dem generation

7/30/2019 DEM Generation

1/7

AbstractProgress in automated photogrammetricDEMgeneration is

presented. Starting from the procedures and the perform-ance parameters of automated photogrammetricDEMgeneration, the results of some practical tests with largescale images are presented. The DEMs are derived fromimages taken by a digital large-frame aerial camera andchecked by reference data of superior accuracy. In average,a vertical accuracy ofsh 13 cm or 0.20 per thousand ofthe mean flying height above mean terrain has been

achieved. Some recent innovations in digital large-framecameras and in the processing software give hope for evenbetter results. In comparison with results from film-basedcameras, it can be stated that both technologies are able to

produce very dense and accurate DEMs.

IntroductionThe generation of Digital Elevation Models (DEMs) has

become important in recent years. Applications such as theproduction of orthoimages and of3D city models require ahigh accuracy and especially higher production rates. Formany years, photogrammetry has been used as the standardmethod ofDEM generation. Manual methods proved to beaccurate, but are too slow. Correlation of overlapping images

enabled automated procedures but requires editing. Thissemi-automatic production can now be improved by meansof new tools and new procedures. Digital large-framecameras seem to enable a more accurate and more reliableDEM generation due to their higher radiometric and geomet-ric resolution. Two types of cameras are in use for theaccurate photogrammetric DEM generation: large-format framecameras and line scanner cameras. There are advantages in

both types, but this article will deal with frame camerasonly. The occurrence of airborne laser scanning gave newpossibilities to acquire DEMs, especially in difficult land-scapes like forests or built-up areas. The generation of DEMsmay require only one of the two technologies, but also acombination of both methods may be useful from a technicalpoint of view. The performance of automated photogrammet-

ricDEM

generation has to be re-evaluated today becausevarious new tools are available. It is the objective of thisarticle to investigate the performance of a digital large-format camera with regard to DEM generation. A comparisonwith the results obtained with analogue cameras may showwhether the new type of cameras has the same or even a

better performance. Recently announced innovations in theprocessing software will also be presented in order to judge

DEM Generation Using a Digital Large FormatFrame Camera

Joachim Hhle

the current potential of the photogrammetric method for DEMgeneration.

First the categories ofDEMs and the procedures in theautomated photogrammetric DEM generation will be summa-rized in order to understand its performance parameters.

Categories of DEMs and Their ApplicationsThere are various ways to categorize DEMs. First of all, thereare two main types, the Digital Surface Model (DSM) and the

Digital Terrain Model (DTM). The DTM is the bare ground,and the DSM contains elevations on top of buildings andvegetation as well. The applications of the two types aredifferent. The design of highways and other engineeringtasks require a DTM, and the generation of true orthoimagesand the planning of towers for cell phones require a DSM.Furthermore, the accuracy ofDTMs and DSMs can be verydifferent. Highly accurate DEMs may have standard devia-tions of less than 0.5 m; less accurate DEMs may have astandard deviation of more than 0.5 m. The density of thepoints will then differ as well. The data can be collectedand stored in different formats. The applications of the DEMrequire either a triangle (TIN) structure or a grid structure.Table 1 gives an overview of the various DTM categories andtheir applications. The method of acquisition will be

different according to the accuracy requirements. The typeof terrain may have influence on the selection of the dataacquisition method.

Procedures in the Automated Photogrammetric DEM GenerationThe present procedures with up-to-date photogrammetricsystems will be outlined in the following. The DEM genera-tion may start from scratch and a new DEM will be derived

by means of at least two images, each one in several levelsof resolution. The method and results with it are describedin the literature, for example in Glch (1994) and Heipke(1995). Professional photogrammetric workstations containelaborated software packages, and the photogrammetricpractice uses the automated photogrammetric generation of

DEMs on a routine basis with images taken by analoguecameras.If a DEM already exists, its accuracy and completeness

may also be improved. The existing heights are used asapproximations in the generation of the DEM. The correctionto an existing DEM can also be derived from horizontal

PHOTOGRAMMETR IC ENGINEER ING & REMOTE SENS ING J a n ua r y 2 00 9 8

Aalborg University, Department of Development andPlanning, Research Group of Geoinformatics, Fibigerstraede11, DK9220 Aalborg, Denmark ([email protected]).

