A COMPARISON OF LIDAR TERRAIN DATA WITH AUTOCORRELATED DSM EXTRACTED FROM DIGITALLY ACQUIRED HIGH OVERLAP PHOTOGRAPHY BACKGROUND

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1 A COMPARISON OF LIDAR TERRAIN DATA WITH AUTOCORRELATED DSM EXTRACTED FROM DIGITALLY ACQUIRED HIGH OVERLAP PHOTOGRAPHY Devin Kelley, Project Manager, Certified Photogrammetrist (ASPRS) Thomas Loecherbach, PhD, Chief Photogrammetrist, Certified Photogrammetrist (ASPRS) HJW GeoSpatial, Inc Broadway, Third Floor Oakland, CA ABSTRACT The advent of large format digital frame photography allows for a high degree of automation in photogrammetric production operations, particularly due to the low overhead in obtaining photography with high amounts of overlap. New upgrades to off-the-shelf autocorrelation software allow for multiple image matching in high-overlap scenarios, as opposed to conventional methods that allowed only for stereo pairs to be matched during autocorrelation. The new algorithms allow for a more robust blunder detection and the computation of more accurate surface models, with little manual clean up required. This paper presents a comparison of autocorrelated digital surface models with LiDAR in terms of accuracy, reliability, and specifically seeks to identify and characterize situations where one can expect to see differences between the two datasets. With these differences characterized, it becomes easier to decide when an autocorrelated DSM can be considered a suitable replacement for LiDAR data, or when LiDAR terrain mapping is an absolute necessity. BACKGROUND Conventional Terrain Data Representations and Data Formats Terrain data can be defined and represented any number of ways, and in numerous data formats; the most appropriate being that which is adequately tailored to the end-user s needs and working environment. Following is a brief summary of the types and formats of terrain models that are used by photogrammetrists, engineers and GIS professionals. 1 Photogrammetrically derived terrain models typically consist of manually collected 3-D points and breaklines as the basis to describe the terrain. Breaklines are an effective way to define characteristic linear features in the terrain (such as tops and bottoms of ditches). Points are used to describe the nonlinear terrain undulations, and are placed with a density suitable to describe the terrain. Photogrammetrists commonly refer to this type of terrain representation as a DTM, or digital terrain model. Often DTM data is manually collected photogrammetrically using standard conventions, and used as input data to contour generation software, generating contours that have a very predictable (any usually desirable) appearance. Breaklines play a key role in enforcing the contour generation process. This type of terrain dataset is manually compiled by a technician, so manual interpretation and skill of the technician play an important role in the end-product s suitability to describe the terrain. A TIN (Triangulated Irregular Network) is a vector based representation of the terrain, made up of irregularly distributed nodes and lines with three dimensional coordinates (x,y, and z) that are arranged in a network of non-overlapping triangles. The point data is imported into various software packages that can apply computational geometry, triangulate the points and generate derived products, such as contours. Often, a dense set of points will be used do describe the linear features that would otherwise be modeled with breaklines in a DTM. Such a dense set of points in these areas effectively enforces breakline information during the triangulation of the points. Also breaklines can be used to control the triangulation by enforcing specific triangle vertices. There is no requirement in the TIN data structure that breaklines be

2 represented. When a properly designed TIN dataset is used as input to automated contour generation, the results can be identical to a contour end product derived from a DTM (points and breaklines). A DEM (Digital Elevation Model) is a raster version of a terrain model, using a regular posting of points- a grid. Obviously, in this data format, no breaklines can be explicitly stored, so there are limits as to how well the terrain data can be described. The limits of terrain modeling are related to the terrain characteristics, DEM point density and vertical accuracy. The DEM data structure is very simple, and quite often, the benefits of a simple data format outweigh the disadvantages of the non-optimal terrain representation. DEMs can be generated as a derived product from TINs and DTMs. DSM (Digital Surface Model) data is usually either a DEM or TIN format, with the difference being that there are above-ground features also represented in the terrain model, not just bare-earth. Above ground features are generally included in terrain models derived from autocorrelation, and also are contained in LiDAR datasets that have not been classified and filtered to bare-earth. Photogrammetric Image Matching/Autocorrelation Image matching is a term commonly used by photogrammetrists to refer to automated raster-based point measurements in two or more images, generally achieving sub-pixel accuracy. Image matching can be achieved by area based methods using autocorrelation or least squares matching, as well as by feature based methods, or a combination thereof. In practice, the algorithms work on a so-called image pyramid, iteratively refining measurements and computations from a low to a high image resolution. Often an intelligent placement of a few points by a human operator is replaced by a measurement of a very high number of points in combination with an outlier removal algorithm. This approach to automated image matching is often referred to as autocorrelation. When applied to stereo (or high overlap) imagery, the