Processing of laser scanner data—algorithms and applications

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  • .ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147

    Processing of laser scanner dataalgorithms and applicationsPeter Axelsson )

    Department of Geodesy and Photogrammetry, Royal Institute of Technology, 100 44 Stockholm, SwedenReceived 5 October 1998; accepted 2 December 1998


    Airborne laser scanning systems are opening new possibilities for surveys and documentation of difficult areas andobjects, such as dense city areas, forest areas and electrical power lines. Laser scanner systems available on the market arepresently in a fairly mature state of art while the processing of airborne laser scanner data still is in an early phase ofdevelopment. To come from irregular 3D point clouds to useful representations and formats for an end-user requirescontinued research and development of methods and algorithms for interpretation and modelling. This paper presents somemethods and algorithms concerning filtering for determining the ground surface, DEM, classification of buildings for 3DCity Models and the detection of electrical power lines. The classification algorithms are based on the Minimum DescriptionLength criterion. The use of reflectance data and multiple echoes from the laser scanner is examined and found to be usefulin many applications. q 1999 Elsevier Science B.V. All rights reserved.

    Keywords: laser scanner; 3D City Models; classification; modelling; filtering; MDL

    1. Background

    Laser scanner systems available on the market arepresently in a fairly mature state of art, where mostof technical hardware difficulties and system integra-tion problems have been solved. The systems arevery complex, being more a geodetic system on thedata acquisition part and more a photogrammetricsystem on the data processing part.

    What very much remains is the development ofalgorithms and methods for interpretation and mod-elling of laser scanner data, resulting in useful repre-sentations and formats for an end-user. The basic

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    processing task of separating the bare ground surfacefrom objects on the surface, i.e., defining a digitalelevation model, DEM, as a subset of a measureddigital surface model, DSM, needs to be investigatedmore profoundly. Also, more application dependenttasks, such as classification of objects, 3D Citymodelling and engineering surveys need to be stud-ied. Existing software are, in general, propriety soft-ware or university products. Commercial softwarenot connected to a specific laser scanner system areoffered by a very limited number of companies.

    2. Laser scanners and laser scanner data

    A laser scanning system produces data which canbe characterised as sub-randomly distributed 3D pointclouds. These point clouds may contain more infor-

    0924-2716r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. .PII: S0924-2716 99 00008-8

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147 139

    Fig. 1. Reflectance image of road survey.

    mation than a 2.5D surface model, in which theelevation has a unique z-value as a function of x andy. This means that vertical walls in certain cases canbe seen as truly vertical, surface points beneathbridges can be measured and volumetric estimationsof vegetation can be carried out. Elevation data canbe acquired with different attributes depending onapplication and laser scanner system. Some of theseattributes affecting filtering and modelling algo-rithms are:fl point densityfl registrations of multiple echoes

    .fl amplitude registration reflectance .The point density is dependent on flying height

    and also on system dependent factors, such as plat-

    form velocity, field of view, and sampling fre-quency. The point density should be adjusted accord-ing to the application. For modelling applications,like 3D City Models or power line surveys, therequirements of the point density are very differentfrom the task of generating DEMs with grid sizes of510 m.

    The second attribute, multiple echoes of one laserpulse, can also be registered by some systems. Thiscan be of importance in filtering and modellingalgorithms related to vegetation and ground surfaceseparation and volume estimations in forestry appli-cations. Multiple echoes can also be found at theedges of buildings, thus indicating a very fast changein elevation.

    Fig. 2. Perspective view of elevations.

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147140

    The last attribute, amplitude or reflectance regis-tration gives radiometric information about the sur-veyed area. The reflectance can be seen as an imagein a very narrow wave length band. It can be used inclassification algorithms, e.g., separating paved areas

    .from grass land see Figs. 1 and 2 .

    2.1. Data used in the examples

    Data from the Saab TopEye system have beenused in the examples presented in this article. Theprimary sensor of the TopEye system is the laser

    .range finder LRF . The LRF sensor measures dis-tances between the aircraft and the ground at afrequency of 7 kHz with up to four distances recordedby each laser pulse. It has a sampling density of0.255 m at flying heights of 501000 m and afootprint of 0.12.5 m. The pulsed laser beam isscanned across the track of the helicopter or airplane,creating a Z-shaped pattern. The position and attitudeof the helicopter are determined by differential GPSand INS. Ground points are measured with a nominalaccuracy of 0.103 m, depending on altitude.

