Lab 6: Geometric Correction

Goals and Objectives:
The main goal for this lab is to introduce a very important image preprocessing exercise known as geometric correction. It is structured to develop skills on the two major types of geometric correction that are normally performed on satellite images as part of the preprocessing activities before the extraction of biophysical and sociocultural information from satellite images.
Methods:
In this lab, ERDAS Imagine 2015 was used in order to analyze information pertaining to the Chicago area and Sierra Leone. The process began with image-to-map rectification. After Erdas Imagine is opened, bring in Chicago_2000.img in one viewer and Chicago_drg.img in the other. Navigate to Multispectral and click on control points. Select Polynomial under Select Geometric Model. Accept the default Image Layer. Add Chicago_drg.img  as the reference DRG image. After maximizing the window and accepting all defaults, the window should look like this.

Clear the existing GCP's from the Multipoint Geometric Correction window and fit the images to frame. Click on the Create GCP tool and add a GCP to the input image (Chicago_2000.img), and another to the same area in the reference image (Chicago_drg.img) as directed. Repeat this process with two more points in the directed areas. You may have to change the color of the GCP's in order to make them visible on the image. After the third image is added, the model solution will change from model has no solution to model solution is current. When this occurs, add a fourth GCP to its directed area only on the input image, but not the reference image. The GCP on the reference image is automatically added. Zoom in on the individual GCP's and make micro-adjustments until the final Root Mean Square (RMS) error is below 2.0. The RMS error can be found in the bottom right hand corner of the window. This process is necessary in order to reduce visual errors in the final image. The finished product should appear as below:

After this process is complete, click the Display Resample Image Dialog button. Add the rectified image to the Lab 6 folder and name it Chicago_2000gcr.img and accept all other parameters. Run the operation and bring in the image to Erdas. The next process used in this lab is image to image rectification. Bring in sierra_leone_east1991.img in the first viewer and Sierra_Leone_east1991grf.img in the other. Go through the same process as in part one under Multispectral, except change the polynomial order to three under Polynomial Model Properties. Use the same process as in Part 1 to add 12 GCP's to the image. After 10 GCP's have been added to both the input image and the reference image, the remaining two GCP's will be added to the reference image automatically. Once the GCP's have been placed, adjust the individual points in order to reduce the RMS error to less than one, an acceptable level of error. The final product should look like this:

Click the Display Resample Image Dialog button. Save the output image as sl_east_gcc.img and change the resample method to bilinear interpolation. Accept the other details and run the operation. When the operation is completed, bring the finished product up on Erdas and compare the rectified image to the reference image.

Results:
After viewing the first rectified image from an image-to-map process, it is apparent that the Chicago_drg.img provided a digital planimetric map, which is the source of obtaining accurate ground control points. The image-to-map rectification method converts data file coordinates to some other grid and coordinate system known as a reference system (in this case Chicago_2000.img). The image data pixel coordinates are rectified/transformed using the map coordinate counterparts. This results in a planimetric image. The reason the four ground control points are spread around the image and not concentrated on one area of the image so as to maximize the amount of the image that is geometrically corrected. If the points were concentrated, the image would only be corrected for a small area. The model used in the first geometric correction exercise is a first order polynomial, in other words the process only requires three ground control points. This model uses a simple y=b+ax approach, which is a slope equation that measures the surface linearly by fitting a plane to the data and is less accurate than higher order polynomials. Next, when creating a 3rd order polynomial, the type of map coordinate system the reference image is in is UTM, and requires at least 10 GCP's in order to perform a transformation. The final image after all the GCP's are placed and the geometric correction is run displays a fairly spatially accurate rectified image. This image is much more accurate than the original two images. Bilinear interpolation was selected for this process instead of nearest neighbor because the polynomial used for the second process is not linear. Since the first image was linear, nearest neighbor was acceptable to use.
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.