matched image points can be projected through the collinearity model to generate terrain points. To do so first requires that the stereo photography has exterior orientation data well established, through aerial triangulation. The exterior orientation and initial terrain height approximations allow for the computation of search windows in the overlapping photography. The image matching windows are used to restrict the search space to regions that are most likely to contain common points. The image matching window should contain enough pixel data to provide a reliable match, while being small enough to optimize computation time. In the computation of a terrain model, the collinearity equations are used to project each set of matched image points into the ground coordinate system, in an automated way. The algorithm systematically measures points either by stepping through the project area in a fixed interval, by correlating each pixel, or by generating a high density cloud of distinctive points. The end-product of image autocorrelation is typically a set of irregularly-spaced terrain points, or regularly-spaced points (DEM), depending on the algorithms used. Since there is often little or no manual guidance of the autocorrelation process, the resulting unedited dataset is often a DSM, containing above ground features in many cases. The degree of above ground features in a dataset will depend on the terrain characteristics, parameters and algorithms applied. Airborne LiDAR Terrain Data Airborne LiDAR data is acquired by an airborne laser range scanner that sends out laser range pulses at a very high rate, often detecting multiple returns for each outgoing point. During flight, airborne GPS/IMU is supported by ground base stations, which accommodate post-mission differential post-processing of the trajectory. The laser range data is processed with respect to the airborne GPS/IMU trajectory, which allows for the range data to become geo-referenced, as terrain points. Most LiDAR sensors can record multiple returns for each outgoing pulse, which ultimately increases the probability of detecting bare-earth points in vegetated areas. Since the laser pulse is not finite in volume, and diverges to over 1 in diameter by the time it reaches ground level, the pulse can hit several objects on its way to the ground. 2 Often, these objects are leaves or branches of trees and vegetation. The incoming returns from interference with these objects are recorded for each outgoing pulse. This information is useful in post-processing the dataset, by restricting that last-return and only return points are designated as candidates for bare-earth, reducing the search space, and increasing probability of correctly identifying points on the ground. In postprocessing, after reducing the search space in this way, automated TIN-based algorithms are used to identify points in this sub-set that are most likely to be bare-earth. This geometric point classification approach usually selects seed points that have low relative elevations, and computationally, grows the bare-earth class from these points based on

3 nearest neighbor analysis, user-defined thresholds and iterations relating to slope, and maximum/minimum horizontal and vertical distances. The end product will typically undergo manual QA/QC, using any ancillary data available, such as orthophotography, to guide the inspection and corrections. INTRODUCTION From 2006 to 2007, HJW GeoSpatial has acquired and processed over 32,000 exposures of Microsoft/Vexcel Ultracam imagery, generating end products at various resolutions over more than 5,500 square miles. One end product that has become a standard for HJW, is orthoimagery produced from autocorrelated DSM extracted from the imagery. For these products, the project is typically designed to be heavily reliant on airborne GPS/IMU, and flown with a 20-cm pixel size (1:21,900 physical image scale) at 80% forward overlap and 30% sidelap. The DSM extraction is typically done in a way to generate an end-product with a 2-meter grid posting. The end-users of these projects are often looking for the best accuracies that can be achieved with the project design parameters, as well the low cost associated with the highly automated workflow. The end user also needs to have an understanding of the characterization of the DSM in terms of accuracy and ability to model the terrain. This understanding is important in the end-user s decision-making and justification of using the autocorrelation approach over airborne LiDAR terrain mapping. Our experience has shown that in many cases, autocorrelated DSM is a suitable alternative to LiDAR data. As with all mapping projects, understanding and meeting the end user requirements is the key to a successful project. The goal of this research is to characterize the difference between autocorrelated DSM and LiDAR terrain models. Both types of datasets can have many elements controlled by project design, engineering and proper use of data and modeling. In the case of LiDAR, which has been well studied, there are many exceptions that are related to terrain characteristics, such as accuracies in sloped areas, as well as skew and point density in vegetated areas. There are also well-established conventions and specifications for LiDAR datasets. 3 However, autocorrelated DSM data does not have the same standards defined, as the technology is hard to characterize due to the various sensors in use and dependency on proprietary algorithms. With digital frame imagery in high demand, we find it necessary to characterize the derived autocorrelated DSMs, so that they can be utilized in a way that optimizes their value. Autocorrelation of Digitally-Acquired High-Overlap Imagery The advent of digital frame sensors allows the advantageous opportunity to acquire imagery with increased forward overlap (80%) without the need to accommodate additional expenses of film, processing and film scanning. Compared to the standard 60% overlap photogrammetric block that, when paired with 30% sidelap, affords the measurement of common tie points in as many as six images, the 80% overlap provides the measurement of common point in as many as 10 images. This opportunity for increased redundancy in tie point measurement can be taken advantage of by automated processing. 