ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

APPLICATIONS FOR LIDAR “IMAGING”

Joseph Tack, PhD The Ohio Department of Transportation

Office of Aerial Engineering 1602 West Broad Street Columbus, OH 43223

Joseph.Tack@dot.state.oh.us

Lewis Graham GeoCue Corporation (NIIRS10) 9668 Madison Blvd., Suite 101

Madison, AL 35758 lgraham@geocue.com

ABSTRACT Most applications of airborne Light Detecting and Ranging (LIDAR) to date have been principally focused on extracting bare earth surface models, and, to a lesser extent, extracting cultural features such as buildings from three dimensional point clouds. We have explored alternative image data sets derived from LIDAR such as orthographic intensity images as well as pseudo stereo. Addressed in the paper will be algorithms used for synthesizing a variety of image types as well as novel but practical applications of the image products. We illustrate the utility of these images by presenting a case study in geometrically correcting a LIDAR highway project.

INTRODUCTION

The Ohio Department of Transportation, Office of Aerial Engineering (OAE) has been conducting research for the past several years aimed at refining LIDAR data accuracy and utility. The OAE has been working with GeoCue Corporation (NIIRS10) to refine tools within the LIDAR 1 CuePac application set to make visualization and processing faster and more intuitive. The overall goal of the OAE is to mature the use of LIDAR applications such that projects can be routinely processed at engineering accuracies.

Together we have been experimenting with a variety of visualization techniques that allow rapid assessment of various attributes of LIDAR data. Since LIDAR data are post-processed into 3 dimensional geocoded points, direct perspective images of these data can be immediately rendered. GeoCue has created a number of different types of LIDAR image renderings that have a variety of uses in both production and post-production. Examples of image renderings and their applications are listed in Table 1.

Table 1. Types of LIDAR images produced in GeoCue’s LIDAR 1 CuePac LIDAR Image Type LIDAR Parameters Used Use Intensity Ortho Intensity Coverage analysis, 2D feature collection,

“ortho” backdrop Elevation Ortho Intensity, Elevation (Z) Terrain visualization Delta Elevation (dZ) Ortho Intensity, Flight Line Number, Elevation

(Z) Relative vertical accuracy (dZ) assessment

Pulse Return Ortho Intensity, Pulse Return Number Visualizing penetratable material such as vegetation

Color by Flight Line Ortho Intensity, Flight Line Number Visualizing true ground coverage by flight line

Color by Class Ortho Intensity, Classification Visualizing quality of data classification Synthetic Stereo Various combinations of Intensity,

Classification, Elevation (Z), Return number

Stereoscopic visualization, direct 3D feature collection such as break lines.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

Depicted in Figure 1 is a LIDAR “ortho” image created from the laser return intensity, Z (elevation) and strip (flight line) number. The image is rendered as gray scale (as seen in the upper left and lower right of Figure 1) where there is no overlap between data from different flight lines (strips). Where two (or more) flight lines overlap, a color rendering is constructed based on the average maximum elevation difference between points in the overlapping strips. The distance (dZ) is used as an index into a user-defined color. In the image of Figure 1, we used 4 color bands separated into 7.5 cm increments. The GeoCue rendering system allows indexing based on signed differencing (e.g. -8 cm would yield a different color than would +8 cm) or the magnitude of the difference. In the rendering of Figure 1, we used magnitude differencing with 4 “bins”, each 7.5 cm in height. Thus green represents dZs from 0 to ±7.5 cm, yellow from ±7.5 cm to ±715.0 cm, orange from ±15.0 cm to ±22.5 cm and finally red for dZ greater than ±22.5 cm. Notice in our example of Figure 1 that the LIDAR strip in the upper portion of the image is clearly contributing to a differential error.

The utility of the dZ image is that it provides a method of very quickly assessing the relative vertical accuracy of a LIDAR project. Without this tool, one would typically pull pairs of strips into an analysis tool, measure relative distances, load a new set of strips, measure and so forth. This is very labor intensive at best and impossible when flight lines are not laid out in very orderly patterns.

We illustrate the utility of the dZ image process by presenting the steps of geometrically correcting a highway LIDAR project.