Lab 5: Lidar Remote Sensing

Goals and Objectives:
The main goal for this lab is to learn about Lidar data structure and processing. The specific objectives for this lab are processing and retrieval of various surface and terrain models and processing and creation of intensity image and other derivative products from point cloud. In this lab, we will work with Lidar point clouds in LAS file format.
Methods:
In this lab, ArcMap and ERDAS Imagine 2015 were used in order to analyze information pertaining to the Eau Claire area. The process began with point cloud visualization in ERDAS Imagine. ERDAS was used to add lidar point cloud files to access their information. Once the point clouds were loaded and displayed on ERDAS, ArcMap was opened. After navigating to Tile Index in Lab 5, QuarterSections_1.shp was displayed and observed. After this, ERDAS and ArcMap were closed out.
Next, an LAS dataset was generated and lidar point clouds with ArcGIS were explored. First, a folder connection was created by opening ArcMap and opening ArcCatalog. From here, we connected to the Lab_5/LAS folder and creating a new LAS Dataset. After renaming the file Eau_Claire_City.lasd, we navigated to LAS Dataset Properties. Under LAS Files, click Add Files and add the individual LAS files. The files are added to ArcMap. View Statistics and click Calculate to see the statistics for all the files, which can be viewed under the tab LAS Files. Next, assign coordinate information to the LAS dataset by first clicking on the XY Coordinate System. Consult the Metadata for Lab 5 by selecting Edit with Notepad++. In ArcMap, view the XY Coordinate System and navigate to NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet) and Apply. Go to the Z Coordinate tab and navigate to NAVD 1988 US Feet and Apply. Units are now applied to the image. Close the Properties window and bring in the image of Eau Claire, which should appear as the image below.






Zoom into individual tiles to view its detail. Examine the Surface pull down menu and observe its features.

Click on the Contour listing and change the index factor by changing the numbers. Return to Layer Properties and change the properties under Filter and observe their differences. Back in the main viewer under the LAS Dataset, set points to Elevation and First Return. Click on the LAS Dataset Profile View tool and use it to view the bridge pictured below.

Finally, we will explore the generation of Lidar derivative products. After accessing Workspace under Geoprocessing, we accessed the information on Lab 5. Then access the LAS Dataset to Raster tool to create a DSM image. Then access the Hillshade tool and use the same process to create a hillshade image.

Now turn on the LAS Dataset and turn off the DSM and hillshade product. Then set the filter to Ground and generate a digital terrain model, or bare Earth raster. Run the operation and observed the finished DTM product. Lastly, a first return image is created based on intensity. Use the LAS Dataset to Raster tool with the Intensity setting selected to create an image based on intensity. After the image is created, view the image in ERDAS due to its superior viewing capabilities over ArcMap. The image appears as seen below.

Results:
When accessing the LAS Files in ArcMap, the statistics on the files display that the Min Z and Max Z for the entire LAS Dataset are 517.85 and 1845.92 respectively. When viewing whether these values are realistic for the city of Eau Claire, it appears as though the highest value makes sense, but the lowest value seems obscure because the sensor may not be able to sense through multiple layers of an area that may be less than 517.85. At this time, we are unsure of the unit of measurement of these numbers. However, after consulting the Metadata, it appears as though the horizontal coordinate system is through D_North_American_1983 with a unit of feet, and the vertical coordinate system is through the North American Vertical Datum of 1988 also with a unit of feet. Viewing the range of the image, the X and Y ranges are 20995.8 feet and 13347.02 feet respectively. There are some areas with limited amounts of points when viewing this image when zoomed in. These limited points could be due to the large amount of data/land that the sensor must calculate in a brief amount of time because of its high elevation. This would result in wide point spacing during the on fly interpolation. When it comes to the distribution of slope for natural land surface features and man made features, the man made features have sharp edges and specific patterns of elevation, whereas the natural surface features appear more random and sporadic. When choosing a filter under Layer Properties, the Ground and Non Return options make use of classification, First Return relies on return number, while All (Default) uses both. There is a difference between these two options because return number does not classify what the object is, whereas the classification does. Looking back at the LAS Dataset under point spacing, the average NPS of the point clouds appear to be 1.485 after calculation. When viewing the nature of features in the first return hillshade derived product, it is apparent that the product is in grayscale and illustrates the tops of every object in the image. It shows great amounts of detail in every building, tree, road, etc. for texture. After creating a bare Earth raster, the image appears much smoother, and objects such as buildings and trees are taken away. What is left in the DTM is what the sensor predicts is the ground in the image; strictly the terrain. The impact from a visual perspective of removing vegetation and buildings from the map view is a contrast between the smooth view of the DTM image and the first return image. Viewing the intensity image, the spectral channel of the intensity image is under the middle infrared band. Overall, however, there are differences in the spectral characteristics of the intensity image. The image is visibly sharper due to ERDAS, but the image is only on the grayscale, instead of the full spectrum of colors.
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 Margaret Price, 2014.