Geometric Correction

Goals and Background

         The purpose of Geometric Corrections lab is to develop skills using two major types of geometric corrections. These corrections are often performed on satellite images before processing in order to better the data quality and integrity. This process helps to align aerial images. Aerial images are rarely if ever perfectly inline due to factors such as the differences in altitude or the angles of the images. Rectification of an image is the process of changing a data file coordinate to a different coordinate system known as a reference system. The two forms of geometric correction are listed below.

1. Image-to-Map Rectification: Map coordinates systems are used to rectify the image data to the correct pixel coordinates.

2. Image-to-Image Rectification: Previously corrected images of the same locations are used to rectify the image data pixel coordinates.

Methods

        The first method that was used was the image-to-map rectification. The Chicago_drg.img was brought into viewer one and fit to frame, this is a USGS 7.5 minute raster graphic (DRG), that covers part of the Chicago region and also adjacent areas. A second viewer was then opened and Chicago_2000.img was opened there.
       Under the multispectral tab in the top right, control points was selected. Under the Select Geometric Model tab the polynomial box was checked. By selecting the geometric model, two tools were opened, the multipoint geometric correction tool and the GCP tool reference setup. All of the default settings were accepted in the new viewer. The Chicago_drg.img was then brought in from the lab 6 folder that was previously copied over into my own personal folder in the Q drive. In the reference map information click okay, from there the polynomial model properties was displayed and before the addition of GCP's it reads model has no solution. Make sure to keep both images at full extent, as the software will crash repeatedly if that is not done.Before additional GCP's can be added the previous ones are deleted, this is done by highlighting the GCP's and right clicking and selecting delete selection.
        Next, three pairs of GCP's were added on the images, this was done by clicking on the Create GCP tool. Once three are added the image will now read "model solution is current," and now GCP's can be added by clicking on only one of the images. Look at the root mean square (RMS) error to see how accurate the GCP's are. For this part of the lab the RMS error should be under 2. The GCP's can be moved by zooming in and moving the GCP until the RMS error in the table on the bottom gets under 2. This process was repeated for all the GCP's. A screen capture is provided below in figure 1 that also shows the table, showing the RMS error under 2.
         From here, the display resample image was clicked and the output image was rename to Chicago_2000gcr.img and saved in the folder in the Q drive. All of the default parameters were accepted and the image was then brought into a viewer to view the improvements.

Figure 1

   


            In part 2 of this lab the majority of the steps are repeated from part 1. Image sierra_leone_east1991.img was brought in and fit to frame and the second image sl_reference_image.img was brought into the second viewer. The swipe function was activated to see the extreme distortion in the images. From there follow the steps described above to get to the point of inserting GCP's. All the same steps were followed for all GCP's added. The Display Resample Image button was clicked again and the image was saved as sl_east_gcc.img in the lab 6 folder in the q drive. The resample method was changed to bilinear interpolation and all other defaults were accepted. This processing takes some real time, so be prepared to wait. The corrected image was then brought into Edras to take note of how much better the quality is.

Figure 2

Results

        This lab helped to give a good basic skill set in Geometric Correction. Making sure that images are correctly rectified is essential to putting out high quality and accurate images for analysis. It is remarkable how an image can appear to be correct in comparison to another one, but looking at the root mean square error can show something different. The ground control point locations this lab were previously selected by Professor Wilson, though selecting good locations for GCP' is essential to the process.

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

Goals and Background

           The goal of this lab is to become familiar with the structure and processing of LIDAR data. In order to gain knowledge about LIDAR lab 5 emphasized how to process and retrieve different surface and terrain models. Additionally, processing and creation of intensity images was done along with deriving outputs from a point cloud. In this lab the LIDAR data that was used was in the LAS file format. Working with LIDAR is an essential tool as it is an extremely quickly growing field.

Methods

         For this lab ArcMap and Erdas will both be used. To begin lab open up Erdas, in the viewer go to open and select the files that were provided in the LAS file in the lab 5 folder. From there be sure to change the files of type to LAS as Point Cloud (*.las). After the file type is changed all of the data can be brought in to Erdas. Be sure to uncheck the always ask button and to click no. This step takes a while for the point cloud to load. When working with an unprojected data set such as this it is important to take a look at the tile index and the metadata. By opening ArcMap and bringing in the QuarterSection_1.shp one can be sure that the point cloud was displayed in the correct area.