Figure 1. dZ "Ortho" in GeoCue.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

COLLECTING THE DATA The project was located in Erie County, Ohio on State Route 250 near Sandusky. The sensor used was an Optech Airborne Laser Terrain Mapper (ALTM) 3010. Ten strips of LIDAR were collected with a 70 kHz Pulse Repetition Frequency (PRF), 40 degree field of view and 50 Hz scan frequency. These settings would result in an approximately one half meter post spacing but the flight lines were collected with 50 percent overlap to decrease the average post spacing to less than 1 foot. The average baseline distance was approximately 18 miles. The flight schedule was programmed such that the Global Positioning System (GPS) Position Dilution of Precision (PDOP) was less than 2.5 for the entire flight. The manufacturer supplied in-air initialization procedures were followed. These settings typically give very good results but, for some reason, this project exhibited very poor accuracies.

POST-FLIGHT GEOMETRIC ANALYSIS Post-flight, the GPS/IMU data were processed using the manufacturer’s (Applanix) supplied software (POSPac). POSPac outputs a trajectory file (called a Smoothed, Best Estimated Trajectory, SBET) that contains the 6 degree of freedom (X, Y, Z, pitch, yaw, roll) of the sensor in 5 millisecond intervals. These data (the SBET) are then used in Optech’s post-processing software (REALM) to geocode the LIDAR points. Immediately after geocoding, the data were imported into GeoCue, project blocks were defined and then delta Z (dZ) images were created using the threshold values listed in Table 2.

Table 2. dZ Color Table used in the project

dZ Range (in feet) Color >0.25’ Red 0.15’to 0.25’ Orange 0.05’ to 0.15’ Yellow -0.05’ to 0.05’ Green -0.15’ to -0.05’ Aqua -0.25’ to -0.15’ Blue <-0.25’ Magenta

Figure 2 is the resultant LIDAR “dZ” orthographic image detailing the difference between elevations of the highest and lowest last pulse returns in a pixel where, as explained in the introduction, the difference is computed based on strip number. The ortho image uses the average return intensity to generate a black and white reflectivity image where there is no strip overlap and colors to represent the elevation differences between strips where there is overlap. The pixel size selected was 2 feet. The centerline of the flight lines are depicted as yellow lines.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

Figure 2. Original dZ plot. A perfectly accurate project would, of course, exhibit no elevation differences when the same ground area is sensed from different sensor positions (flight lines). Using the color table we defined for this project, the areas with no elevation differences would appear green. In practice, however, these differences do exist. Differences that are not indicative of actual geometric errors can be caused by reflectance of the laser pulse from different objects (such as ground and vegetation). However, pulses detected from two different sensor locations that reflected from ground points of the same absolute elevation should show no difference. Upon analyzing the images of Figure 2, it is apparent that bore sight angles need to be adjusted. When looking at individual strips, one side of the strip typically has a positive residual and the other either negative or less positive (as indicated by the various colors). There also appears to be a slight scale error since the edges of flight lines have a larger magnitude of dZ error than do the centers (nadir regions). TerraMatch, a LIDAR geometric analysis and adjustment program from Terrasolid, was implemented to solve for Heading, Roll, and Pitch misalignments and to determine a mirror scale correction. A sample of the LIDAR data was selected at a highway interchange at the far south of the project (not shown). The sample area was approximately 70 acres in size. The sample was classified into two classes; ground (bare earth points) and other (points not in the ground class). Classification was performed for each flight line individually since this technique allows for better comparison between flight lines. The bare earth points were used with TerraMatch to determine adjustments to the bore sight angles and mirror scale factor. The sample included two cross strips and 4 normal strips. The site was selected because the topography was fairly clean (no trees or buildings) and consisted of various incline angles. TerraMatch completed the bore sight calculations using 2,487,825 ground points in 14.25 hours. The resultant correction parameters are listed in Table 3.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

Table 3. First pass correction parameters

Parameter Value Heading 0.0353 Roll -0.0038 Pitch 0.0071 Scale 0.00103

The corrections can be applied to the LIDAR data using either the manufacturer’s post-processing software, the GeoCue LIDAR 1 CuePac tools or with TerraScan (LIDAR processing software also from Terrasolid). We applied the corrections using Optech’s REALM software since we felt this would be the fastest approach. In retrospect, we would use the GeoCue tools since this would eliminate the need to re-ingest data. Following the data correction, the points were again imported into GeoCue and dZ images regenerated. The total processing time for these operations was about 10 hours.

These second pass images are depicted in Figure 3. When Figure 3 is compared to Figure 2, it is apparent that the errors are more uniform along the individual LIDAR strips. This would appear to indicate that the bore sighting may not have improved the accuracy of the data, but it did reduce the number of causes for error.