4 Another important characteristic of digital frame imagery is the radiometric resolution of the sensor, which measures 12 bits of information per channel, and ultimately stores image data at 16-bits per channel (Microsoft/Vexcel Ultracam and the Z/I DMC camera). This is in contrast to conventional approaches with film, which almost always involves scanning at 8-bits per channel. Additionally, with digital sensors, the signal to noise ratio is much higher than scanned film, which is affected by film grain. 5 The increased radiometric resolution and the increased overlap provide increased redundancy and information that can be utilized in advantageous ways by automated processes. 6 The low noise, high dynamic range, and high overlap serve to enhance the performance of automatic aerial triangulation as well as DSM computation. Characterization of Autocorrelated DSM Autocorrelated DSM data can either be an irregularly spaced set of terrain points, or computed and represented as a raster format, with a regular grid. In all cases, one can make all reasonable efforts to optimize the density to adequately represent the terrain s morphologic information with consideration for the vertical accuracy that is attainable from a given set of photography and its aerial triangulation solution. As an automated process, there is not much control over which above-terrain features are included in the DSM, so individual trees may be left out of the model, while the top of canopy for dense forested areas may be included in the DSM. Conventional limitations of photography- not being able to see through vegetation canopy- still apply, so this will have an effect on the end product. Not being manually measured, there is not the opportunity for a technician to use judgment about average

4 canopy height in the determination of ground points. Finally, as a completely automated process that relies on thirdparty software, the end product may contain artifacts in difficult-to correlate areas, or noise-level systematic errors, seemingly related to software idiosyncrasies that cannot be easily characterized. Characterization of LiDAR Terrain Data LiDAR terrain data can be described by nominal point density and vertical accuracy. Of course, the terrain characteristics (amount of vegetation) can play a role in the resulting point density. The vertical accuracy can be well understood, with accommodations for sloped surfaces, which degrade the accuracy locally, and thick vegetation, which skews the terrain model. These and other characteristics are well documented and studied. PROCEDURE Project Design The project area is a site of 215 acres, located in San Mateo County, CA. The terrain is mostly open, rolling hills ranging from sea level to 405 feet, with minimal build up of man-made features. There is a two lane highway (Pacific Coast Highway), a creek bed covered in thick vegetation, and also coastal cliffs with vertical or oververtical faces, approximately 100 feet in height. The 215-acre study area is a subset of larger flight block, covering two square miles. The fight block consists of two lines of 15 exposures of Ultracam D imagery, controlled by airborne GPS, flown by HJW in July Aerial triangulation was performed on the block, using airborne GPS as well as 12 targeted ground control points. Next, BAE Socet Set ATE module was used to compute an autocorrelated DSM at a 2-foot posting for the study area. The choice of 2-foot posting was made in order to most closely match the point density of the LiDAR dataset, which is being compared. HJW GeoSpatial acquired LiDAR data for this area in January, 2006, and later collected Ultracam imagery in June, Additionally, in Spring, 2007, HJW had performed conventional photogrammetric mapping for 2 contours of this site. Both the LiDAR dataset and the conventional mapping will be used as control datasets with which to compare against the Ultracam-derived DSM dataset. The collection parameters are expected to result in somewhat similar terrain model end products. With the tools available in the TerraSolid suite of software, including TerraScan, several different elements of the surface model are inspected. First, accuracies related to the project design parameters and choice of terrain representation (a regular grid DSM) are tested. Next absolute accuracy of check points in open and vegetated areas are checked against the LiDAR point data. Terrain model completeness is considered in light of the absence of breaklines. Finally, we inspect the dataset for autocorrelation blunders and consider the implications. ANALYSIS Measurement of Accuracies Related to Project Design and Terrain Model Representation In this study 12 ground control points, along with airborne GPS, were used to control the aerial triangulation. The ground control provided the following statistics from aerial triangulation. RMS control point residuals (X, Y, Z) in USFT Maximum control point residuals (X, Y, Z) in USFT This information indicates that the aerial triangulation results tie in well to the control network. Other statistical information in the aerial triangulation solution indicates that the photo block is strong, with adequate redundancy to provide a high degree of confidence in moving forward with autocorrelated DSM computation. With the DSM computed, we measured the delta-z values from 10 adjusted tie point positions to the DSM surface.

