managing and processing lidar data within grass...proceedings of the open source gis - grass users...

Proceedings of the Open source GIS - GRASS users conference 2002 - Trento, Italy, 11-13 September 2002

Managing and processing LIDAR data within GRASSM. A. Brovelli , M. Cannata and U. M. Longoni

Politecnico di Milano – Campus Como, Via Valleggio 11 – 22100 Como, Italy++390223996517, fax ++390223996519, e-mail [email protected],

[email protected],[email protected]

1 Introduction

LIDAR (Light Detection and Ranging) is one of the most recent technologies insurveying and mapping. The LIDAR is based on the combination of three different datacollection tools: a laser scanner mounted on an aircraft, a Global Positioning System(GPS) used in phase differential kinematic modality to provide the sensor position and anInertial Navigation System (INS) to provide the orientation. The laser sends towards theground an infrared signal, which is reflected back to the sensor. The time employed bythe signal, given the aircraft position and attitude, allows us to compute the earth pointelevation. In standard conditions, taking into account the flight (speed 200- 250 km/hour, altitudes500-2000m) and sensor characteristics (scan angle �10-20 degrees, emission rate 2000 –50000 pulses per second), the earth elevations are collected within a density of one pointevery 0.5-3 meters.The technology allows us therefore to obtain very accurate (5-20 cm) and high resolutionDigital Surface Models (DSM). For many applications the Digital Terrain Model (DTM)is needed: we have to automatically detect and discard from the previous DSM all thefeatures (buildings, trees, …) present on the terrain. The elaboration procedure has beenimplemented within GRASS.

2 LIDAR technology

DEM (Digital Elevation Model) is a continuous mathematical model representing theshape of the surface, i.e. the elevation as function of the cartographic coordinates North-East or a function of latitude and longitude.We can distinguish between two kinds of DEM: the digital surface model (DSM), whichdescribes the Earth’s surface, including all the objects on the ground; the digital terrainmodel (DTM) reproducing the ground “natural” surface, i.e. the bare Earth’ s surface.Both there two surfaces are being widely used in many fields such as topographicmapping, urban planning, ecological and environmental studies, flood prevention anddrainage mapping , forestry, landscape design and infrastructure construction,maintenance and management. Moreover with the 3-D GIS development the DEMsassume even more importance, because of the central role played by topographic datawithin the GISs.The conventional techniques used to collect altimetric data include land surveying andaerial photogrammetry. In the last years a new method to measure the topography of theEarth’s surface has attracted interest because of its high accuracy, low time-consumptionand competitive or lower costs than earlier methodologies. This method, known asLIDAR (Light Detection and Ranging) or ALSM (Airborne Laser Swath Mapping) has avery simple measurement principle. It is given by the combination of three differentequipment: a laser scanner, the global navigation system (GPS) and an inertial navigation

2 Managing and processing LIDAR data within GRASS

system (INS). The laser scanner is installed on the aircraft as the phogrammetric camerausually is. Laser pulses are emitted towards ground with high repetition rates per second(2000 up to 50000) and are reflected back to the instrument. A mirror inside the lasertransmitter rotates in a sweep motion perpendicular to the direction of flight in order toblanket the surface of the Earth in a strip buffering this direction (swath width of up to75°). The time each pulse takes to reach the ground and return back and the angle fromthe nadir at which it has been emitted is used to determine the relative position of thereflecting ground spot (with size from 60 cm up to 3m depending on the flight height)with respect to the laser scanner emission point. The sensor location is determined by aGPS receiver working usually at one second sampling rate, while the INS provides itsorientation characteristics, namely the pitch, the roll and the yaw angular values.The laser data are then combined with the sensor location and orientation to give thecoordinates X, Y, Z of the laser footprint on the surface of the terrain. In effect, due to thespot size, some pulse may be partially reflected by objects at different height and partiallyby the terrain. The sensor collects at least two (up to several) returns for a single pulseemitted. The data recorded as ‘last pulse’ (the last return of a single pulse) has the greatestprobability to detect the ground. Moreover the cases corresponding to ‘first pulse’ almostequal to ‘last pulse’ show homogeneity in the geometric characteristics of the terrain spotilluminated.On the opposite a considerable difference between them can be a clue for the presence ofobjects at different heights (vegetation or edges of objects, such as buildings, cars,transmission lines, etc.).A part from this classification no chance exists to directly recognise whether thereflecting point belongs to the bare Earth or to an object.To generate the DTM data must be then filtered in order to eliminate vegetation andbuildings or generally speaking off-ground points. The paper discusses the strategy wehave adopted to this aim.

