Diplomarbeit Analysis of full-waveform airborne laser scanning data for the improvement of DTM ...

74 Pages·2008·3.12 MB·English

Diplomarbeit
Analysis of full-waveform airborne laser
scanning data for the improvement of DTM
generation
Ausgeführtam
Institut für Photogrammetrie und Fernerkundung,
Technische Universität Wien
unterderAnleitungvon
Dipl.-Ing. Dr.techn. Markus Hollaus
und
Dipl.-Ing. Dr.techn. Christian Briese
und
Univ.Prof. Dipl.-Ing. Dr.techn. Norbert Pfeifer
durch
Werner Mücke
Stadtplatz 40, Top 17
4600 Wels
Wien, September 2008 ............................
Contents
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Listofacronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
1 Introduction 1
1.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Structureofthiswork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 State of the art 5
2.1 Airbornelaserscanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 ComponentsandcharacteristicsofanALSsystem . . . . . . . . . . . . . . . . 5
2.1.2 Measurementprocess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3 Analysisofthefull-waveforminformation . . . . . . . . . . . . . . . . . . . . . 10
2.2 Segmentationofpointclouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Model-drivensegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Data-drivensegmentationbyclustering . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 DTMderivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Morphologicalfilters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 ProgressiveDensification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Surface-basedfilters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.4 Segmentation-basedfilters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Previoususageoffull-waveformdataforDTMgeneration . . . . . . . . . . . . . . . . 27
3 Study Area and Data 29
3.1 StudyArea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Dataacquisitionandprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 TestSite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4 Methods 34
i
4.1 Radiometriccalibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 Point-baseddataanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.3 Segmentation-baseddataanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.4 Usageoffull-waveforminformationforimprovedDTMgeneration . . . . . . . . . . 39
5 Results 44
5.1 Point-basedanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.1.1 Amplitudeandechowidth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.1.2 Backscattercrosssection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.2 Segmentation-basedanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.3 ImprovedDTMgenerationusingfull-waveformobservables . . . . . . . . . . . . . . . 52
5.4 ComparisonofDTMsandqualityassessment . . . . . . . . . . . . . . . . . . . . . . . . 52
6 Discussion 59
6.1 Lackofgroundtruth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6.2 Improvementpossibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
6.3 Conceptsfortheintegrationoffull-waveforminformationinpresentmethods . . . 61
7 Conclusion 62
Literature 63
WWW references 67
ii
Acknowledgements
I wish to extend my gratitude to my colleagues at the Institute of Photogrammetry and Remote
Sensing, who provided me with the best learning and working atmosphere a student can think
of. Above all I would like to thank Dr. Markus Hollaus, Dr. Christian Briese and Prof. Dr.
Norbert Pfeifer, for their continuous support and advice in many different matters concerning
this thesis. Furthermore, Dr. Bernhard Höfle, who provided components of the utilized software
andDipl.Ing. AndreasRoncat,whoreviewedpartsofthemanuscript.
Last but not least I would like to express my gratefulness to my family, especially my mother
Maria and my grandfather Johann for their continuous mental and financial support throughout
my studies. Also particular my girlfriend Marlene, we went through a lot together! Next time I
willdobetter! ;-)Thanksforsupportingandbelievinginme!
iii
Abstract
This thesis deals with the potential of full-waveform airborne laser scanning for improved digi-
tal terrain model generation. The additional observables amplitude, echo width and backscatter
crosssectionfromthisnewscanningtechniqueareseparatelyanalysedtoassesstheirdiscrimina-
tory power for classifying airborne laser scanner point clouds into terrain and off-terrain points.
Basedontheresultsoftheexploratorydataanalysis,threedifferentapproachesfortheextraction
ofterrainpointsarepresentedandtestedinapracticalapplication. Ontheonehand,hardthresh-
oldsforthefull-waveformobservablesareappliedonthesinglepointstodiscriminateintoterrain
and off-terrain. On the other hand, the full-waveform information is used to derive weights for
thesinglepoints,describingtheprobabilityforbelongingtotheclass"terrain". Properprobability
and weight functions are introduced. The third method is segmentation-based. A seeded region
growing algorithm is used to gather points with equal full-waveform attributes. A pragmatic
concept for the extraction of segments representing terrain is described. Digital terrain models
are computed from each of the three classified terrain point clouds. A comparison of these three
models with a conventional terrain model, computed without the additional observables, points
outthepotentialforimprovementbyintegratingfull-waveformmeasurements. Inareasofdense
vegetation with poor penetration rates of the laser measurement an enhancement in computing
performance,aswellasinadequacyoftheresultingdigitalterrainmodelscouldbeobserved.