5 Statistics in USFT Average dz Minimum dz Maximum dz Average magnitude RMS Standard deviation The results show that there is a measurable difference between terrain points manually measured in the stereoimagery, and the representation of those positions in the end-product DSM. A key consideration when considering the cause of the delta-z offsets should be the terrain representation of the DSM, which is a 2-foot posting grid. This is different than the manually-measured point, which was not measured on a grid interval, but instead irregularly placed. Depending on the local terrain undulations nearby the check points, the terrain representation itself, and not the terrain extraction accuracy, may be the reason for a delta-z value being larger than the theoretical best-case. Absolute Accuracy The LiDAR point dataset was used as a control set to check the absolute accuracy of the DSM. The LiDAR data has been tested to have a fundamental accuracy of 0.6. In vegetated areas, using the 95th-percentile approach to accuracy assessment, the resulting value is 1.3. The supplemental accuracy is directly equated to the 95th percentile, where 95 percent of the errors have absolute values that are equal to or smaller than 1.3. The table below shows the statistical results of the fundamental absolute accuracy assessment for the county-wide dataset that HJW acquired and processed in Statistics in USFT Number of points: 898 Average vertical error Min vertical error Max vertical error Average magnitude Standard deviation RMS Accuracy (95%): Since the LiDAR dataset has been filtered to bare-earth, we applied the same bare-earth filtering approach to the DSM in order to increase the likelihood that the presence of surface features has been minimized and that we are working with comparable products. The filtering approach is TIN-based, using TerraScan. Since the LiDAR dataset utilized multiple returns, it is expected that the filtered LiDAR bare-earth will have a more robust representation of the bare-earth in places where the DSM is modeling the top of vegetation. The absolute accuracy was tested in both an open area and in vegetated areas. LiDAR point comparison to DSM in open areas: Statistics in USFT Number of points 38 Average dz Minimum dz Maximum dz Average magnitude RMS Standard deviation 1.348

6 LiDAR point comparison to DSM in vegetated areas: Statistics in USFT Number of points 28 Average dz Minimum dz Maximum dz Average magnitude RMS Standard deviation The following table shows statistics for the same points, this time with the DSM having been filtered to bareearth. In vegetated areas, the TIN-based algorithm filtered out approximately 66% of the data as vegetation, allowing the remaining 34% to model the terrain. Statistics in USFT Number of points 28 Average dz Minimum dz Maximum dz Average magnitude RMS Standard deviation For this dataset, attempts to filter the DSM to bare-earth did not produce any evidence that the terrain model has been improved. Assessment of Terrain Model Completeness This evaluation is qualitative and visual in nature. First, LiDAR and DSM are compared in terms of morphologic information content, which is unquestionably higher than the information represented in the manually compiled DTM. The TIN used for 2 contour generation is considered to contain the most relevant information required to model the terrain in a way that will generate 2 contours to conventional industry standards. When comparing 2 contours generated by the LiDAR and the DSM, they both demonstrate such high morphologic information that they are both considerably different than the conventionally-generated cartographic contours. The problem of generating visually-pleasing contours from data that is unusually dense is not a new, but rather the challenge brought on by the utilization of these new technologies. With the absence of breaklines in both the LiDAR and DSM, features like road edge are not represented in a way that will generate cartographically-consistent contours. Again, the point density is greater than that of the manually-compiled data, but not specifically placed to represent terrain features. Regardless of vertical accuracy, the representation of such features is limited to the point density. This characteristic is apparent when comparing 2 contours generated from the LiDAR and DSM across roads in the study area. The multiple-return nature of LiDAR increases the probability that points are measured on the ground in vegetated areas. This allows for the establishment of ground points in heavily vegetated areas, where autocorrelation will model the top of the vegetation. This limitation of DSM terrain representation is not unlike that of conventional photogrammetric compilation, only that the manual step of estimating canopy height is not involved, and there is no automated way to designate and represent terrain information that is not on the bare-earth. Autocorrelation algorithms do offer bare-earth extraction which has been proven effective, but for large areas of thick vegetation, the inability to see the ground in the photography will also stop the automated processing from determining the bare-earth. Visualization of the DSM data in cross-sections, while the LiDAR data is also displayed, clearly shows that there are some areas where the tops of canopy are modeled in the DSM, while the LiDAR was able to penetrate.