    Recent development of the TopEye system makesit possible to register the amplitude of the returninglaser pulse. Even if the noise level of the reflectancedata is higher than for a film- or high resolutionCCD-image it can still be used in some applications.

    3. Processing of laser scanner data

    The processing of laser scanner data often aims ateither removing unwanted measurements, either in

    the form of erroneous measurements or objects, ormodelling data given a specific model. Removingunwanted measurements, as in the case of finding aground surface from a mixture of ground and vegeta-tion measurements, is in this context referred to asfiltering. The unwanted measurements can, depend-ing on application, be characterised as noise, outliersor gross errors. Finding a specific geometric orstatistic structure, as buildings or vegetation, is re-ferred to as classification. The generalisation, finally,of the classified objects is referred to as modelling.Filtering, classification and modelling are thus de-fined according to aim and not to method.

    Some information in the original sub-randomlydistributed 3D point clouds are lost if data are inter-polated into a regular grid, i.e., a DSM. The loss ofinformation can be significant, especially if multiple

    .echoes are registered in forested areas c.f. Fig. 16since points with similar xy-coordinates but at differ-ent elevation are difficult to represent in a regulargrid. For this reason, we believe that original datashould be used in the filtering and modelling processuntil an object dependent representation and general-isation can be made. For some application, e.g.,extraction of power lines, this is almost a necessitysince a power line must be described as a true 3Dobject and not in 2.5D.

    Most applications will require special algorithmsand strategies for the classification and interpretationof LRF data, but one task can be seen as general tomost of these applications and that is the separationof objects from the ground surface. Once the objects

    Fig. 3. Connecting ground surface to LRF data.

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147 141

    Fig. 4. Separating ground from objects.

    are separated from the ground surface, they can betreated by various algorithms according to the appli-cation at hand.

    Following these ideas, a strategy for the interpre-tation of LRF data is formulated based on elevationdata but with the possibility of adding other types ofsensor data if available. The strategy can be sum-moned as:fl Use original LRF data as long as possible, prefer-

    ably in a TIN structure for easy access of neigh-bouring points,

    fl Separate surface and objects on the surface,fl Develop application dependent algorithms for ob-

    ject classification and modelling.Other data types, such as reflectance data or

    information of multiple echoes from the LRF, orexisting images and GIS data, should be used and

    included in the processing if available c.f. Haala, 1997; Hug, 1997 .

    3.1. General task finding the ground surfaceHere, a method is developed where a surface is

    .connected from below to the point cloud see Fig. 3 .Only the principles of the method are described hereto give a background to the application dependentalgorithms. The surface is allowed to fluctuate withincertain values. These fluctuations can be controlled

    .by, e.g., Minimum Description Length MDL mod-els, constrained spline functions, active contourmodels like snakes or geometrical thresholds forelevation differences. Some characteristics of theapproach is:fl A ground surface of connected points in a TIN is


    Fig. 5. One classified scan line of suburban area classified in ground, vegetation and buildings with breakpoints.

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147142

    Fig. 6. One classified scan line of urban area.

    fl It bridges over large surface objects, e.g., indus-trial plants,

    fl The surface goes through the original data points,fl Low ground surface points will always be in-

    cluded.An implementation of a simplified version of the

    approach was carried out. The implementation analy-ses each scan line at a time and not at a wholesurface. The implementation fails if no ground sur-face is present in a scan line, but works satisfactory

    .in most cases see Fig. 4 . A surface-based imple-mentation based on TIN will be carried out.

    3.2. Classification of buildings

    Delineated surface objects consisting of unstruc-tured point clouds linked to a TIN are processed byarea-based methods looking at one object at a time.A classification tool is developed that classify sur-face objects in the two classes buildings and vegeta-tion. The ability of the laser to penetrate vegetationand thus giving echo from several heights makes itpossible to distinguish between the two classesman-made objects and vegetation. The classificationprocedures is based on an implementation of the

    MDL criterion for robust estimation Rissanen, 1983;.Axelsson, 1992 . A cost function is formulated for

    the two classes buildings and vegetation based on thesecond derivatives of the elevation differences. Asimplified implementation based on the scan lineswas carried out.

