Geometric Correction

Goals and Introduction

The goal of this lab was to learn a few ways to correct an image by looking at a reference image. We specifically learned Image to map rectification and image to image registration. To do this Ground Control Points (GCP's) were added to the images to correct them. This process is explained more below.

Methods

This lab started by creating a lab 6 folder within our personal folder where we would save any and all outputs. To start the image to map rectification we first added the Chicago_drg.img in one viewer and the Chicago_2000.img image in another viewer side by side. Next I clicked on Multispectral to activate the raster tools and then clicked on control points. This opened the "Set Geometric Model" where I then clicked Polynomial. The default was then accepted on the Image Layer. The reference image Chicago_drg.img was then added where I clicked okay for the Reference Map Information dialog. The Chicago_2000.img was also added to be corrected. Since we performed a 1st order polynomial we only needed to add three GCP's but added four to make sure the image would correctly rectify.

Part 1

I clicked on the GCP tool and added a GCP on the input image (Chicago_2000.img) and then added a GCP in the same exact place on the reference image (Chicago_drg.img). I did this exact same thing for the next two GCP's. This changed the status of the image from "model has no solution" to "model solution is current." The fourth GCP was added to only one image and it automatically placed the corresponding GCP in the other image.

Next the GCP's were evaluated, this is done by Root Mean Square (RMS) Error. The ideal RMS error should be 0.5 or below. Currently at this time the RMS error was well above 2.0. To fix this, each ground control point was zoomed into and then carefully moved around while watching the RMS Error. The goal was to get the total RMS Error value below 2.0 (since we were beginners for this lab). This was done for each GCP until the total RMS error was below 2.0.

Now the geometric correction is ready to be done on the image. The "compute transformation matrix" has already been computed from the Ground Control Points. I then clicked the "Display Resample Image Dialog" button and the image was saved in my lab 6 folder as Chicago_2000gcr.img. All parameters were left as they were and the geometric correction output ran. I did not save the points on either image as we were told to not to save them.

Part 2

This part of the lab was image to image registration and was also done in Erdas Imagine. I worked with the sierra_leone_east1991.img which had serious geometric distortion. We looked at the distortion by overlaying another image named sl_reference_image.img and then activated the swipe tool to compare images and just see how much distortion there really was. After looking at the distortion I began to fix it by bringing in the distorted image (sierra_leone_east1991.img) and in the second viewer I brought in the reference image (sl_reference_image.img).

Once again I clicked on Multispectral to activate the raster tools and then clicked Control Points. I then clicked on polynomial in the set geometric model and then OK on the Collect Reference Points From in the GCP Tool Reference Setup. Next I added the sl_reference_image.img and clicked OK on the Reference Map Information and then on the Polynomial Model Properties I changed the Polynomial order to 3 and clicked close.

Now that it was set up for the GCP's I clicked the "Create GCP" tool. I added a GCP first to the input image (sierra_leone_east1991.img) and then added a GCP to the same exact place on the reference image (sl_reference_image.img). I continued this process for points 2 through 9. After adding 9 points the model still stated "Model has no solution" so three more points were added. I then moved and repositioned the Ground Control Points until the RMS error was less than 1.0.

Next I clicked the "Display Resample Image Dialog" button on the Multipoint Geometric Correction tools bar. The output image was saved as sl_east_gcc.img in my lab 6 folder. I then changed the resample method in the resample image window to bilinear interpolation, the defaults were accepted and the geometric correction output was executed. No points were saved for either of the images.

Results

Figure 1: First image corrected with 4 Ground Control Points

Figure 2: Showing the Total RMS error is 1.33 (less than 2.0)

Figure 3: Second image corrected with 12 Ground Control Points

Figure 4: Showing the total RMS error of this image was 0.9099 (less than 1.0)

Lab 5: LiDAR remote sensing

Goals and Background

The goal of this lab was to become familiar with Lidar images and performing various tasks with these images including:

• Becoming familiar with point cloud visualization
• Retrieval of surface/terrain models
• Processing intensity images

Methods

The first part of this lab was to look at the lidar point cloud image of Eau Claire. We first had to do this by opening ERDAS Imagine and adding all the .las files from the folder Lab 5/Las. It opened an image full of dots of varying color indicating elevation. We then opened ArcMap and loaded the shapefile QuaterSections_1 which helped us find the location of certain tiles in ERDAS. This first task was to have us become familiar with what a point cloud was.

