Lab 6: Geometric Correction

Goal:

The purpose of this lab is to gain experience with different remote sensing concepts and georeference a raster image of the Chicago Metropolitan Statistical Area and Sierra Leone.

Methodology:

Part 1:

I began by adding two images to two separate views in ERDAS imagine. I then used a polynomial equation in the software to geometrically correct the image. To do so I first had to delete the current ground control points of the image. I then added four ground control points. There are certain parameters to follow when choosing these points. First, it needs to be something that is non-changing and has a defined start and end point. Often the curb on a corner or the corner of a roof are good locations. Natural things such as river connections are not good because they can change over time.  Secondly, it is good to have them along the outer edges of the image and scattered.

Once finished, I then calculated the root mean square (RMS) error. This tells me how accurate the georeferenced image is. At first the RMS was very high. I then zoomed into the points and made them more exact on both images until the RMS was less than 2 (Figure 1).

      Figure 1 Screenshot showing a RMS of 0.0002 

Part 2:

For this section I added two images of Sierra Leone. I will be rectifying one of them and the other is used as a reference. I followed the same methods as in part one to fix the images coordinate system. I set the GCPs and then calculated the RMS. I then moved the GCPs to a more accurate location on both images until the RMS was below .1. My RMS is seen below.

Figure 2 Screen shot showing an RMs of less than .1

 Results:

These are the following fixed images created through geometric correction. Figure 3 was created with a nearest neighbor resampling method. While figure 4 used a bilinear resampling method.

Figure 3 part I final image

figure 4 part II final image

Conclusion:

This lab helped me gain experience created a referenced image with the use of geometric correction techniques. It also helped me understand the various methods and techniques that can be used during this process and helped to understand some of the reasons behind each one.

References:

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.

Lab 5: LiDAR Remote Sensing

Goal:

The purpose of this lab was to expose us to some basic components of LiDAR processing and data structure. Also, we will learn strategies of working with Lidar point clouds in LAS file format.

Methodology:

Part 1: Point cloud visualization in Erdas Imagine

 In this section, I added all of the point cloud data into an empty viewer in Erdas (Figure 1). A very important aspect of working with LAS data is to make sure you have the title index along with the metadata. I then used ArcMap to insert the tile index of the files just added to Erdas. I then used the “select by attributes” tool to find a tile.

Figure 1 LAS point clouds in Erdas

 Part 2: Generate a LAS dataset and explore lidar point clouds with ArcGIS

From this point on, the lab will completed in ArcMap because the processing of point clouds is easier. Tasks to be completed for this part included…

• Create a LAS dataset
• Explore the properties of LAS dataset
• Visualize the LAS dataset as point cloud in 2D and 3D

 

I began by bringing the LAS file from the class lab folder into my lab 5 folder and then created a folder connection with my Lab 5 folder in my Q-drive. I then navigated to this LAS file in arccatalog and created a new LAS dataset entitled “Eau_Claire_City”. I then added the same files added to Erdas in part 1 to the new dataset properties. Here, you can view the various data components for each file (figure 2).

Figure 2 LAS Dataset Properties window after adding the LAS files

In the same window, I went to the statistics page and ran the calculation of statistics for the entire dataset, however, the statistics for each individual file can also be viewed. Statistics are important to show quality assurance/ quality control (QA/QC) of both LAS files and datasets. To determine the data quality of our dataset I compared the maximum and minimum elevation of the data to the real world. Turns out, the range of elevation is not accurate in the dataset because the dataset does not have an assigned coordinate system.

 

Next, I assigned a coordinate system. First, I had to check the lab metadata folder for any information that would help selecting the coordinate system. I then assign both a vertical and horizontal coordinate system by entering the XY Coordinate System tab and navigated to the correct one.

                                                Horizontal: NAD_1983_HARN_WISCRS_EauClaire_County_Feet

                                                Vertical: NAVD_1988_Foot_US

 

 

 

 

From here, I then added the new dataset to ArcMap. To ensure the correct coordinate system was assigned to the dataset I added a shapefile of Eau Claire. Next, I examined the points themselves. The mad has them color-coded by elevation range (Figure 3). In some cases, there was little data collected which resulted in areas of “flat surfaces” when in reality it was a peak. This could be caused by the lack of sunlight hitting the far side of the peak therefore the sensor does not have the ability to collect data.  

 

                     

 

 Figure 3 Point elevation color code