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. 

Miscellaneous Image Functions

Goal and Background:

The goal of this exercise was to show my ability to: delineate a study area from a larger satellite image scene, optimize the spatial resolution of images for visual interpretation purposes, use some radiometric enhancement techniques in optical images, link a satellite image to Google Earth, to use various methods of resampling satellite images, to explore image mosaicking, and to use binary change detection through the use of simple graphical modeling. 

Methods:
Part 1: Image Subsetting of a Study Area
Section 1: To take my first subset of the Eau Claire area, I implemented the raster tool, inquire box. I created an inquire box around my selected area and then used the Subset & Chip-Create Subset Image tool to create my subset image. 

Section 2: To take my second subset of the Eau Claire area, I added a shapefile of Eau Claire and Chippewa Counties to the viewer with my input image. I then created an area of interest around the shape files and saved the layer as an AOI file. Then I employed the raster tool Subset & Chip and used the new AOI file I created to create my subset image. 
  

Part 2: Image Fusion
I employed the raster pan sharpen tool Resolution merge to execute image fusion. I input both a panchromatic band image and a multispectral band image into the tool. I employed a the multiplicative method and the nearest neighbor resampling technique. 
 
Part 3: Simple Radiometric Fusion Techniques
I employed the raster radiometric tool Haze reduction on my input image to reduce the haze and clouds in the image.

Part 4: Linking Image Viewer to Google Earth 
With the input image in Edras Imagine, I connected to Google Earth through Edras. I then matched Google Earth to my view screen and synced the two displays to make a viable interpretation key out of Google Earth for my input image.  

Part 5: Resampling
I used the raster spatial tool, Resample Pixel Size on my input image. I repeated this tool twice, once using the Nearest Neighbor approach and once using the Bilinear Interpolation to see the differences that occur. Both times I resampled the image from 30x30 meters to 15x15 meters and ensured that I kept my pixels square.  

Part 6: Image Mosaicking
I imported my input images ensuring that Multiple Images in Virtual Mosaic and Background Transparent were set.

Section 1: Image mosaic with the use of Mosaic Express
I employed the raster mosaic tool, Mosaic Express by inputting my two images in the correct order and running the program. 

Section 2: Image mosaic with the use of MosaicPro
I employed the raster mosaic tool, MosaicPro by adding my two input images into the tool, ensuring that Compute Active Area was set as the default. I corrected the order of the images within the tool and adjusted the radiometric properties by selecting histogram  matching and overlap areas as the settings. I then ran the mosaic.  

Part 7: Binary Change Detection
Section 1: Creating a difference image
I employed the raster functions tool, Two Image Functions, inputting a 2011 and 1991 multispectral images of the Chippewa Valley area. I ran this tool and then used the histogram and metadata to determine the cutoff points for the values that have changed between those two years. I used the equation mean + 1.5 *standard deviation. 

Section 2: Mapping change pixels in difference image using spatial modeler
I used the following equation to create a model to remove the negative values in my difference image: 

ΔBVijk = BVijk(1) – BVijk(2) + c 

Where:

ΔBVijk = Change pixel values. 

ΔBVijk(1)= Brightness values of 2011 image. 

BVijk(2) = Brightness values of 1991 image. 

c = constant: 127 in this case. 

I = line number 

J = column number 

K= a single band of Landsat TM.

I then employed Model Maker to create a model using band 4 of my 1991 and 2011 images and the equation to create a difference image. I then determined the change threshold using the equation: mean + (3*standard deviation). I then created another model using my difference image and Either IF OR function. I input: 

EITHER 1 IF ($n1_ec_91 > 202.1) OR 0 OTHERWISE

After running the model I input my difference image into ArcMAP to make more legible map. 

Results:

Figure 1. This image demonstrates the results from part 1, section 1 and shows a subset of an image using the inquire box. 

Figure 2. This image demonstrates the results from part 1 section 2 and shows the results of subsetting an image using a shape file. 

Figure 3. This image demonstrates the results from Part 2, displaying both the input and pansharpened images. 

Figure 4. This image demonstrates the results from part 5, displaying both the input image and the result after resampling using the nearest neighbor method. 

Figure 5.  This image demonstrates the results from part 5, displaying both the input image and the result after resampling using the bilinear interpolation method. 

Figure 6. This image demonstrates the results from part 6, section 1 after using Mosaic Express. 

Figure 7. This image demonstrates the results from part 6 section 2 after using MosaicPro.

Figure 8. This histogram demonstrates the results from part 7 section 1 and displays the cutoff points where changes have occurred between 1991 and 2011.  

 

Figure 9. This map demonstrates the results from part 7 section 2, where areas of the Chippewa Valley have appeared to have changed within band 4 between 1991 and 2011. 

Sources:  
All data was provided by Dr. Wilson with Lab 4.