3 DTM generation

LIDAR technique can not distinguish between terrain and off-terrain points. Thereforethe generation of a DTM needs a method to cluster the observations into these twogroups. As the aim is the DTM computation starting from the natural terrain heights only,if points with uncertain classification remain, they will not be used in the finalinterpolation. Artificial features, like bridges, elevated road, have been classified asobjects and therefore removed.In this paragraph the designed theoretical filtering method is presented, while in the nextsome application examples are shown.A variety of algorithms for automatically extracting DTM is available in literature (seefor example [1], [2]). Recently a comparative test has been set up within the frameworkof International Society of Photogrammetry and Remote Sensing Commission III, WGIII/3 in order to evaluate the performance of the existent filtering methods with differentterrain and surface morphologies and with different point densities.The algorithm we propose was originally designed for LIDAR data in urban areas andwithout information on pulse reflectance ([3]). In this new version it has been testedunder different scenarios and it takes advantage also of reflectance data.The algorithm works in the following sequential steps. The first one consists of an outlierrejection. Data has been interpolated by bicubic spline functions with Tychonovregularisation in a least square approach.The spline step (So) depends on the planimetric resolution of the raw data and has beenchosen 3 up to 4 times this parameter. The Tychonov regularisation parameter (�o),introduced to avoid local and global singularity in the least square approach (in case ofzones where observations are missing) has been chosen in order to assure the regularity ofthe surface, minimising the curvature in empty areas. Imposing to �o a high value a

M. A. Brovelli , M. Cannata, U. M. Longoni 3

surface with a behaviour quite different from an exact interpolator is obtained: the surfacefeels as little as possible the influence of possible outliers. The analysis of the histogramsof residuals between observed and interpolated values shows a bimodal behaviour (Figure1) and suggests the empirical threshold (To) being adopted. Data corresponding toresiduals exceeding the threshold are considered as outliers and removed.

Fig. 1 –Histogram of differences between observed and interpolated values.

Data has been subdivided in tiles (the dimensions depending on the spline steps, havingfixed that each tile contains 200 x 200 splines).The second step allows to detect the edges of the surface objects: an edge is a boundarybetween two different regions, i.e. a significant change in the height value correspondingto a small shift of the horizontal position. The implementation of an algorithm to detectedges is complicated by the non regularity of the distribution of the observations: in thiscase we can not directly adopt the solution provided by the image analysis theory. Twoapproximations of the DSM are then computed by means of bilinear (spline step Sg) andbicubic (spline step Sr) spline functions with Tychonov regularisation in least squaresapproach. Theoretical considerations suggest that we should regularise the surfacesminimising respectively for the two surfaces the gradient and the curvature. In the firstcase a low regularisation parameter (�g) brings the interpolating functions as close aspossible to the observations, whereas in the second one the high value chosen (�r) gives arough and loose-fitting surface.Starting from the bilinear spline surface:

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is computed.The imposition of a unique threshold to the gradient magnitude is not suitable because ifwe choose a low value we will not discriminate between an actual edge and a possible


measurement noise and if we choose a high value we will detect only very sharp heightchanges. The basic hypothesis is that a noise corresponds mostly to an isolatedobservation or, at least, adjacent noises are generally not organised in a regular shape.Vice versa an edge shows a regular, chain-like behaviour.The computation of the direction of the edge vector

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can strengthen, as we will show, the classification. Two thresholds for the magnitudegradient, named high (Tg ) and low (tg ) threshold, are set. Every point P where themagnitude gradient exceeds Tg is classified as possible edge point; for every point wherethe magnitude gradient is lower than Tg but exceeds tg we find, along the direction of themaximum direction of gradient rise (perpendicular to the direction of the edge vector), thetwo neighbouring and opposite points. If these points P1 and P2 have the same edgedirection of P ( gPP 1

�� , gPP 2�� , g� threshold to be set up) and if the

magnitude gradient in the eight nearest neighbouring points exceeds Tg at least in two ofthem, the point will be classified as a possible edge point. In other cases it is classified asnon-edge point.Another problem related to edge detection is that the edge output is usually thicker than aconnected unit-width chain around the boundary of an object. In fact, analysing themethod we proposed, one can observe that theoretically the maximum of the gradientmagnitude corresponds (as shown in Figures 2 and 3) both to the edge point and to itsprevious nearest neighbour.

