Lab 6: Geometric Correction

Goal and Background: 

The goal of this lab is to display the technique and skills to geometrically correct a satellite image.  Two types of geometric correction will be displayed, image-to-map and image-to-image.     Geometric correction is necessary because an image may be distorted or not collected in its proper planimetric position.  This results in an image that is skewed or objects appear different than they actually are.

Methods: 

In order to geometrically correct an image there must be a reference image that is already accurate.  The first step is to bring the correct reference image and the image to be corrected into Erdas.  Once both images are in Erdas in seperate viewers the process is ready to begin.  To correct the image the user must add control points to both images.  This is done by using the control points tool under the multispectral raster processing tools.  A number of windows then pop up to set the parameters.  the reference image must be selected again and the order of polynomial must be selected.  In the image-to-map example, first order polynomial was selected.  This means only three ground control points are necessary to correct the image.  When the parameters are set, a new window will open and the user is ready to add control points. Control points 1-3 must be manually added to both images in the same exact spot.  After 3 are added it will say "model solution is current" and control points will automatically be added on the second image when inserted in the first.  After all the points are added, some fine tuning of the points is required to lower the root mean square error (RMS error).  The lower the RMS value the more accurate the image will be.
For the image-to-image geometric correction, the same process was used but this time was performed using a third order polynomial.  This mean ten control points are necessary to correct the image.

Results:

Image-to-Map Geometric Correction Control Points

When adding control points it is important to zoom in when adding each point to match the exact pixel that the point is being placed in both images. The Image on the left is the image that is being corrected and it was corrected to within .4 of a pixel to the reference map.

Image-to-Image Geometric Correction Control Points

A total of 12 ground control points were placed in the above photo.  10 GCP's are necessary in order to correct a third order polynomial correction, but 12 were added to ensure even more accuracy.  The RMS error value in the bottom right of the image is .001 and is extremely accurate but not perfect.  

Conclusion:

It is necessary to geometrically correct an image before using it for analysis.  Erdas makes this process user friendly and easy for everyone to do in a timely fashion.  The resulting image will then be ready to take place in research or further analysis.  This is a simple tool that should be used every time a new image is being used in a workplace.  

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

Goals and Background:

The goal of this lab is to be introduced to working with LiDAR data structure and processing.  Surface and terrain models along with an intensity image will be created from LiDAR point clouds.  Working with LiDAR data and the LAS file format is important because it is a growing field in the industry and is an important skill set to have.  

Methods:

To begin working with LiDAR data, a LAS dataset was created in Arccatalog from a file of point cloud data.  This dataset was then imported into Arcmap.  Using the metadata from the point cloud data the LAS dataset was assigned a horizontal and a vertical coordinate system.  Using the LAS toolbar in Arcmap, filters, and other options can be manipulated to view the data in different ways.  Elevation, aspect, slope and contour are all options that the data can be viewed in.  An interesting tool that is available in Arcmap is the LAS dataset profile view tool.  This allows the user to select a area of interest and view it like they were standing on the ground next to it.  This example is looking at a bridge. 

LAS Dataset Profile View Tool

Next a Digital Surface Model and a Digital Terrain Model were created from point clouds.  To do this the LAS dataset was converted into a raster in Arcmap by using the LAS dataset to raster tool and using the maximum value as the input value.  This raster image was then turned into a digital surface model by running it through the hillshade tool.  This created a surface model that includes buildings and vegetation. 

Digital Surface Model

To create the digital terrain model the same thing was done with the point cloud data except this time minimum was used as the input value to make the raster image.  This made the image use the data that was closest to the surface of the earth. This digital terrain model is of the ground in the area of interest. 

Digital Terrain Model

The last image that was created from the LiDAR data was an intensity image.  This was done using the same LAS dataset to raster tool but this time using intensity as the value.  This image is like a optical image but without the visible bands. 

Intensity Image

Results:

 The resulting images that were created can be used in a variety of different ways.  The digital terrain and surface model can be overlaid on each other and a swipe tool can be used to look at them at the same time.

Swipe tool in Arcmap

This can be useful if the effects of water drainage and water flow wanted to be examined.  The user could look at the terrain model and determine where the water would flow without any vegetation or buildings.  The surface model could then be looked at to see how it would influence the water after the general flow from the terrain was already determined.  This could be very useful when trying to plan emergency water drains for the city.  

Sources:

Eau Claire County. (2013).

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

Lab 4

Goal and Background:

The goal of this lab is to show how to delineate a study area from a larger satellite image, demonstrate how spatial resolution of images can be optimized for interpretation, use radiometric enhancement techniques in optical images, linking a satellite image to Google Earth, explore various methods of resampling satellite images, image mosaicking, and use simple geographical modeling to detect binary change.

Methods:

Image Subsetting:

Using ERDAS Imagine 2016, subsetting an image is simple.  After the image is brought in, simply right click, use the inquire box tool, set the box over the desired area of the image, then use the subset and chip set of tools to create subset image.  This opens a new window that lets the user save the subsetted image to a new file.  Simply click "from inquire box" and okay and a new image is created from what subset. If a square is not the right shape for the area that is being subset, a shape file can be imported and used as the area of interest.


Image Fusion:

This can be used to pan sharpen images and give them higher resolution.  To pan sharpen the resolution merge function can be utilized to combine a reflective band image with a panchromatic band image to create a higher resolution image.  Both images are input to the function, the user names the output file and selects the method and resampling technique and then runs the function.  The resulting image will have a better resolution.


Simple Radiometric Enhancement Techniques:

Haze reduction helps an image look crisper and makes it easier to view. Using the haze reduction tool under the raster settings, the user inputs the image that they want the haze to be reduces, name an output file name and click run and the tool reduces the haze in the image.

Linking Image Viewer to Google Earth:

Linking the ERDAS viewer to google earth is a good way to get another view of the image being examined.  ERDAS has a google earth function that makes this very easy.  All the user has to do is click the Google Earth tab at the top and click connect to Google Earth.  This will open a new window and then can be open side by side.  ERDAS and Google Earth can be matched and linked by clicking, match GE to view and then syncing them together.  Google Earth has very high resolution and labels making it useful to identifying objects being examined.

Resampling:

Resampling refers to changing the size of pixels in the image.  This can be useful if the user wants to run a function where two images need the same pixel size.  To do this the user goes to spatial under the raster tab, then clicks on the resample pixel size tool.  This opens up a new window where the input and output and new pixel size can be entered.  The user then clicks ok and the program is ran, changing the pixel size.

Image Mosaicking:

Image mosaicking is used when the area of interest cannot be contained in one satellite image.  Mosaicking is used to bring together two separate pictures into one image.  Mosaic pro is a program in ERDAS that makes mosaicking easy and does the job well.

Binary Change Detection:

Binary change detection is used to determine if two images have changed.  This is useful when comparing an area from 1991 to the same area in 2011.  In this section of the lab the model maker function was utilized to create an image from two images and determine what changed in that time.  The new image was then overlaid on a whole image in ArcMap and the changes in the images were then clearly visible.




Results:

Subset Image

Pansharpened Image

Google Earth Sync View

Mosaicked Image

Binary Change Detection Overlay