The next part of the lab was to was to generate a LAS dataset. For this part we worked in ArcMap and created a new LAS dataset within the geodatabase. We then added all of the .las files and looked over the statistics of the data. It was possible to view the LAS data statistics as a whole but also for each individual LAS file. After looking over the statistics we then gave the LAS dataset a coordinate system of NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet). This is the exact coordinate system that the data was collected in. For the vertical coordinate system we chose NAVD 1988 US feet. The data was then displayed in ArcMap as a grid, the point cloud data appeared when you zoomed in on the data. The data displayed as points, color coded by the elevation. The data could be switched between elevation, aspect, slope and contour where each could help display the data in a different way for a different purpose.

After having the point cloud set up in ArcMap we explored the point cloud according to class, return and profile. To do this we set the data to elevation and first return. Next we clicked the LAS Dataset Profile Tool from the LAS toolbar and viewed one of the bridges of Eau Claire as a 3D point cloud. The data was very accurate and it appeared that we were looking at the bridge in real life.

The next task was to generate LiDAR derivative products. The raster derivative products were developed at 2 meters spatial resolution to reduce computation time. We created four derivative products:

• Digital surface model (DSM) with first return
• Digital terrain model (DTM)
• Hillshade of the DSM
• Hillshade of the DTM

To start the first derivative product we set the display of the LAS dataset to points color by elevation and the filter to first returns. Next we opened the tool LAS Dataset to Raster, the parameters were set as follows:

• Value Field: Elevation
• Cell Type: Maximum
• Void Filling: Natural Neighbor
• Sampling type: Cellsize
• Sampling Value field: 6.56168 feet (approximately 2 meters)

Once the tool ran the dataset was displayed as a typical elevation model ranging from light gray to dark gray (higher elevation to lower elevation). Next we developed a hillshade for the DSM. To do this we opened the hillshade tool. The tool ran and was added to ArcMap. Below is the result of the hillshade of the first derivative product in the results section labeled figure 1. Next we derived a digital terrain model (DTM) from the lidar point cloud. To do this we set the filter to ground and had the raster display as dots color coded by elevation. We then ran the Dataset to Raster tool once again and used the following parameters:

• Interpolation: Binning
• Cell Assignment Type: Minimum
• Void Fill Method: Natural Neighbor
• Sampling type: CellSize
• Sampling Value: 6.56168 (approximately 2 meters)

The tool ran and created the DTM, this is the study area without any of the above ground features, just the Earth's elevation. Next the hillshade of this DTM was created. Once this was created we could use the effects toolbar to look at the differences between the first return derivative product (figure 1) and the bare Earth DTM. By comparing them you could see that the bare Earth DTM had a lot less elevation changes, and you could see the footprints of where the buildings were supposed to be.

The final task was to generate a LiDAR intensity image. This followed much of the same procedure as creating the DSM and DTM. To start I set the LAS dataset to points and to filter by first return. Then I opened the LAS to Raster Tool and the following parameters were entered:

• Value Field: INTENSITY
• Binning Cell Assignment Type: Average
• Void Fill: Natural Neighbor
• Cell Size: 6.56168 (approximately 2 meters)

The intensity image is created but it was too dark for ArcMap so the image is then displayed in Erdas imagine as a .tiff.

Results

Figure 1: Hillshade of the first derivative product

Figure 2: LiDAR intensity image

Unfortunately with the LiDAR intensity image, even after it was brought into Erdas imagine it still remained a dark color.

Sources 

• Lidar point cloud and Tile Index are from Eau Claire County, 2013
• Eau Claire County Shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price, 2014