Fig. 2 – DSM with one edge Fig. 3 – DSM gradient

In a real case, due to noise presence, the situation becomes even more intricate: in samecases (see for example Figures 4 and 5) the maximum of the magnitude gradient fallsoutside both the edge and the corresponding object.

Fig. 4 – DSM with one edge and noise Fig. 5 – DSM gradient


Thus a further criterion has been introduced. The residuals between the observations andthe values interpolated from the bicubic spline DSM give suggestion about theclassification of the points: as it is clear in Figure 6 positive and negative residualscorrespond respectively to in-object edge points and terrain points.

Fig. 6 – Analysis of the sign of the residuals.

Applying the method to the DSM shown in Figure 4, we have two points exceeding thegradient threshold (dashed line in Figure 5). Moreover from the observations and thecomputed DSM (Figure 7) we obtain the residuals (Figure 8) whose signs show thecorrect edge point (in the circle).

Fig. 7 – Observations and computed DSM Fig. 8 – Residual signs

The analysis of the joined distribution of the magnitude of the gradient and of the sign ofthe residuals shows (see Figure 9) the stochastic independence of these two terms: thetwo classifications are therefore independent and can be applied separately.

Fig. 9 – Example of distribution of the two characteristics: residuals ( x axis) and gradientmagnitudes (y axis). The points in the upper-right rectangle belong to object edges.

Observation – interpolated value positive ��in-object edge point negative = terrain pointIntepolating

surface+_


To have a proof of the algorithm performance in the Figure 10a-10b-10c an actual detailof the LIDAR DSM is shown: in the first case (a) a low magnitude gradient threshold hasbeen applied, in the second (b) a high one while in the third (c) the double classificationhas been take into account. It is apparent that the new method produces more accurateand thinned edges.

Fig. 10 – Application of low (a) and high (b) threshold to the gradient magnitude; theoutput with the new method (c).

Once detected the edges, the aim is to fill-in the objects they limit. The simplest idea isthat the inner part of an object has generally a greater height than its edges. But thisconsideration can not be true for the vegetation and in some cases also for buildings withparticular roofs. Furthermore sometimes the edges, due to classification errors, do notrepresent a closed line. Finally the presence of noise in the observations makesinadequate the method. Some tests has to be added before we apply a region growingalgorithm.The data are then rasterized with the minimum resolution rd depending on their rawdensity. For each cell the presence of points with double pulse is evaluated (differencebetween first and last pulse greater than Td).Starting from the cells classified as ‘edge’ and with only one pulse, all the linked cells arefound and a convex hull (Figure 11) algorithm is applied on them, computing at the sametime the mean of the corresponding heights (mean edge height). The points inside theconvex hull are classified as object in case their height is greater or equal to thepreviously mean computed edge height.

Fig 11- Convex hull of a set of points.

Obviously the method proposed works better in case of data with high planimetricresolution and it is practically inapplicable with point densities lower than 0.18 points persquare metre, corresponding to point spacing of about 2.0 - 3.5 m.

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Anyway the algorithm fails in some cases: the simplest we can recall is for instance thecase where we have unusual roofs with pitches at different heights. In this example partof the building was identified as object and part as terrain. Analogously cases of terrainmisclassification in the procedure output remain.To overcome this problem, a bilinear spline interpolation (spline step Sc) with Tychonovregularising parameter �c only on the points classified as ground has been performed. Theanalysis of the residuals (�) between the observations and the interpolated valuescompared with two thresholds tc ,Tc show four cases:� if the point is classified as ground and � > Tc , it will be reclassified as object;� if the point is classified as ‘double pulse ground’ and � > Tc , it will be reclassified as

‘double pulse object’ (edge or vegetation);� if the point is classified as object and |�| < tc , it will be reclassified as ground;� if the point is classified as ‘double pulse object’ and |�| < tc, it will be reclassified as

‘double pulse ground’.

The procedure can be eventually iterated so many times as the visual analysis indicate wehave definitely solved or reduced the ambiguous cases; generally only one or twoiterations are enough.

Finally the last step consists in the DTM computation. A bilinear spline interpolation(spline step Si) with Tychonov regularising parameter �i only on the points classified asground has been performed. The heights can be computed in grid form with resolution rior in spread points.

