Lab 6

Goals and Background

The purpose of this lab is to introduce students to geometric correction using both Image-to-map and Image-to-image rectification.

Methods

To begin the lab imported an image of the Chicago area into Erdas Imagine. This image was distorted and needed to be corrected using image-to-map rectification. To do this I used the Control Points function under the Multispectral tools tab. This function allowed me to place Ground Control Points (GCPs) onto another image to improve its spatial accuracy. I chose to place the GCPs onto a reference map and perform a first order polynomial transformation. I then brought in the digital reference map and placed four GCPs spaced across the images, placing them at places such as road intersections. After adjusting the GCPs to make sure they had minimal error I ran the Display Resample Image tool that created my newly resampled using the nearest neighbor method.

The next section of the lab involved following a similar process to correct a spatial distorted image. Instead of using a reference map in the previous section I used an image of the same area. I brought in the image and used the Control Points function and adjusted the setting to create a third order polynomial transformation. I brought in the reference image and began to plot my GCPs. Because I was performing a third order polynomial transformation I needed to place a minimum of 9 GCPs. I plotted 12 to ensure that the new image would be more spatially accurate. Once the GCPs were adjusted, I resampled the image using Bilinear Interpolation, which made the image more spatial accurate but it did reduce the contrast in the newly created image.

Results

Image-to-map rectification

Image-to-image rectification

Resampled image using Bilinear Interpolation

Lab 5

Background and Goals

The objective of this lab was to be exposed to the processing and data structure of LiDar data. This included processing various surface and terrain models and the creation of intensity images and similar models using derivative products from point cloud data. Data was presented in LAS file format.

Methods

The first section lab was to create a LAS database in ArcMap using the LAS files in the class folder. Once I created the data base, I calculated the statistics of the data. The next process that I needed to complete to bring the data into ArcMap was to assign the data coordinate system for both XY and Z. To find the correct coordinate system for the data I looked in the metadata for the LAS files where I found the correct coordinate systems. For the XY (horizontal) coordinates the coordinate systems was D_North_American_1983 and the Z (vertical) was North American Vertical Datum of 1988.

I then imported the newly created LAS dataset into ArcMap (figure 1). To make sure the data was spatially located correctly, I overlaid the LAS data set with a shapefile that contained the outline of Eau Claire.  After verifying that the data was indeed correct I proceeded to the next section lab. 

The next section of the lab involved using the LAS to Raster and Raster Surface tools in ArcMap to create both Digital Surface (DSM) and Digital Terrain Models (DTM). To create the DSM, I first used the LAS Raster Tool to convert my LAS Dataset into a raster image where the the Raster Surface Tool could process it. In the LAS TO Raster Tool, I used the elevation field, set the cell assignment to Maximum, and Void Fill Method to natural neighbor. Changed the cell size to 6.56 (~2 meters). Once the raster tool had processed the image, I brought this new image into a blank new ArcMap browser, where I ran the Hillshade Raster Surface tool to create the DSM image (Figure 2).

To create the DTM model I filtered the original LAS data set to only show the Ground category and used the LAS TO Raster Tool again used similar parameters as used to create the DSM image. I set the Interpolation method to Binning, Cell Assignment Type to Minimum, Void Fill Method to Natural Neighbor, and Sampling Cell value to 6.56 feet. I then used the Raster Surface Tool and processed the image using the Hillshade option.  Where I created the image in (Figure 3). 

The DTM model shows the information only at the ground level and excludes surface features such as buildings and vegetation. DSM models are useful for identifying surface features and determining spatial relationships between them. DTM models are better for studying the shape and topology of the actual bare surface.

I then created an Intensity Image from the LAS data. Intensity Images measure the highest voltage captured by the sensor. This can be used to aid in identifying classified Lidar data. To create this model, I filtered the LAS Dataset to First Return. I then used the LAS To Rater Tool set the Value Field to Intensity, Binning Assignment to Average, Void Fill to Natural Neighbor, and Cell Size to 6.56 feet. Once the Image was finished processing I converted the image to a TIFF file where I could then open it in Erdas Imagine (Figure 4). 

Results

Figure 1 Point Cloud

Figure 2 Digital Surface Model

Figure 3 Digital Terian Model

Figure 4 Image Intensity Model

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 MArgret Price, 2014.