Lab 6: Geometric Correction
Shelby Short
Goal and Background:
The goal of this lab was to gain understanding in image preprocessing, specifically by using geometric correction. This lab explores two major types of geometric correction such as image-to-map rectification. These methods are normally performed on satellite images as a method of preprocessing before looking at the biophysical and sociocultural data that can be extracted from the given satellite images(Wilson, 2020).
To complete this lab we used a United States Geological Survey (USGS) 7.5-minute digital raster graphic (DRG) image of the Chicago Metropolitan Statistical Area and adjacent regions to correct a Landsat TM image of the same AOI. Next, we collected ground control points (GCPs) from the USGS 7.5 minutes DRG and used it to rectify the TM image. Finally, we used a corrected Landsat TM image for eastern Sierra Leone to rectify a geometrically distorted image of the same AOI(Wilson, 2020).
Methods and Analysis:
The first part of this lab looks at image- to - map rectification. In order to perform this, we use a multispectral raster processing tool of Control Points which allows us to start the geometric correction. After selecting the default reference setup and we change to polynomial map properties and accept the given properties. Then, we deleted the previous entered control points. At this point, we are ready to perform multipoint geometric corrections(Figure 1). Now, we are ready to add our Ground Control Points (GCPs) by using the create GCP tool. We added 4 points to start off with(Figure 2). After spending time adjusting the control points so there is minimal error, we are ready to save our image to our output folder so we can perform the geometric correction.
Figure 1: Multipoint Geometric Corrections with the left pane showing our imput (Chicago_2000.img) image and the right pane showing the reference image(Chicago_drg.img). The second part of this lab has to do with image to image registration. We were given an image(e sierra_leone_east1991.img) that was highly distorted. To visualize these differences we used the swipe tool(Figure 3). To fix this we will add control points as we did in the first part of the lab. We are using the polynomial to start the geometric correction utility. we increase the polynomial to a third order polynomial to due the extensive distortion. We added 12 control points with a total RMS error less than 1(Figure 4). Finally we correct our image to get the final image(Figure 5).
Figure 3: Swipe tool that shows the mismatch between the top reference image and the bottom distorted image.
Figure 4: Total of 12 control points added between the two images.
Figure 5: Final Corrected Image.
Results and Knowledge Gained:
After completing this lab I ended up with two corrected images, one og Chicago and another from Sierra Leone. No matter the distortion in my original points, ground control points were used to fix the spatial errors.
Data Source:
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.
Wilson, C. (2020) Geometric Correction, LAB 6, GEOG 338: Remote sensing of the Environment (pp. 1-16).
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