The method was implemented into the public domain software for geographicinformation systems GRASS (Geographic Resources Analysis SupportSystem) [4]. The developed commands, considering the LIDAR data nature, wereintegrated into the set for the site data analysis. Each new command corresponds to aspecific procedure step in such a way as to provide the user with the possibility of besttuning, as they are required, the various parameters.

Therefore the commands now available are:� s.edgedetection: to detect the edges of the objects. The output contains the

classification of the nature of the measurement points (terrain, object, uncertain). Theparameters to be set are: the spline steps Sg , Sr in the directions East-West and North-South, the Tychonov �g regularising parameter with bilinear splines, the twothresholds tg and Tg for the gradient magnitude, the threshold g� for the edgedirection, the Tychonov �r regularising parameter with bicubic splines (sign analysis).

� s.growing: to apply the region growing procedure. The input files contain the outputof the previous command and the information of the first and last pulse. The outputcontains the cell classification (terrain, terrain with double pulse, object, object withdouble pulse). The parameters are: the classification threshold allowing to start fromthe cell the region growing procedure and the minimum difference to assume adouble pulse for each cell.

� s.correction: to apply the bilinear spline correction. The output contains the newclassification (terrain, terrain with double pulse, object, object with double pulse).The parameters are: the spline steps Sc in the directions East-West and North-South,the Tychonov �c regularising parameter with bilinear splines, the two thresholds tc

and Tc for residuals (�) between the observations and the interpolated values.� s.bspline.reg: bilinear/bicubic spline regularised interpolator. The parameters are: the

spline steps Si in the directions East-West and North-South, the Tychonov �iregularising parameter, the type of interpolation (bilinear or bicubic). The output canbe given in terms of spread point or in a grid.


4 Examples of application

The approach was used to filter the data provided by the ISPRS Commission III, WGIII/3 “3D Reconstruction from Airborne Laser Scanner and InSAR data” within the teston “Extracting DEMs from point clouds: a comparison of existing automatic filters” [5].Measurements are representative of two different morphological characteristics: urbanand rural landscape. The area were selected because of their diverse feature content(buildings, railroads, roads, rivers, vegetation, water surfaces, powerlines,…) in order toevaluate the filter performance within various scenarios.The point density for the urban and rural areas are originally roughly 0.67 and 0.18 pointsper square meter respectively. For two sites (one urban and one rural) new datasets werecreated by regularly reducing by 2 and 4 the number of the original measurements. Fromthe geographic point of view the flown areas were the Vaihingen/Enz test field andStuttgart city center. Data were produced by FOTONOR AS with an Optech laserscanner. Both first and last pulse return geometry and reflectance were recorded. The datasummary is presented in Table 1.

Data bounds PlanimetricResolutionLocation Site(s)

Xmin Xmax Ymin Ymax (points/m2)0.67

512050 513150 5403440 5404100 0.18Site10.04

Site2 513450 513870 5402650 5403280 0.67Site3 512023 512549 5403120 5403500 0.67

City

Site4 513120 513630 5403190 5403760 0.67Site5 493650 495250 5419770 5421000 0.18Site6 496350 497950 5420750 5421817 0.18Site7 495950 497200 5421350 5422380 0.18

0.18499450 500550 5418330 5419430 0.05

Forest

Site80.01

Table 1 – Data summary


For the first site each processing step (classification, intepolation, differences betweenDTM and DSM) at different resolutions is shown. For the other sites only the results arepresented.The first site represents a region with urban characteristics (Figure 12); features ofinterest are steep slopes (1), mixture of vegetation and buildings on hillside (2), buildingson hillside (3) and data gaps (4).

Fig. 12 – Laser scanning DSM of site 1

In the edge detection output (Figure 13) a yellow band, indicating uncertain classification,appears in the zone corresponding to the overlay of the two tiles in which data has beensubdivided to be processed.The next step, i.e. the region growing shows that in the points A-F (see Figure 14) wherevegetation is close to the buildings, the procedure tends to overestimate the buildingareas, as vegetation is included as part of the building edges. Moreover in the G pointthere is a building not correctly detected.After the correction step (Figure 15), the problems in the points A-D and F are almostcompletely removed; only in the E point an area of misclassification persists, but it is of asmall size. On the other hand in the point G the misclassification, although it has beenimproved, can not single out the whole object because of the particular shape of the roof.It is worthwhile to remark that, thanks to the double pulse measurements, a rough butreliable distinction between handmade objects and vegetation can be performed. The limitof the method is that the building edges often present this characteristic and are thereforeclassified as vegetation.The last step consists in a DTM computation. In this case a grid of resolution2 m x 2m was computed (Figure 16). The comparison between the DSM and DTM mapsshows (Figure 17) that all the handmade objects present on the bare terrain were removed.Moreover the DTM in the areas without data both because they are originally missing andbecause they are removed from the final computation as not belonging to the terrain, wassubstantially well interpolated.