       Next, close Erdas and ArcMap and open a blank page in ArcMap. The goal of this next objective is to create a LAS dataset, explore the properties of the LAS dataset and to visualize the dataset as a point cloud in both 2D and 3D. After connecting to the student folder using ArcCatalog a new LAS dataset was created named Eau_Claire_City. The same files from Erdas were then added into ArcMap by clicking on add files. After the data is in, click on calculate which is under the statistics tab, this will calculate the statistics for the dataset. These statistics are used to ensure data quality and help make sure that the LAS Dataset is accurate.

        The next step is to add a coordinate system to the LAS Dataset. No coordinate system was specified, so the metadata was used to find the information regarding the coordinate system. After consulting the metadata it is possible to define the (XY) and (Z) coordinate systems. NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet) is used for the (XY) coordinate system, while NAVD 1988 US feet is used for the Z coordinate system. To make sure that the dataset is in the correct spatial location a shapefile of Eau Claire county is brought into ArcMap. After a little examination it is clear the dataset is in the correct location.

        Next, be sure the LAS dataset tool bar is active, this will be used to visualize the point cloud and it will be used later to help generate other products. Under the properties tab of the Eau_Claire_City shapefile that was created earlier change the number of classes from 9 to 8. When zoomed out at the full extent the points may not be visible, this is done to make the software faster and not bog it down, upon further inspection by zooming in the data will appear.

         Using the LAS dataset tool bar expand the surface menu, from there aspect, slope and contour will be assessed one at a time. Next, the contours will be used to help give a better idea of what will be generated with the DSM. The contour interval can be changed and it is essential to see how it affects the display. Another way to change the contour is to go into the layer properties tab and click the filter tab, on the bottom right of the tab is four predefined settings that use different methods of classification.

        In this next step there will be DSM's and DTM's created using the same pointcloud. The raster products were created at a 2 meter spatial resolution. There was four products created, a DSM, DTM and a hillshade of both the DSM and DTM. The LAS dataset to raster tool was used here which is found under conversion tools. Some defaults were accepted, though the sapling value field was changed to 6.56168 each time which is approximately 2 meters. The hillshade tool is found under 3D analyst tools and raster surface. The defaults there are all acceptable, just be sure the place it will be saved is somewhere easily accessible.

        The final step of this lab is to create a LIDAR intensity image from a point cloud. This was done very similarly to the step above. The LAS dataset to raster tool was used and the value field was changed to intensity, the void fill changed to natural neighbor and the same cell size used in the DSM and DTM is acceptable. From here the image was brought into Erdas, where it was automatically enhanced, take note that the file needs to be brought in as a Tiff.

Results

       

Figure 1 DSM

          Figure 1 above is a screenshot showing the grid that was used throughout the exercise that is picture by the red lines. The image is a Digital surface model, meaning that it is bare earth, there is no vegetation or trees included. In contrast, figure 2 below is a Digital terrain model that shows the terrain of Eau Claire including the trees and buildings. Each has there own uses. The DSM shows the elevation changes much more clearly, while figure 2 gives a better representation of how populated the area is and what the density of vegetation is.

Figure 2 DTM

       Figure 3 below shows the intensity output that was created. In ArcMap the image was very hard to see anything, it is nearly impossible to differentiate anything.

Figure 3 shows the intensity output on ArcMap

        Figure 4 below shows the intensity image that was created in ArcMap, though it is displayed in Erdas in figure 4. When bringing the image into Erdas as a Tiff. Erdas automatically enhances the image. When looking at the image below it is clear that it has very high spatial resolution, this is an image that would be very well suited for aerial image interpretation. It is a very clear image and it is easy to tell what is water, vegetation or man made buildings.

Figure 4 shows the intensity output in Erdas

Sources

Lidar point cloud and Tile Index are from Eau Claire County, 2013.
Eau Claire County Shapefile is form Mastering ARcGIS 6th Edition by Margaret Price, 2014.