Lab 6: Geometric Correction

Background

Geometric correction is essential to the creation of accurate images which help to create accurate results when using these images in the scientific field.  Geometric correction is correcting the pixels to be in the correct X and Y locations.  In this lab there are two basic ways to correct the image to accuretly represent the real world.  The first is image to map rectification and the other is image to image rectification.  An image can only be rectified from a perviously corrected image.  Therefore, if many corrections are needed then the original image used to correct an image must be corrected fro.  If not corrected for then the accuracy will be very low.

Goals:

To geometrically correct images using the image to map rectification and image to image rectification .
Methods

Image to map rectification

Given an area of interest in the Chicago area there were two images to use the image to map rectification.  This is done by adding ground control points (GCP) in ERDAS Imagine to use the Multipiont Geometric Correction window.

This first part was done using a first degree polynomial formula allowing us to only need to add 3 GCPs.  The first step was to add the two images into two different viewers within the program.  Then to select the Multipsectral tab and select control points.  Then a new winow appears which allows the polynomial selection to take place.  next is to select new layer and imput the DRG (Digital Raster Grahic) for the reference image and accept the default model properties.

 Now add the GCP's to the map.  This can be at splits in rivers or area that are more permanent.  In this lab we were given places to put the GCPs.  Once the requirement of three is met then the program will automatically place GCPS added to one image onto the other.

Now that all of the GCPS are placed the next step is to get the root mean square error (RMS) to under or below 2.  I was lucky enough to get mine down to .4.

Finally save the output image to a folder to bring into ERDAS to view the final product.

Figure 1: Image to Map Rectification (RMS: 0.4411)

Image to Image Rectification

Image to image rectification is done by using the same processes as above.  This time the images were from Sierra Leone, Africa.

The difference in the directions is that this is a third order polynomial that requires ten ground control points instead of three.

Also this one required to get a RMS error of less than 1, and then use the Display Resample Image tool was used again to get the results.

Figure 2: Image to Image Rectification.  The ground control points are in white.  An RMS  of .012 was used.  

Results

Geometric correction is a very important aspect for looking at aerial images or any images that need to be spatially correct.  It makes it more accuretly represent the Earth's features.  If this processes was taken out of the "norm" the images people would deal with would be so far off that none of the data would be accurate enough.  It is also important to understand the RMS error which tells the accuracy of the points.

Sources

Satellite images

Earth Resources Observation and Science Center, United States Geological Survey

Digital raster graphic (DRG)

Illinois Geospatial Data Clearing House

Lab 5: LiDAR Remote Sensing

Background

LiDAR data collection has become very popular due to its ability to be analyzed in many different data analysis.

Goals

• Produce Surface and Terrain Models
• Create intensity image and other raster (DSM and DTM) from a point cloud data set that was in LAS format

Methods

Creation of Surface and Terrain Models

1. Copy the LAS files into a person folder and create a new LAS datasets.
2. Add the LAS Files into the LAS dataset by clicking "Add Files" and make sure to use all of the files from the folder.
3. Use quality control to make sure the dataset makes sense.
4. Add the correct coordinate system to the X,Y system and Z coordinate systems.  This is found in the files properties and under XY Coordinate System and Z Coordinate System.
For this example XY was set to NAD 1983 HARN Wisconsin (US Feet) and the Z-Coordinate was set to NAVD 1988 US feet.

5.  The next step is to add the LAS data to ArcMap where only a grid will appear, and add basemap of the area in quetions to make sure the data is in the correct spot.  This is used to check the coordinate system.
6. Turn on the LAS Dataset tool bar in Arcmap, and turn on the 3-D analyst extension from the Customize dropdown tab to active this tool.
7.This allows us to see the different options in the LAS toolbar to learn about the various types of models that can be used to utilized this type of data.
8. Choose which type of model to create from the surface symbology render options which include elevation , aspect, slope, and contour.  In figure 1 I will be displaying Aspect, Slope, and Contour.
Another import option is the LAS dataset Profile View which allows the user to view the LiDAR point cloud in a 2-D view.  This creates a pop-up window to be used for easy viewing.  The next important option is the LAS dataset 3-D view.  This is used in viewing the Z-Vaules of the aspect image.

Figure 1: Showcasing Aspect, Slope, and Contour symbolizes LAS datafields from a section of Eau Claire, Wisconsin

There is also a button within the LAS Dataset tool bar that allows the user to see what part of the pointcloud is most important.  This is determined by elevation, class, or return which is based on their classification code, or based on the LiDAR pulse return number.

Using the LAS Dataview a Cross section can be used to see the Z-Values of the point cloud seen in Figure 2. There is also the 3-D view that gives a view of the cross section into 3-D.

Figure 2:  Cross section dataview of the point cloud. This is a 2-D view.

Creating an intensity image

This part of the lab was focused on deriving DSM and DTM products from point clouds.  The average nominal pulse spacing is critical to understanding the spatial resolution should be for the DSM and DTM output images. 

Digital surface model (DSM) with First return

To start this the LiDAR points need to be in elevation.  Then use the LAS dataset to RASTER tool to create the DSM.  Imput the Las file being used and then for the value field use elevation.  Set the interpolation type to binning, maximum and nearest neighbor. The sampling value was set to 6.56168 which is about 2m in feet.  This is becuase our data is 2m by 2m.  The rest of the values are set to deafult.

Digital Terrain Model (DTM)

To create the DTM the same first steps were used.  The only changes were to change the maximum to minimum in the interpolation.  All the other settings were the same except for that.

Hillshade of DSM and DTM

Creating a hillshade for the DSM and DTM help to create an elevation of the land surface.  to accomplish this 3D analysis needs to be turned on.  Now, to start the process search for the hillshade (3d analysis) tool.  Just enter the input raster and name the output to get a hilshade of the selected raster.  This is the same for both the DSM and the DTM.

Figure 3:  The left image is the Hillshade output derived from the DSM.  The image on the right was derived from the DTM.  The difference between these images are the DSM is the first return points compared to the image on the right that has the ground return points.  Therefore the right image will be a smoother surface to represent the ground.  

Intensity Image

To create our final maps the Dataset to Raster tool was used to create the intensity image. This was done by changing the data back to point symbology and changing the filter to First Return.  This time in the tool the value field was set to intensity instead of elevation.  The interpolation was also set to average instead of max or minimum.  The sampling value once again stayed the same to get that 2mx2m grid size.

Now after saving this raster we can bring it into ERDAS becuase ArcGIS only really shows a black or dark image.  Once it is brought into ERDAS by saving it as a .TIF the image can be seen to show the intensity.

Figure 4:  The image on the left is the final image in ERDAS where we can see the image has contrast to make out details.  The image on the right was created in ArcGIS which makes just a dark image that is very hard to see much of any details.  

Conclusion

Raster data is very useful when trying to understand LiDAR data.  It can be helpful to understand elevation and to just analyze raster data.  LiDAR is constantly increasing in popularity due to the amount of possibility that it has and potential.  This lab was helpful to get a foot in the door to understand how to manipulate and use point clouds.

Sources:

Eau Claire County. (2013).

Price, M. (2014). Mastering ArcGIS 6th Edition. Mastering ArcGIS 6th Edition Dataset [shapefile]. New York: McGraw Hill.