Photogrammetric Engineering & Remote SensinVol. 75, No. 1, January 2009, pp. 8793

0099-1112/09/75010087/$3.00 2009 American Society for Photogrammetr

and Remote Sensin


2/7

88 J a nua r y 2 0 09 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

parallaxes between two overlapping orthoimages. Theprocess can be carried out automatically. This approach wasfirst presented in Norvelle (1994) and further refined andtested by other authors, for example Georgopoulos andSkarlatos (2003) and Potuckova (2004). Both approacheshave the following steps of production: Acquisition ofimages, georeferencing of images, DEM generation or DEMimprovement, editing, and quality control. These steps areshortly described in the following in order to discuss theperformance parameters and the test results thereafter.

Acquisition of ImagesThe acquisition of images needs careful planning of the

flights. The sensors and the flying altitude have to be chosenso that the required DEM accuracies can be achieved. Cam-eras have to be calibrated and their relationship to othersensors such as Global Positioning Systems (GPS) and InertialMeasurement Units (IMU) has to be known. The imageacquisition has to be completed when vegetation has not yetgrown. Furthermore, the atmospheric conditions and sunangle have to enable good image quality (brightness, con-trast, short length of shadows). The film-based images must

be scanned with a precision scanner. The digital imagesthen have to be converted into image pyramids, compressed,and saved.

Georeferencing of ImagesThe digital aerial images need to be supplemented with the

data of interior and exterior orientation. The exteriororientation of the images is usually determined by aerotrian-gulation. A few ground control points (GCPs) are required.The use ofGPS and IMU can further reduce the number ofnecessary GCPs or avoid them completely. This directgeoreferencing requires calibration of the bore sight,which has to be determined by means of a test field. The so-called Integrated Sensor Orientation uses the GPS/IMU dataand the tie-point measurements in a joint adjustment(EuroSDR 2002). This approach enables high accuracy and is

becoming the standard method for the georeferencing ofimages to be used in the generation of highly accurate DEMs.

DEM GenerationThe generation ofDEMs is accomplished in softcopy worksta-

tions by means of stereo models. It can be done manually,semi-automatically, or fully automatically. The manual workconsists of collecting breaklines and mass points which bothcharacterize the terrain. The semi-automated approachguides the operator to the next point, where it is decidedwhether the automatic measurements should be used or not.For the collection ofDTMs, for example, the operator willnot use measurements on top of houses and trees. Theautomated approach uses correlation techniques. Thematching of corresponding image parts can be area-based orfeature-based. Features are, for example, edges which areextracted from the images in advance. Parameters in thesoftware packages have to be selected according to theterrain type and image quality. The automatically generated

DEMs represent the surface; they are therefore DSMs. Blundersmay occur at image areas with low texture and low contrast.

Editing

The conversion of the DSM to the DTM (bare earth data) andthe removal of blunders requires editing of the raw data.Also, thinning of the data may be necessary. The visualiza-tion of the automatically derived heights as colored dots inthe image pair makes it possible to detect errors by stereovision. The human operator can remove erroneous heights,add manually measured spot heights, and breaklines.Derived contour lines may also be used for checking andediting of the generated DEM. These visual procedures can be

supplemented by automated filtering of the DEM, which willreduce the DSM to bare earth data. Also, blunders in thedigitally correlated data set may be detected and removedautomatically.

Quality ControlQuality control is carried out by means of accurate referencedata. Checkpoints can be derived in the process of aerotrian-gulation or by ground measurements. Their accuracy should

be higher than the accuracy of the DEM points, and thesample size should be sufficiently large. The assessment ofthe accuracy should include the vertical as well as thehorizontal accuracy. The accuracy measures are derived foronly a few samples. The checking of large DEM areasrequires automated methods. In EuroSDR (2006) various

automated checking methods are described and tested. Theyuse aerial images of the same (large) scale as for orthoimageproduction.

