Remote Sensing Lab 6

Goals

This lab is designed to introduce us to a very important image preprocessing exercise known as geometric correction. The lab is structured to develop our skills on the two major types of geometric correction that are normally performed on satellite images as part of the preprocessing activities prior to the extraction of biophysical and sociocultural information from satellite images. 

Methods

The first part of this lab was dealing with image-to-map rectification. We started off in Erdas Imagine and were looking at the Chicago_drg.img image. We compared it to the Chicago_2000.img. We needed to use the first order polynomial equation to geometrically correct the Chicago_2000.img. We used the Multipoint Geometric Correction window to place GCPs on both maps. Because this was a first order, we only needed to use four GCPs. Once all the points were placed, we needed to make slight adjustments to their placements so that we could lower our Root Mean Square error. For this part, we were just supposed to reduce it below 2.0. The image below is the comparison between the original image and the corrected image. 

In par two of the lab, we did a similar exercise. We were using image to image registration. We were working with two different images of Sierra Leone. One of the images had some pretty serious distortion that needed to be fixed. We did the same process where we opened the Multipoint Geometric Correction window. The difference here was that we changed it to a 3rd order polynomial. This meant that in order for it to work, we needed to have at least ten GCPs on each map. Because of this, when we finished plotting points, our RMS error was very high. We had to work with all the points and adjust them ever so slightly  in order to achieve an RMS error below 1.0. I was actually able to get my RMS error to .0125 which I felt pretty good about. The image below is the screen shot of both images with their GCPs in place. 

Results

From this lab, we learned how to deal with some of the distortion that we might find in images that we are working with. Although this was a shorter lab compared to our other ones, geometric correction is a very important tool when working with remote sensing images. This lab helped us work through and develop more accurate images.