7 Evaluation of Gross Errors Gross errors may be a result of the algorithm being used, the quality of the input imagery, or unexplained due to the proprietary nature of the software that available off-the-shelf. Two areas- approximately 1000 square feet in size- were identified in these study areas that seemingly were affected by gross errors in the DSM. In these cases, the DSM was lower in elevation than the LiDAR bare-earth data, which would not be expected, based on the LiDAR s better ability to model terrain. These areas were located the shadows of the hilly terrain, and it is suggested that the low image texture in these areas contributed to the measurement blunders. Another example of blunders in this dataset was on the paved road, where image texture was again low. In some areas of the road, the DSM surface was visually degraded, and upon examination, the discrepancies from the LiDAR were close to 2. CONCLUSION In this study, the LiDAR derived bare-earth TIN and autocorrelated DSM differ most in vegetated areas even after the autocorrelated DSM has undergone the same filtering process as the LiDAR data. LiDAR has the advantage over photogrammetrically-derived terrain information (both manual and automatic), in that it only needs one reflected laser pulse instead of two images (paired with extraction) to measure the ground. Another advantage of LiDAR is that the multiple signal returns from each outgoing pulse allow for enhanced differentiation between ground and vegetation. Terrain extraction from imagery does not have these characteristics, but the manual terrain compilation has the advantage over automated extraction in that the human operator can estimate the distance from the canopy surface to the ground. Additionally, manually-compiled terrain information is subject mainly to measurement or interpretation blunders, while automated algorithms can introduce blunders related to assumptions in the data extraction algorithms. In any case, quality control steps should be tailored to the technology and the logical assumptions and probabilities associated with each terrain extraction approach. Traditionally, elevation data was represented by contours, and this is what many Surveying and Engineering clients continue to request today, since they are easily interpretable and there are systems in place that are built around the use of contour data. Contour generation from a LiDAR TIN or an autocorrelated DEM is challenging due to the lack of breaklines and the overwhelming amount of information (high point density). Numerous computer applications, however, do not require contour lines but rather a DEM or TIN. With the advent of Google Earth and Microsoft s Virtual Earth a DSM may even be preferred over a DEM for many applications such as rendering or fly-through visualization. Depending on the application there are obvious advantages and disadvantages of a manually compiled DTM, a LiDAR bare-earth TIN or an autocorrelated DSM. Both approaches that are heavily-reliant on automation; LiDAR and autocorrelation, have their utility depending if a DSM and a bare-earth TIN is required. There are other considerations, mainly cost effectiveness. Depending on flight logistics and mobilization overhead, two separate flights- one for LiDAR, one for imagery- could be cost-prohibitive. A project will rarely ever include just terrain data, but will almost always also include the collection of imagery from which to extract planimetric information and to generate orthoimagery. If imagery is to be collected as a project requirement, the extraction of terrain data from imagery comes at no additional acquisition cost, although there is CPU time and labor associated with quality control and data editing. The autocorrelated DSM extraction in the study was done using BAE s ATE tool in SocetSet. In the future we intend to investigate and consider the use of the new NGATE by BAE which has considerably different algorithms, and also the use of the well established Match-T by Inpho, which applies a very different approach to image matching than ATE. REFERENCES 1 Maune, D. (2007). Digital Elevation Model Technologies and Applications: The DEM Users Manual, 2 nd Edition. The American Society of Photogrammetry and Remote Sensing, pp Burtch, R. (2002). LiDAR Principals and Applications IMAGIN Conference, Traverse City, MI.

8 3 ASPRS LiDAR Committee. (2004). ASPRS Guidelines- Vertical Accuracy Reporting for LiDAR Data, version Gruber, M., Perko, R., Ponticelli, M. (2004). The All Digital Photogrammetric Workflow: Redundancy and Robustness. ISPRS Proceedings, Istanbul 2004, Commission I, WG I/6. 5 Perko, R. (2004). Image Quality: Digital Pansharpening Versus Full Color Film. Institute for Computer Graphics and Vision. 6 Leberl, F., Gruber, M. (2005). ULTRACAM-D: Understanding some Noteworthy Capabilities. Photogrammetric Week 05, Wichmann Verlag, Heidelberg, 2005.