4

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Fig. 13 – Edge detection output (caption: beige = terrain, orange = object,yellow = uncertain points)

Fig. 14 – Region growing output (caption: beige = terrain, light green = terrain withdouble pulse, orange = object, green = object with double pulse points)

E

B

FG

A

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Fig. 15 – Final classification output (caption: beige = terrain, light green = terrain withdouble pulse, orange = object, green = object with double pulse points)

Fig. 16 – DTM of Site 1

E

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C

D


Fig. 17 – Map of the differences between DSM and DTM

The differences between the DSM and DTM are always smaller than 30 cm.All of the features of interest were correctly determined even in case of steep slopes andfeatures places against each other. The procedure applied to the lower resolution data getsgood results again as it is shown in the Figures 18 and 19, where the final classificationsare presented.

Fig. 18 – Final classification output (caption: beige = terrain, light green = terrain withdouble pulse, orange = object, green = object with double pulse points); the planimetricresolution is 0.18 points per square meter


Fig. 19 – Final classification output (caption: beige = terrain, light green = terrain withdouble pulse, orange = object, green = object with double pulse points); the planimetricresolution is 0.04 points per square meter

The second site represents a City landscape again (Figure 20); the main features ofinterest are: large buildings (1), irregularly shaped buildings (2), a road with a bridge anda small tunnel (3) and data gaps (4).The related classification map and DTM are shown in Figures 21 and 22.The presence of large buildings has called for a choice of parameters that, as side effect,caused an overgrow of the points classified as object close to the road (3) within the zone(A), (B), (D). Case C presents an analogous problem: an area of height lower than the oneof the close road (immediately facing south); the area can be a fountain basin orsomething like that.The choice of a different set of parameters can limit the errors in the previous zones butthis at the expense of the complete buildings detection and therefore of the correctdetermination of the DTM. In fact the point misclassification causes the presence ofinexistent small hills in the final interpolation and this is not acceptable.With regards to the highlighted features of interest, the conclusion is that the methodworks correctly also with large and irregularly shaped buildings. The small bridges areaccurately detected while the tunnel and the terrain in its vicinity, presenting the samecharacteristics of the handmade objects, are classified as such and then removed in theDTM. The effect is an artificial widening of the tunnel entrances visible in areas (A) and(B) of Figure 22.



Fig. 21 –Site 2 final classification output (caption: beige = terrain, light green = terrainwith double pulse, orange = object, green = object with double pulse points); theplanimetric resolution is 0.67 points per square meter.

1 2

3

4

3

B

A

C

D



The same good results have been obtained by processing data of Site 3(Figure 23); in this case the features of interest are: densely packed buildings withvegetation between them (1), a building with an eccentric roof (2), an open space withmixture of low and high features (3) and, as usual, the data gaps (4).The only error we get in the classification is in the circled area in Figure 24.Here it is also evident the functionality of the LIDAR in extracting the bare ground ofvegetated areas: used with a suitable high repetition rate the system is able to penetratealso dense foliage and to gets enough returns to determine both the DTM and the accuratemeasurement of the foliage coverage. In the bottom part of the figure two vegetated areasare characterized with to different green hues. The light green represents points wherepart of the pulse has reached the ground; the deep green is used for the points where thepulse reaches only the tree canopies, i.e. the points where the vegetation is denser.Obviously only the first points have to be taken into account in the DTM generation but,as is shown in Figure 25, they are enough to compute a continuos and correct surface.This is undoubtedly a great advantage of the LIDAR system compared with thephotogrammetry. Another important question is the considerable reduction of theshadows in the LIDAR images: also in steep areas or in city where buildings with highrise are present the measurements can be taken at any time day or night.The processed area corresponding to Site 3 is a flat one and in this case the method worksparticularly well and the solution is highly accurate. Moreover also in this case the resultsare correct on all of the highlighted features of interest.

