Geometric Correction

Goal and Background:

The goal of this exercise is to show my ability to use two different types of geometric correction techniques.

Methods:
Part 1: Image-to-map rectification
First I opened a satellite image of an area of Chicago and a map of the same area in Edras Imagine. I then used the Multispectral Control Points tool, using a polynomial model and collected my GCPs from the map image. I used the Create GCP tool to place my GCPs on the image and the map layer until I had four GCPs. I then adjusted my GCPs until my Control Point Error (Total) was less than 2.0. I then used the Display Resample Image Dialog tool to create my adjusted image, leaving the default settings the same. 

Part 2: Image to image registration
I opened a distorted image of Sierra Leone and a reference image in Edras Imagine.  I then used the Multispectral Control Points tool, using a polynomial model, changing the polynomial order to 3, and collected my GCPs from the reference image. I used the Create GCP tool to place my GCPs on the image and the map layer until I had 12 GCPs. I then adjusted my GCPs until my Control Point Error (Total) was less than 1.0. I then used the Display Resample Image Dialog tool to create my adjusted image, changing the resampling method to Bilinear Interpolation. 
 
Results:

Figure 1. This image demonstrates the GCPs I placed in part 1, with a total RMS Error of 1.6068.

Figure 2. This image demonstrates the results from part 1, and shows my resampled image based on image to map rectification that I conducted. The new image is much closer to how the map appeared and the distortion of many of the features has been lessened. 

Figure 3. This image demonstrates the GCPs I placed in part 2, with a total RMS Error of 0.5887.

Figure 4. This image demonstrates the results from part 2, and shows my resampled image based on image to image registration that I conducted. The new image is much a lot less contrast due to the bilinear interpolation resampling method I used. It also still appears distorted which could be due to the amount of RMS error I still had among the placement of GCPs. 

Sources:  
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. 

Lidar Remote Sensing

Goal and Background:

The goal of this exercise was to show my ability to: processes and retrieve various surface and terrain models and  process and create an intensity image and other derivative products from a point cloud.

Methods:
Part 1: Point Cloud Visualization in Edras Imagine
I opened the LAS dataset with ArcMap and used the label manager and a shapefile to determine the tile position of the files within the dataset.
 

Part 2: Generate a LAS dataset and explore Lidar point clouds with ArcGIS
Section 1: Create Folder Connection
I used ArcCatalog to created a LAS dataset containing  the Eau Claire data files. I ensured that the correct statistics were with the files and looked at the metadata to assign the proper horizontal and vertical coordinate systems. 
 
Part 3: Generation of Lidar derivative products
Section 1: Deriving DSM and DTM products from point clouds
I used the LAS Dataset to Raster tool in the ArcToolbox to create my digital surface model of the first return by setting the points tool to color for elevation and the filter for first return. I used a Binning interpolation method with a natural neighbor void filling and a 2 meter cell size. I then created a hillshade of the DSM by using the 3D Analyst Tools in the ArcToolbox.

I then used the LAS Dataset to Raster tool in the ArcToolbox again, this time setting the filter to GROUND with the point tool show points colored for elevation to create the DTM. I used a Binning interpolation method with a natural neighbor void filling and a 2 meter cell size. I then created a hillshade of the DTM by using the 3D Analyst Tools in the ArcToolbox.  

Section 2: Deriving Lidar Intensity image from point cloud
I set the LAS dataset to Points and the filter to first return. I then used the LAS Dataset to Raster tool in the ArcToolbox, setting the value field to INTENSITY, the Binning cell assignment to AVERAGE, the void fill to natural neighbor and the cell size to 2 meters.

Results:

Figure 1. This image demonstrates the results from part 3, section 1 and shows the hillshade image of the DSM.

Figure 2. This image demonstrates the results from part 3, section 2 and shows the intensity image obtained from the Eau Claire LAS dataset. 

Sources:  
All data was provided by Dr. Wilson with Lab 5.