Remote Sensing Lab 6

Remote Sensing Lab 6 

Goals and Background:

This lab exercise seeks to teach the user about the process of geometric correction. Geometric correction is the important task of correcting the location of pixels in an image, which is often the start of major Remote Sensing projects. Two types of geometric correction will be covered: that of image to map rectification and image to image registration.

Methods:

Part 1: Image to Map Rectification 

The first method we will cover is that of image to map rectification. This method involves creating and matching a series of GCPs (ground control points) to a reference map. For this exercise, the image to be corrected will be of Chicago.

We will accomplish the actual correction through the Geometric Correction interface, which is accessed through the Control Points option under the Multispectral tab. For this correction, we will use a Polynomial Geometric Model of the 1st order. This will only require 3 GCPs for the correction to be successful, though we will add an additional 4th one for increased accuracy. 

GCPs are first placed on the reference image, and then placed in the related location on the reference map, linking the two points. Once the threshold number of GCPs is reached for the polynomial order the user specified (in this case 3), the GCPs only need to be placed on the input image, as the program will place the related reference map point automatically.  

figure 1: The input image with the 4 GCPs added.

Another important part of geometric correction is reducing the margin of error. This is achieved through lowering the total Root Mean Square (RMS) error.  Though the ideal standard for total RMS is below 0.5, for now we'll accept under 2. RMS can be lowered by adjusting the location of the input image GCPs to better match those on the reference map. 

After the correct number of GCPs have been placed and RMS is at a satisfactory level, we can now perform the actual correction. For the resampling method, we will use the Nearest Neighbor method. 

figure 2: The Display Resample Image Dialog, with the Nearest Neighbor option selected.

Part 2: Image to Image Rectification 

For the second part of the exercise, we will perform an image to image rectification. This includes the same process as before, however this time instead of a reference map we will use another already geometrically correct image as reference. Our input image is of an area in Sierra Leone. 

For this correction, we will use a polynomial order of 3 rather than 1. This will require 10 GCPs instead of 3, but will result a more accurate correction. For additional accuracy, we will add a total of 12. Since we attempting a more accurate correction, we will also reduce the total RMS to under 0.5. 

In addition, we will use the Bilinear Interpolation resampling method. Though this is more computationally expensive, it will contribute to a more accurate output. 

figure 3: The Display Resample Image Dialog, with the Bilinear Interpolation option selected.

Results: 

figure 4:  The image for Image to Map Rectification. The 4 GCPs have been added and dispersed throughout the map for the best possible result. RMS total error has been reduced to below 1, which will create an accurate output image.

figure 5: The image for Image to Image Registration (input image on the left). The 12 GCPs have been added and dispersed throughout the map for the best possible result. RMS total error has been reduced to below 0.5, which will create a very accurate output image. 

Sources:

Illinois Geospatial Data Clearinghouse. (n.d.). Retrieved April 28, 2019, from https://clearinghouse.isgs.illinois.edu/

Earth Resources Observation and Science (EROS) Center. (n.d.). Retrieved April 28, 2019, from https://www.usgs.gov/centers/eros