Performance Parameters of the DEM GenerationThe imagery should be taken with the proper sensors. Thereis a strong movement to using digital cameras so that acompletely digital work flow can be achieved. One of theadvantages of digital cameras is the higher radiometricresolution than in film-based cameras. Many more (4,096)intensity values of 12-bit (or more) data are stored perspectral band. Details in the shadows are therefore easier torecognize. The geometric resolution (pixel size) of digitalframe cameras is very small, for example 9 mm. The format

of the output image and the parameters of the lens system(camera constant, lens distortion, resolution) are importantfor accurate and economic work. If stereo pairs with 60percent overlap are used, the base/height ratio is consider-ably smaller than at analogue cameras of the 230 mm 230mm format and equipped with wide-angle lenses (typicalcamera constant 153 mm). This unfavorable base/heightratio for the digital large-format frame cameras may reducethe accuracy of the DEMs, if the matching accuracy cannot beimproved by a factor of two when using the same imagescale. Table 2 shows some of the performance parameters forexisting digital large-format frame cameras.

The deliverable images are panchromatic, normal color,and false-color, and are all produced from a single flight

TABLE 1. OVERVIEW ON THE VARIOUS DEM CATEGORIES AND THEIR APPLICATIONS

accuracy density format applications acquisition method

DTM 0.5 m 15 m TIN design of highways and laser scanning &other engineering tasks photogrammetry

0.5 m 550 m grid orthoimages photogrammetryDSM 0.5 m 15 m TIN true orthoimages laser scanning &

photogrammetry0.5 m 550 m grid planning of cell phone towers photogrammetry


3/7

PHOTOGRAMMETR IC ENGINEER ING & REMOTE SENS ING J a n ua r y 2 00 9 8

mission. The color images are produced by pan-sharpening(for example, the DMC or UltraCam) or directly by means ofthe CCD sensors (for example, DIMAC). In the UltraCamcamera the images of four spectral bands (red, green, blue,and near-infrared) are taken by four additional lenses withsmaller camera constants.

The eight CCDs capture images which are used togenerate three large-format images of different spectralcharacteristics. The panchromatic image is fused by meansof four images and nine CCD images

Additional technical details on digital aerial camerascan be found in the literature, for example in Sandau (2005).

In order to meet a specified DTM accuracy, the flyingaltitude above mean terrain can be calculated by formula:

(1)

where h is the flying altitude above mean terrain, sh is therequired DEM accuracy (standard deviation), c is the cameraconstant, b/h is the base to height ratio, and spx is thematching accuracy (standard deviation).

The matching accuracy or parallax accuracy (spx)depends on the quality of the cameras, the contrast andtexture in the image, the pixel size, and the applied algo-rithm. It can be derived by formula:

(2)

where mb is the image scale number.If the matching accuracy and the required DEM accuracy

are known for a camera system, flying height and imagescale can then be found for a DEM project (with its specifiedaccuracy) using Equations 1 and 3:

(3)

The pixel size on the ground or the Ground SamplingDistance (GSD) is then:

(4)

where pel is the pixel size of the digital image.With the known GSD and the number of pixels across

the flight direction, the coverage across track (swath width)can be calculated. All the large-frame cameras use the longerside of the rectangular format across-track in order to reducethe number of strips. This fact is, however, not in favor of ahigh DEM accuracy.

The parameters in the automated DEM generationconsist of

the number of levels in the image pyramids, the size of the matching windows,

GSD pel# mb

Mb 1

mb

c

h.

spx

sh # bh

mb

h

sh# c#

b

h

spx

thresholds for the correlation coefficient, and thresholds for the accuracy of the least squares matching.

The selection of proper values for these parameters has astrong influence on the results.

The density of grid posts can be very high; practicallyeach pixel can have an elevation. Normally, certain gridspacing is selected and an elevation is determined for eachgrid post. Elevations can only be determined at positionswhere good conditions for correlation exist. The estimatedaccuracy of the determined elevation can be visualized bycolored dots which are displayed on top of the stereo pair oron top of an orthoimage. The visual inspection will detecterrors or problems. The automatic detection and elimination

of blunders is an important feature of advanced DEM extractioprograms. The applied methods compare a generated elevatiowith the elevations of the neighborhood. A statistical evalua-tion will decide whether the generated height is a blunder.

