GCP tool to place my GCPs on the image and the map layer until I had four GCPs. I then adjusted my GCPs until my Control Point Error (Total) was less than 2.0. I then used the Display Resample Image Dialog tool to create my adjusted image, leaving the default settings the same.
Part 2: Image to image registration
I opened a distorted image of Sierra Leone and a reference image in Edras Imagine. I then used the Multispectral Control Points tool, using a polynomial model, changing the polynomial order to 3, and collected my GCPs from the reference image. I used the Create GCP tool to place my GCPs on the image and the map layer until I had 12 GCPs. I then adjusted my GCPs until my Control Point Error (Total) was less than 1.0. I then used the Display Resample Image Dialog tool to create my adjusted image, changing the resampling method to Bilinear Interpolation.
Results:
Figure 1. This image demonstrates the GCPs I placed in part 1, with a total RMS Error of 1.6068.
Figure 2. This image demonstrates the results from part 1, and shows my resampled image based on image to map rectification that I conducted. The new image is much closer to how the map appeared and the distortion of many of the features has been lessened.
Figure 3. This image demonstrates the GCPs I placed in part 2, with a total RMS Error of 0.5887.
Figure 4. This image demonstrates the results from part 2, and shows my resampled image based on image to image registration that I conducted. The new image is much a lot less contrast due to the bilinear interpolation resampling method I used. It also still appears distorted which could be due to the amount of RMS error I still had among the placement of GCPs.
I opened a distorted image of Sierra Leone and a reference image in Edras Imagine. I then used the Multispectral Control Points tool, using a polynomial model, changing the polynomial order to 3, and collected my GCPs from the reference image. I used the Create GCP tool to place my GCPs on the image and the map layer until I had 12 GCPs. I then adjusted my GCPs until my Control Point Error (Total) was less than 1.0. I then used the Display Resample Image Dialog tool to create my adjusted image, changing the resampling method to Bilinear Interpolation.
Results:
Figure 1. This image demonstrates the GCPs I placed in part 1, with a total RMS Error of 1.6068.
Figure 2. This image demonstrates the results from part 1, and shows my resampled image based on image to map rectification that I conducted. The new image is much closer to how the map appeared and the distortion of many of the features has been lessened.
Figure 3. This image demonstrates the GCPs I placed in part 2, with a total RMS Error of 0.5887.
Figure 4. This image demonstrates the results from part 2, and shows my resampled image based on image to image registration that I conducted. The new image is much a lot less contrast due to the bilinear interpolation resampling method I used. It also still appears distorted which could be due to the amount of RMS error I still had among the placement of GCPs.
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.
No comments:
Post a Comment