    The model assumes that buildings consist of con-nected planar surfaces. Neighbouring TIN facets onthe same plane will have the same orientation withina noise level. Similarly, neighbouring scan line pointswill lie on a straight line with the second derivativesof elevation differences zero. Roof ridges and otherchanges of direction of the roof will cause a non-zero

    Fig. 7. Scanned aerial photo of the surveyed area. The arrowindicates the scan shown in Fig. 6.

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147 143

    value of the second derivatives. The geometric modelof buildings is thus formulated along a scan line as:

    E2 zs0 point g Straight line segment 1 .2Ex

    E2 z/0 point g Breakpoint2Ex

    where x is the direction along the scan line and zthe elevation.

    The cost function, or description length DL, of ..the building model contains three parts see Eq. 2 .

    fl A parametric model for the zero line of thesecond derivatives. The cost for the model isconstant.

    fl A statistical model for the assumed Gaussiandeviations from the parametric model.

    fl A statistical model for the breakpoints which areassumed to have random behaviour similar to

    .that of vegetation .

    DL sDL q DL qDL n .buildings par roof gauss roofqDL n 2 .break break

    whereDL sC ;par constant

    .DL s lb Lr ;roof . .. .DL s lb sr q 1r2 lb 2Pe ;gauss

    w . . . .xDL s lb R r q lb R r ;break x y zn qn sn ;roof break tot

    Fig. 8. LRF data automatically labelled by MDL using the costfunction described in Section 3.2 as ground, vegetation and build-ings with breakpoints.

    Fig. 9. LRF data displayed as a shaded perspective TIN-model.

    L length or size of building;R defines the size of the search space for the

    building;s 2 the variance of the residuals; resolution, or accuracy, of data;lb binary logarithm with base 2

    Fig. 10. LRF data automatically labelled as ground, vegetation andbuildings with breakpoints. Only the breakpoints of the buildingare displayed. Building points between breakpoints have been leftout.

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147144

    Fig. 11. Step 1: separating ground and objects; 76304 pointsclassified as ground, 23696 points classified as objects.

    Vegetation is modelled as points with randomlydistributed second derivatives:

    E2 z/0 Random behaviour of vegetation points2Ex

    3 .

    where x is the direction along the scan line and zthe elevation.

    The cost function for the vegetation model con- .tains only one part: i A statistical model for vegeta-

    Fig. 12. Step 2: separating vegetation and lines; 22160 pointsclassified as vegetation, 1536 points classified as power lines.

    Fig. 13. Step 3: final Hough classification, 76 304 points classifiedas ground, 23024 points classified as vegetation, 672 pointsclassified as power lines, divided on five parallel lines in 2D.

    tion which are assumed to have random behaviour .similar to that of breaklines :DL sDL nveget rand tot

    R Rx y zDL s lb q lb . 4 .rand A minimum is located for the building model

    where the optimum number of breakpoints is bal-anced against the Gaussian deviation. The minimumcost is compared to the cost of the vegetation modeland a classification is made depending of whichvalue is lower. Examples of the classification can beseen in Figs. 510. A surface-based implementationbased on TIN is under development.

    Fig. 14. Accumulated sums of the eight largest cells on the a-axis.

  • ( )P. Axelssonr ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138147 145

    Fig. 15. The eight largest cells of the peak on the a-axis in Fig.14.

    The results from the classification will be input tothe next part of the processing, namely object mod-elling. The modelling should lead to a general de-scription of the building in a CADrvector represen-tation. A likely solution is a segmentation based onTIN facets and detected breakpoints in combinationwith a simultaneous least squares adjustment of thesegmented planes.

    3.3. Classification of electrical power lines

    The surveying and modelling of electrical powerlines is an application suitable to laser scanning.Using a helicopter as platform makes it possible tofollow the line very precisely and to get a dense

    point distribution. The classification procedure isdivided in the following three steps.

    fl Separation of ground surface and objects usingthe model described in Se...


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