The editing of the raw DEM data is an interactiveprocess. Editing tools include the visualization of the DEM acolored dots or contour lines on top of the stereo model.Profiles or perspective views may also be used to detectproblem areas. The operator re-measures spot elevations andsupplements with breaklines and spot elevations. Newcontour lines are generated on the fly, and the operatorcan make decisions whether the generated DEM can beaccepted.

The checking of the DTM can also be done automaticallyIf map data are available, logical checks can be carried out

by software. For example, the elevations at shorelines oflakes should be equal, and heights at creeks and riversshould continuously decrease in the direction in whichwater flows. Filtering of the DEM with a proper algorithm canautomatically reduce the DSM to bare earth data. The per-formance parameters in the editing are the times necessary tcorrect the raw data. The amount of data which can behandled is a performance parameter as well. If the programsof automated DTM generation produce a minimum of blun-ders and gaps, then the editing time will also be short. Thepossibility of using other data (vector maps, orthoimages)during the editing, and/or to represent the DEM in variousforms is an important feature of such editing programs.

To support the quality control ofDEMs, the verticalaccuracy has to be determined. The accuracy measures to be

derived include the Root Mean Square Error (RMSE), thenumber of blunders, the systematic error (average error), andthe standard deviation. Checkpoints of superior accuracy (atleast by a factor of 3) and of a sufficient number (20 or more)are usually used in order to derive the accuracy measures of aDTM (Maune, 2007). The checkpoints can be derived by fieldmeasurements, for example, by means ofGPS. The formulas fothe aforementioned accuracy measures are given in Table 3.

Practical Tests

As it was shown above, the results of the DEM generationdepend on many parameters. In order to meet the specifica-tions of a DEM task, it is necessary to know the flight heightwhich is based on the matching accuracy of the applied

TABLE 2. LARGE-FORMAT FRAME CAMERAS AND THEIR PERFORMANCE PARAMETERS

pixel image size image base at camera base tosize [mm] 60% overlap constant height

producer camera name [mm] [mm] [mm] ratio

Intergraph DMC 12 165.9 92.2 36.9 120 0.31Vexcel / MS UltraCamD 9 103.5 67.5 27.0 101 0.27DiMAC Systems DiMAC 2.0 6.8 71.4 49.0 19.6 80 0.25


4/7


TABLE 3. ACCURACY MEASURES FOR DEMS

Difference from reference data (for a pair i) hiNumber of tested points n

Root Mean Square Error

Maximum difference hmax Definition of a blunder (threshold) h 3 RMSENumber of blunders N

Number of points without blunders n n N

Mean

Standard deviation s Kan

i1

(hi m)2

(n 1)

m

an

i1

hi

n

RMSEKan

i1

h2i

n

TABLE 4. DATA OF THE TESTS WITH THE DIGITAL LARGE-FORMATFRAME CAMERA ULTRACAMD

terrain type built-up areaflight altitude 662 m

image scale 1:6530base/height ratio 0.28GSD 6 cmswath width 1,150 m

DEM generation grid spacing 5 mmethod fully automatic, no

approximationsthreshold correlation coefficient (0.75)editing non

checkpoints number 25accuracy 2 cm (standard deviation)

camera system. It is therefore an objective to know thematching accuracy for the digital camera used. Practical testswill be carried out in order to determine this value for thedigital large-format camera used.

The tests are based on large-scale imagery taken by thedigital large-frame camera, UltraCamD. The swath width was1,150 m, and the ground sampling distance about 6 cm.Altogether, five models were evaluated. The test areas aresituated in the suburbs of Aalborg, Denmark, and all of themcan be characterized as open terrain covered with grass. Afew houses, trees and paths were also present. The differ-ences in elevation do not exceed 30 m, the average slopewas below 10 percent, and the form of the terrain is rathersmooth. Figure 1 depicts one of the test areas. The test areahas texture and contrast, and the quality of the image isgood. The other test areas were similar. Each test areacovered about 4 percent of the generated DEM.

The test areas were also surveyed by means ofGPS/RTK,and about 60 terrain points could be used for checking.

Their accuracy was s 2 cm (standard deviation) andthereby of superior accuracy.

The generation of the DEMs was fully automatic usingthe program Image Station Automatic Elevations, version5.1, of Z/I Imaging (Z/I imaging, 2006). The spacing of thederived grid posts was different in the five models averaging5 m. Editing and filtering of the DEM data was not done.Checkpoints were on the terrain (bare earth) only. Thedata of the test are summarized in Table 4.