A

B




1

2

3

4



The last urban landscape is a LIDAR image (Figure 26) of a railway station and itssurroundings. The features of interest of this site, named Site 4, are: railway station, i.e.low density of terrain points (1), trains (2), bridges (3) and data gaps (4).The railway station is a large build which considerably masks the bare earth.The method succeeded also in this case, where few terrain points are available, eitherbecause of the efficiency of the LIDAR techniques and because of the goodness of theclassification (Figure 27).The line shown by means of the arrows in the DTM (Figure 28) isn’t a mistake becausethe terrain presents effectively the computed shape as the station is built on anembankment.In cases of urban areas our conclusion is that the proposed filtering method seems to besuitable and well working.The second dataset provided by the ISPRS test organizers represents different rurallandscapes. As previously, we present only the synthetic description of the areas, theirmain features of interest and the processing results.The first forest zone, Site 5, presents: step slopes with vegetation (1), a quarry (2),vegetation on river bank (3) and data gaps (4). A part from these features of interest,suggested by the ISPRS test organizers, near the river (5) there is an industrial plant,probably connected to the quarry activities, characterized by high elevations (compared tothe usual height of the buildings in the area) and irregular shape (Figure 29).




2

4

1

33



The areas characterized by the presence of vegetation were correctly analyzed (Figures.30 and 31). The built area on the left side of the LIDAR image was also well processed.The quarry is a feature particularly tricky as its side are greatly slanting and, being usedas road to get to the quarry bottom, they are terraced. Thus they present the traits ofmanmade objects and they are classified in such a way: the measurements will be thenremoved and in the DTM output the quarry presence will disappear. Obviously thissolution is not suitable for our purposes. On the opposite our method allows to accuratelymodel also this critical case: the quarry sides are slightly rounded but substantiallycorrect. Moreover also the industrial plant in the quarry was detected despite its particularshape.


2

1

3

4

5


The sites 6 and 7 (Figures. 32 and 35) present almost the same features and partiallyrepresent the same area. In the site 6 we have mainly to pay attention to the largebuildings present (1), to a road with embankments(2), a an underpass (3) and to data gaps(4); in site 7 the features of interest are: a bridge (1), the underpass seen also in Figure 32(2), the road with embankments (3) and the usual data gaps (4).In both the datasets a lot of gross errors are present. The preprocessing step hadaccurately detected and removed these outliers (A).Long distance power lines highlighted by arrows in the classification maps (Figures. 33and 36) appear clearly. The detection of these features further prove the accuracy of themeasurements and the performance of the developed filtering algorithm.The (B) point in Figure 33 shows a hill that was classified as object: this is due to thepresence of rich vegetation and to its sides particularly steep because of the excavationsmade to open two roads against the hill itself (on the contrary the same feature in the Site7 – Figure 36- was correctly detected). Moreover in the (C) spot there is a buildingwrongly classified.In the DSM image (Figure 32) we can observe also suspicious measurements. By the waythe least square approach allows to filter the noisy observations: in the DTM their effectscompletely disappear.The last remark refers to the road embankments visible in both the LIDAR images: theirgreat extension urges to regard them as part of the bare earth. The LIDAR observations inthis case are used to model the terrain: the rounded shapes of the embankments appear inthe DTMs (A in Figure 37).


2

3

4

1




B

CA




A

1

3

4

2

A

B


Fig. 37– DTM of Site 7

In the last zone (Site 8) the measurements were provided at various resolutions (see Table1). In the following we are showing only the maps related to the highest (0.18 points persquare meter). Anyway analysing all the results we can draw the same conclusion seen incase of Site 1.The features of interest (Figure 38) are: the high bridge (1) diagonal left to right, abreakline (2), the vegetation on the river bank (3) and, as usual, the data gaps (4).The high bridge was accurately classified and removed in the DTM: the shadow whichappears in the top left corner of the image in Figure 41 corresponds to the entrance of thetunnel that was dig through the hill.Referring to the breakline it is worth to note that the upper part of the hill close to it wasclassified as object (Figure 39) and consequently these measurements do not enter in theDTM computation. The sharp variation in the DSM heights was filtered and the resultingDTM presents a smoothed behavior.To summarize, the results obtained are almost ever good and the classification errors leftare restricted to few cases. Paying particularly attention to the features of interestsuggested by the test organizers, we can conclude that the method succeeded every time apart in case of the breakline in the last Site, which appear to be too smoothed. By the waythis is intrinsically connected to the proposed algorithm. A trivial way to face thisproblem can be to provide both the DTM and the information of the localization of theeventually present breaklines. Anyway this is not the optimal solution and we think thatin this frame further work has to be done.To conclude in Table 2 and 3 the parameters used in the various processing steps andtheir caption are reported.