Figure 3. Second pass dZ images with Heading, Roll, Pitch, and Mirror Scale corrected for the entire project.

Figure 3 also indicates that several strips appear to be too high and several too low. Based on this analysis, we again used TerraMatch, this time to correct for individual strip roll error and absolute elevation errors. This run of TerraMatch was implemented on a sub-sample of the overall project data comprising sections chosen at both locations where crossing flight lines occur. One was in the south (outside of the image areas presented in this paper) and the other was located in the images of the above figures. Points were again classified into ground and non-ground values for individual strips. Strip trajectories (the subset of the GPS/IMU smoothed, best estimated

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

trajectory results associated with each strip) were determined to be of normal quality based on the “measure match” tool in TerraMatch. The solution obtained was derived in two steps using 50 iterations. The total processing time was 1.2 hours. This was considerably faster than the initial TerraMatch run due to the use of a significantly smaller data sample. The combined solution is listed in Table 4.

Table 4. Second pass corrections

Flight Line Parameter Value (in feet) Parameter Value (radians) 25 Z shift +0.018 Roll shift -0.0002 26 Z shift -0.103 Roll shift -0.0065 27 Z shift 0.000 Roll shift 0.000 28 Z shift 0.000 Roll shift 0.000 29 Z shift +0.103 Roll shift +0.0074 30 Z shift -0.064 Roll shift -0.0081 31 Z shift +0.342 Roll shift +0.0010 32 Z shift -0.200 Roll shift -0.0014 33 Z shift 0.000 Roll shift 0.000 34 Z shift -0.018 Roll shift -0.0002

Figure 4 illustrates the Z residuals still in existence after adjusting the bore sight angles and mirror scale factor

of the project and adjusting for average elevation residuals and roll angles for each strip individually. When comparing the resulting image to those of Figure 3 above, there is substantially less error being exhibited.

Figure 4. Heading, Roll, Pitch, and Mirror Scale corrected for entire project and Roll and elevation corrected for each strip.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

To further decrease elevation error, TerraMatch has a “find fluctuations” utility. This tool determines the average elevation residual of each flight line to the overall average in one second time intervals along the strip. These elevation residuals are then used to correct the strip up to a user specified maximum limit. For this project 0.05 feet was used for the limit.

Figure 5 is the resulting dZ ortho image after applying corrections for fluctuations. The time required to determine the fluctuations is unknown as it was started late on Friday and observed on the following Monday morning. Figure 5 illustrates that elevation fluctuation corrections do not have a huge impact on accuracy, but do reduce errors to some extent.

Figure 5. Heading, Roll, Pitch, and Mirror Scale corrected for entire project; Roll and elevation corrected for each strip; and Fluctuations removed.

After applying corrections for the entire project, individual lines, and removing fluctuations, one final step remained. LIDAR data points near the strip nadir are typically more accurate than are points toward the edge of scans. This phenomenon is caused by the deceleration/acceleration of the scanner’s oscillating mirror as it changes its direction of rotation at scan edges. A common recommended practice is to ‘clip’ both edges of the LIDAR swath to remove the less accurate points. Of course, anticipation of this procedure must be included in flight planning to ensure that sufficient swath overlap exists.

Figure 6 depicts the result of removing LIDAR points within 5 degrees of the edges of scan lines. The points were also reclassified using a single algorithm for the whole data set prior to generating this image. As can be seen in Figure 6, not all error was removed from the project, but the majority of places where overlap still exists exhibit much less error than the original data. Most of the data now exhibit Aqua, Lime, or Yellow pixel values. This indicates that almost all of the overlap regions have a relative error of less than 0.15 feet.

ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006

Figure 6. Heading, Roll, Pitch, and Mirror Scale corrected for entire project; Roll and elevation corrected for each strip; Fluctuations removed; and 5 degrees of side lap removed from either side.

CONCLUSION As was demonstrated by this project, it is very beneficial to have a tool that illustrates the effect of corrections being implemented on an entire project. Data relative accuracy can quickly be assessed, both qualitatively and quantitatively. Additionally, the visual inspection of the dZ image provides indicators of the corrective actions that need to be applied in geometric adjustment tools. Unfortunately, fully calibrating a LIDAR project is very time consuming; with the bulk of the processing in the TerraMatch steps. This project required 4 full days of computing time on a 3.2 GHz dual processor with 4 GB of RAM. Thus while this project illustrates the value of LIDAR analysis and correction tools, these tools need to be performance tuned to process data more quickly and efficiently.