The accuracy of the DEMs can be found by comparingthe elevations of the test points with the values obtained byinterpolation in the (automatically) derived DEM grid.Blunders are detected by a threshold which is defined byh 3 RMSE. For the computation of the systematic error(bias) the elevation errors larger than the specified threshold

are removed. The remaining errors are reduced by thesystematic error, and standard deviations are calculated. Theformulas used can be seen in Table 3. Several models have

been processed, and the RMSE, average error and standarddeviation are presented in Table 5 and depicted in Figure 2.

The average value for the root mean square erroramounts to RMSE 14 cm or 0.20 per thousand of the flyingheight above average terrain. The standard deviation is aboutthe same (s 13 cm) because the systematic error is verysmall. The matching accuracy after Equation 2 amounts to6 mm or 0.6 pixel. This accuracy is based on ground truthand can therefore be used for the planning of the flightmission using Equations 1 and 3. A required DEM accuracycan then be achieved. For example, if a specified accuracy

ofsh 0.185 m ( 0.61ft) has to be met, the required flyingheight (above average terrain) for the UltraCamD camera

(used with 60 percent forward overlap) is then:

The images taken have then a scale ofMb 1:8325 and aground sampling distance ofGSD 0.0009 8325 7.5 cm.

Comparison with Results from a Film-based CameraIn order to evaluate the results with the digital camera, afew tests with a film-based camera (Zeiss RMK-TOP, format23 cm 23 cm, camera constant 153 mm) have beencarried out as well. The base to height ratio at the usual60 percent forward overlap is b/h 0.60. The images were

h 0.185m # (101 # 0.27/0.006) 0.185m # 4545 841m.

Figure 1. Orthoimage of the Test Area #5at the DEM test with the UltraCamD.


5/7

PHOTOGRAMMETR IC ENGINEER ING & REMOTE SENS ING J a n ua r y 2 00 9

TABLE 5. RESULTS OF DEM GENERATION WITH THE DIGITAL LARGE-FORMAT FRAME CAMERA ULTRACAMD

accuracy measure / test # 1 2 3 4 5 average

number of check points 156 25 49 25 111 73RMSE [cm] 23 11 6 17 12 14( 0.22 h)average error [cm] 2 2 4 10 1 1standard deviation [cm] 23 11 4 13 12 13 ( 0.20 h)matching accuracy [mm] 10 5 3 7 5 6 ( 0.6 pel)

Figure 2. Examples of achieved absolute DEM accuracywith large-scale images (1:6 300) with the digital large-format camera UltraCAMD.

TABLE 6. RESULTS OF DEM GENERATION WITH A FILM-BASED LARGE-FORMATFRAME CAMERA (RMK-TOP)

accuracy measure/test # 1 2 3 average

number of check points 2,033 116 94 748RMSE [ h] 0.18 0.13 0.25 0.19standard deviation [ h] 0.18 0.09 0.07 0.11matching accuracy [mm] 17 12 23 17matching accuracy [pel] 0.8 0.6 1.5 1.0

taken from different flying heights, and their scale variedbetween 1:3 000 and 1:25 000. The photographs weredigitized in a precision scanner with a pixel size of 15 mmand 21 mm. The three test areas can be characterized as amixture between open terrain and built-up areas. The DTMgeneration took place with different spacing of the grid posts(1 m and 25 m). Elevations of new points were interpolatedat the position of the checkpoints. The number of check-points was above 90, and their accuracy was at least threetimes better than the automated generated elevations. Theresults of the comparison between the elevations of check-points and the automatically generated (and interpolated)elevations are presented in Table 6.

The achieved accuracy for the three models was in

average RMSE 0.19 per thousand of the flying height aboveaverage terrain elevation. The computed matching accuracywas in average spx 17 mm or 1 pixel. The result for thematching accuracy is based on accurate reference values,which are determined from images of a much lower flyingheight or from ground truth. Details on this investigation arepublished in Hhle and Potuckova (2006).