B



Fig 39 –Site 8 final classification output (caption: beige = terrain, light green = terrainwith double pulse, orange = object, green = object with double pulse points); theplanimetric resolution is 0.67 points per square meter.

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6Y

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x25

0.1

21

28x

80.

14

Site

72.

0-3.

58x

82

508x

80.

010.

266

38x

82

40.

6Y

es50

x50

0.1

21

18x

80.

14

2.0-

3.5

8x8

250

8x8

0.01

0.26

63

8x8

24

0.6

Yes

25x2

50.

12

13

8x8

0.1

44.

0-5.

512

x12

250

12x1

20.

010.

266

312

x12

26

0.6

No

25x2

50.

12

11

12x1

20.

16

Forest

Site

87.

0-10

.020

x20

250

20x2

00.

010.

266

320

x20

210

0.6

No

25x2

50.

12

11

20x2

00.

110

Tabl

e 2

– P

roce

ssin

g pa

ram

eter

s val

ues

Caption of Table 2So Step of the bicubic interpolation splines in outlier rejection (m)�o Tychonov regularisation parameter minimising the surface curvature in

outlier rejectionTo Threshold of the residuals between the observed and the interpolated

values in outlier rejection (m)Sg Step of the bilinear interpolation splines in gradient estimation (m)�g Tychonov regularising parameter minimising the surface slope in

outlier rejection�g Angle threshold in edge direction computation (rad)Tg High gradient threshold in edge detectiontg Low gradient threshold in edge detectionSr Step of the bicubic interpolation splines in residual evaluation (m)�r Tychonov regularising parameter minimising the surface curvature in

residual evaluationrd Rasterizing grid resolution in region growing (m)Td Threshold for double pulse in region growing (m)

Appl Convex Hull & region growing algorithms application (yes/not)Sc Step of the bilinear interpolation splines in correction (m)�c Tychonov regularising parameter minimising the surface curvature in

the correctionTc High threshold in ground correction (m)tc Low threshold in object correction (m)N Numer of iteration of the correctionSi Step of the bilinear interpolation splines in DTM computation (m)�i Tychonov regularising parameter minimising the surface slope in the

DTM computationri Grid resolution of the DTM (m)

Table 3

ConclusionsThe paper presents a method to extract DEMs from LIDAR measurements. The algorithmwas implemented into the public domain GIS GRASS. The validation of the filter wasalready performed on an Italian dataset (town of Pavia – Northern Italy) by means oftopographic, geodetic and photogrammetric independent information [3]. In this work wepresent the improvements introduced to manage also measurements in spread points (asthe LIDAR data is) and a way to take advantage of the reflectance information.The new testing phase here presented confirms the good quality of the filter. Furtherresearch will be done to optimally solve the modelling of the breaklines.

AcknowledgementThis research was partially founded by the Italian MURST Project “Digital surveymethodologies, GIS and multimedia network for Architectual and EnvironmentalHeritage” (2000-MM08162572).

Maria A. Brovelli, Massimiliano Cannata, Ulisse M. Longoni 29

References

[1] G.Vosselman, 2000, Slope based filtering of laser altimetry data, IASPRS, Vol.XXIII, Amsterdam, The Netherlands.

[2] K.Kraus and N. Pfeifer, 2001, Advanced DTM generation from LIDAR data,IASPRS, Vol. XXXIV – 3/W4, Annapolis, MD.

[3] M. A. Brovelli and M. Cannata, 2002, Digital Terrain model reconstruction in urbanareas from airborne laser scanning data: the method and the example of the town ofPavia (Northern Italy), in print.

[4] Markus Neteler, Helena Mitasova , 2002, Open Source GIS: A GRASS GISApproach, ISBN: 1-4020-7088-8, Kluwer Academic Publishers, Boston, Dordrecht,Book Series: The Kluwer international series in Engineering and Computer Science:Volume 689

[5] http://www.geo.tudelft.nl/frs/isprs/filtertest/

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