Lab 6 - Geometric Correction

Introduction
     Lab 6 focused on a form of image processing technique known as geometric correction and the various ways of accomplishing it. This lab focused on two specific techniques. Those two types of correction are known as image-to-map rectification and image-to-image. These are both done to help ensure image accuracy before it is interpreted and processed.

Methods

The first form of correction we focused on was image-to-map rectification. The goal of this part of the lab was to take an image that was inaccurate and correct it using a map. Hence the name image-to-map rectification. Our study area was Chicago. In order to correct the image, the user must use Ground Control Points or GCPs. GCPs are points the user puts on both the image and the map. They should be as close as possible to the same location on both the image and map. Depending on the image delegates how many points will be needed to correct it. In the case of our image, there was low distortion meaning it was a first order polynomial. Going off this chart (Figure 1) a minimum of 3 GCPs are required.

Figure 1 - Required amount of GCPs

While adding GCPs it is important to check the level of accuracy you are achieving. Erdas Imagine has a feature that monitors the Root Mean Square or RMS while adding points. For any image you want to shoot for under 2.0 RMS (Figure 2) but the ideal level is less than .5.

Figure 2 - Adding GCPs and checking RMS error

Once satisfied with the level of accuracy it is time to re-sample the image. Re-sampling is mainly done to correct pixels that lack a brightness value. The final comparison of the first image vs. the corrected image can be found below (Figure 3).

Figure 3 - Resampled and corrected image overtop original

The second form of image correction we  did is referred to as image-to-image rectification. It is essentially the same process before except instead of a map we are using another image that is spatially accurate. Our study area for this correction is Sierra Leone. This image (Figure 4) happened to be a lot more distorted than the Chicago image and requires a third order polynomial. If we check the chart (Figure 1) we can see that at least 10 GCPs have to be used. After placing the GCPs (12) of them I got my RMS error to be less than 1 (Figure 5)

Figure 4 - Spliced Image presenting the differences between the distorted image and the already corrected image

Figure 5 - GCPs placed on image and RMS error lowered

Once again, after completion the corrected image had to be re-sampled. Because bilinear interpolation is more accurate I re-sampled using that method instead of nearest neighbor and the results were accurate and smooth (Figure 6)

Figure 6 - Finished image spliced with original corrected image

Conclusion
Images that are properly geometrically corrected and very important when doing accurate analysis. GCP location is essential for obtaining the highest accuracy and getting the RMS error the lowest possible should be a priority. Tools like ERDAS make correction relatively easy if you know the correct methods and what re-sample method to use.

Sources -
Images - U.S. Geologic Survey
Program Used - ERDAS Image

Lab 5 - Introduction to LIDAR

Background and Goals

This lab's main purpose was introducing us to Lidar and how it can be processed and manipulated. Lidar is a form of active remote sensing. Basically Lidar works by sending a laser pulse to the ground from an aircraft, the pulse then returns to a sensor mounted on the aircraft. This data is used to create a point cloud. This data is separated in to different return heights allowing to calculate location and elevation.

This lab was meant to introduce us to Lidar while showing us the basics in an area we are familiar with, Eau Claire, Wisconsin.

Methods

The very first step of the lab was to import the Lidar data in to a program that would help us view the data. For this lab we once again used, Erdas Imagine. Erdas Imagine has a lot of visual tools to view LAS point cloud files.

We also used ArcMap for the majority of the lab because it allows us to examine the statistical data of the imagery. While looking at the elevation statistics it shows that our study area (Eau Claire) is around 1000ft while there was one value around 1800ft. (Figure 1) The Z value represents elevation.  ( Later in the lab while exploring the data I found out what was responsible for the anomaly.

Figure 1 - Table showing Z Max & Mins

Often times Lidar data lacks a coordinate system so the user must define one while examining the metadata. (Figure 2)

Figure 2 - Coordinate Systems Specifications

By looking at the metadata we can tell that our XY coordinate system needs to be NAD 1983 and our Y coordinate system should be NAD1988. To adjust these coordinate systems you have to go under the LAS Dataset Properties.

With the LAS Dataset Toolbar enabled a user can look at the surface data in a number of ways included Elevation, Aspect, Slope, Contour. I predominantly used Elevation but it varies depending on what you are doing with the data.

Occasionally there are missing chunks of data. In our dataset one instance of that was in a chunk of water. Without having the data Arc could not determines a value so the program guessed and made it seem like there was elevated water there when in actuality, data was just missing. (Figure 3)

Figure 3 - Missing water data

Like mentioned earlier, when "touring" the data I came across a very specific "high point" in elevation. When looking at it in 3D view it became more apparent of what the high spike in elevation was. (Figure 4)

Figure 4 - 1800ft Tower

The point in data stood out so vividly because as you can see everything surrounding it is much, much shorter. This appears to be some sort of radio/television tower. The 1800ft elevation makes much more sense now knowing this can be found in Eau Claire.

Using ArcMap tools, Lidar data can be turned in to 3D images. For example, we took the first return data for Eau Claire and created a digital surface model (DSM) (Figure 5) at a resolution set at 2 meters. We also made a digital terrain model (DTM) (Figure 7). Lastly, a hillshade model was created to enhance the DSM. (Figure 6)

To do this the LAS Dataset to Raster tool was used with the Value Field set to Elevation, Cell Type: Maximum and Void Filling: Natural Neighbors. In addition, the Cell Size was set to 6.56168 which equates to basically 2 meters.

Figure 5 - DSM

Figure 6 - DSM with Hillshade

Figure 7 - DTM of study area

 To create the DTM you have to set the filter to "Ground" this creates a "bare earth" raster image. This eliminates buildings and trees and focuses on terrain and elevation.

The last iteration of the data I created was an "Intensity" image. This is created using first return data. Once again using the LAS Dataset to Raster tool. The only thing changed settings wise is Value Field which has requires Intensity to be selected. The produced image was dark, not showing great detail but once lightened in ArcMap the image was enhanced and very detailed. (Figure 8)


Figure 8 - Intensity image created and brightened in ArcMap

Result
This lab lays out the basics of Lidar and highlights a few ways the data can be used for image processing. Lidar is an ever expanding field and I only expect it to become more popular and easier to work with. ArcMap and Erdas compliment each other very well when working with Lidar and I look forward to digging further in to Lidar.

Sources:
Lidar point cloud and Tile Index obtained from Eau Claire County, 2013.
Eau Claire County Shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price, 2014.