Other investigations on DEM generation with analoguecameras derived similar results. In Saleh and Scarpace(2000) for example, scanned photographs with standard

base/height ratio (b/h 0.6), but of various pixel size, wereused. An average matching accuracy ofspx 18 mm can bederived from the published RMS values and the flightparameters.

A matching precision has been derived for terrain of

different slope in Karras et al. (1998). The achieved valuesdiffer between 0.4 and 0.7 pixels. The reference values werederived from manual measurements with the same images.

Discussion of the ResultsThe achieved accuracy ofDEMs with a digital large-framecamera and large-scale images (mb 6300 and GSD 6 cm)

was s 13 cm or 0.20 per thousand of the flying heightabove average terrain. There was no significant difference fromthe results with a film-based camera. The digital camera used(UltraCamD) can obviously compensate for its drawbacks(unfavorable base/height ratio and smaller format) by means oa higher matching accuracy. The computed matching accuracyin the tests amounted to spx 6 mm at the digital camera anspx 17 mm for the analogue camera. The reasons for thehigher matching accuracy are very likely the higher radiomet-ric resolution and the way in which the forward motion iscompensated. The analogue cameras move the film mechani-cally, but the digital cameras integrate the intensity values(digital numbers) of the pixels during the time interval ofexposure. This electronic approach leads to higher image

quality and the matching accuracy certainly benefits from it.The investigation with the digital large-format camera is

based on five stereo models. They contained open terrain anbuilt-up areas. Other categories of landscape, e.g., woodedareas, will produce different results. In order to specifyaccuracy standards (as it is required in many countries), testswith similar and other terrain types have to be carried out.

New DevelopmentsIn recent months, some improvements in the tools regardingthe DEM generation have become known. Furthermore, thedigital camera is widely accepted as the image acquisitiontool, and the production ofDEMs, orthoimages, and photore-alistic 3D models can now be realized in a completely digita

workflow with a high degree of automation and highproduction rates.

The new UltraCAMX of Microsoft Photogrammetry hasa small pixel size in the nine CCDS (7.2 mm), a new lenssystem (c 100 mm for panchromatic and c 33 mm forcolor and color-infrared images), and a frame rate of 1.35seconds (Gruber, 2007). These improvements may changethe methodology in DEM generation. Pan-sharpened colorimages of 80 percent forward overlap at high groundresolution (for example with GSD 5 cm) can be pro-duced. The DEM can then be derived from multiple imagesespecially if a side overlap of 60 percent is used. Blunderand gaps in the DEM will be reduced. The accuracy of theDEM can further be improved by applying a calibrationgrid. Lens distortion and possible shifts of the CCDs due

to a temperature change during the photo flight canthen be corrected. The correction grid may be created by


6/7


self-calibration in the aerotriangulation program and thenapplied in the DEM program (Jacobsen, 2007).

Other improvements in software concern the DEMprograms. In several programs (e.g., Match-T 5.1 of InphoGmbH), the single stereo model is replaced by a number ofimages which contribute to the generation of a very densepoint cloud. Besides the X-Y-Zcoordinates, each point hasalso information about color, correlation, and accuracyvalues. From the dense point cloud, an accurate and reliableDEM is derived by noise filtering, reduction to bare earth,

and thinning.Improvements are achieved in the matching algorithms.

In the program NGATE ofBAE systems, for example, thematching takes place for each pixel which leads to verydense point clouds. Furthermore, the size of the searchwindow is adapted to the height differences in the windowarea. In built-up areas, edges are derived from the imagesfirst. Each pixel lying on an edge is then matched witha pixel of the corresponding edge. The results are morereliable so that considerably less editing work is required.More information can be found in Zhang et al. (2007).

New developments in the editing ofDEM data happenedas well. Special editing stations like DTMaster of InphoGmbH were created. The program package (version 5.01) canhandle up to 50 million points at the same time and uses

stereo vision and stereo measurements for checking andimproving ofDEMs in an interactive process. Automatedfeatures are also integrated, for example, plausibility checks,deletion of double measured points, and detection of blun-ders and gaps. Such editing stations can handle point cloudsfrom photogrammetry and from laser scanning. The tasks andthe problems in editing are the same for both technologies.The developments of editing programs in both fields havestimulated each other. Improvements in the editing are alsovery necessary because this work is the bottle neck in theefficient generation of accurate and reliable DEMs.

The quality control ofDEMs requires reference data ofsuperior accuracy. Ground surveying is usually used foraccurate DEMs, but it is expensive and only a few check-points are used. Automated methods of checking are

developed and tested in a recent EuroSDR project (EuroSDR,2006). The methods are based on photogrammetry. Theapplied imagery is taken from lower flying heights than theone for DEM generation. The results showed that an auto-mated quality control is possible and efficient. More detailson this research work are published in Hhle and Potuckova(2006) and Hhle (2007).

Relation of Photogrammetric DEM Generation and Airborne LaserScanningAccurate DEM data of high-density can also be derived bylaser scanning. This acquisition method does not needsunlight and texture on the surface. The connection of stripscannot be done by single points but by area elements only.

The achievable accuracy depends on the performance of theGPS/IMU. An in-flight calibration of the boresight is necessaryand requires a suitable test field. Positional errors may occurand should always be checked. Laser scanning has advan-tages in urban and in forest areas.

When applying photogrammetry, the images within andacross the strip are connected by tie-points. This network ofrays can be used to determine the parameters of exteriororientation. GPS/IMU data are not necessary. The structureand breaklines of the terrain can be derived and contributeto DEMs of high quality. The images taken may be used forcompilation of vector maps and for orthoimage productionas well. Both technologies have their advantages and thetasks will decide which one should be used.

A combined use would be ideal. Laser scanners alreadyuse digital medium-format cameras. By means of the images,the point clouds can be interpreted much better. OneGPS/IMU unit can be shared when both systems are com-

bined. The processing of the data including editing has alsoto be integrated into one system. Whether such a combina-tion is an economic solution has to be evaluated.

ConclusionsDuring the last few years many changes have occurredin the generation ofDEMs. Automated photogrammetricmethods can now use images of digital large-format camerasas well as efficient matching and blunder-detectionalgorithms. The editing tools have become more efficientusing automatic filtering and interactive procedures. Thepresented results of practical tests with the UltraCamDcamera indicate that vertical accuracies ofs 0.13 m (or0.2 per thousand of the flying height above average terrain)can be achieved using images with a ground resolution ofGSD 0.06 m. The derived matching accuracy is in averagespx 6 mm. This accuracy is based on ground truth andcan therefore be used for planning of the flying height (orthe image scale and ground sampling distance) in order tomeet a required accuracy. The spacing ofDEM posts can be

as small as the ground sampling distance. Additionalinvestigations revealed that there was no significant differ-ence from the results with a film-based aerial camera whichhas a larger format and a longer focal length than the useddigital camera.

Recent developments concern improvements in thegeometric resolution (pixel size) at the digital large-formatframe cameras. Systematic image deformations can behandled by improved postprocessing and use of calibrationgrids. A change from the stereo model approach to amulti-image approach may also improve the accuracies ofDEMs. Images should then be taken with a higher longitu-dinal and lateral overlap. Changes in procedures concernthe use of approximate DEM data and the derivation ofcorrections for an approximate DEM. This can efficiently

and accurately be done by means of automated parallaxmeasurements in two orthoimages. The editing of the rawDEM data remains the bottle neck in the automated DEMproduction; it requires interaction with the human opera-tor. Such interactive editing can use stereo vision and mapdata for checking, decision-making, and re-measurement.Digital Surface Models and Digital Terrain Models can beproduced at the same time. In comparison to lidar, thephotogrammetric method has several advantages, forexample, easy interpretation of objects, measurement ofaccurate break lines and of characteristic terrain points,and universal use of images for other mapping tasks. Thedigital large-frame camera is also a valuable supplement toa lidar system, and a simultaneous use of both systemsmay happen in the future.

AcknowledgmentsThe author wants to thank M. Potuckova for support inprogramming. Students of Aalborg University contributed tothis article with ground surveying and processing of thedata. B. Nrskov is thanked for her improvements of theEnglish language.

ReferencesEuroSDR, 2002. Integrated Sensor Orientation, Test Report and

Workshop Proceedings, EuroSDR Official Publication No. 43,ISSBN- 3-89888-864-9, 297 p.


7/7

dem generation

Documents