Zusammenfassung
Diese Diplomarbeit beschäftigt sich mit der Nutzung von Full-Waveform Airborne Laser Scan-
ning Daten für die Erstellung von naturgetreuen digitalen Geländemodellen. Die zusätzlichen
Beobachtungen Amplitude, Echobreite und Backscatter Cross Section, die von Messungen mit
dieserneuenTechnologieabgeleitetwerdenkönnen,werdenschrittweiseuntersucht. IhreTrenn-
schärfe zur Unterscheidung von Laserscanner Punkten in Boden und Nicht-Boden Punkte wird
analysiert. BasierendaufdenErkenntnissendieserexplorativenDatenanalysewerdendreiMeth-
odenzurExtraktionvonBodenpunktenausdergesamtenPunktwolkebeschriebenundaneinem
Beispieldatensatz getestet. Zum einen werden harte Grenzwerte für die Full-Waveform Attribute
iv
an die Einzelpunkte angebracht, um so eine Klassifizierung in Boden und Nicht-Boden vorzu-
nehmen. Zum anderen werden die Einzelpunkte anhand der Full-Waveform Information mit
Gewichten versehen, welche die Wahrscheinlichkeit der Zugehörigkeit eines Punktes zur Klasse
"Boden" beschreiben. Die dafür verwendeten Wahrscheinlichkeits- und Gewichtsfunktionen wer-
denvorgestellt. DiedritteMethodenutzteinenSeededRegionGrowingSegmentierungsalgorith-
mus. Dieser wird verwendet um Punkte mit ähnlichen Full-Waveform Attributen zu einzelnen
Segmenten zusammenzufassen. Ein pragmatischer Ansatz zur Identifikation von daraus resul-
tierendenBodensegmentenwirdpräsentiert. UmdieVerbesserungimVergleichzuGeländemod-
ellen, welche ohne die Verwendung von Full-Waveform Informationen erstellt wurden zu unter-
suchen,werdenGeländemodellevonallendreimitdenzuvorgenanntenMethodenklassifizierten
Punktwolkenberechnet. DieIntegrationvonFull-WaveformInformationindieErstellungderdig-
ital Geländemodelle führte zu einer Steigerung der Recheneffizienz und zu einer Verbesserung
der Genauigkeit des resultierenden Modelles in Bezug auf den Naturstand. Vor allem dort wo
auf Grund dichter Vegetation und niedriger Durchdringungsrate des Laserstrahls nur wenige bis
stellenweisekeineBodenpunktevorhandenwaren.
v
List of acronyms
ALS Airbornelaserscanning
APOS Austrianpositioningservice
ASDF Averagedsquaredistancefunction
DN DigitalNumbers
DSM Digitalsurfacemodel
DTM Digitalterrainmodel
FOV Fieldofview
FWF Full-waveform
GPS Globalpositioningsystem
IMU Inertialmeasurementunit
I.P.F. InstituteofPhotogrammetryandRemoteSensing,ViennaUniversity
ofTechnology
LiDAR Lightdetectionandranging
NASA Nationalaeronauticsandspaceadministration
NIR Nearinfrared
POS Positionandorientationsystem
SCOP Stuttgartcontourprogram
SOCS Scanner’sowncoordinatesystem
SRG Seededregiongrowing
VRML Virtualrealitymodelinglanguage
VRS Virtualreferencestation
WWW Worldwideweb
vi
1 Introduction
Thesamplingofsurfaceswithlasertechnologyforobtaininginformationoftheirgeometricstruc-
turehasbecomeaviableandverypowerfulmethodfordataacquisitionoverthepastfewyears.
The constant development of new scanner systems increases the accuracy of the measurements
while the actual time for the measurement process is decreasing. Thus, an accordingly bigger
amountofdatacanbegatheredwhichagainleadstohigherlevelsofdetail.
Althoughairbornelaserscanning(ALS)hastocompetewithothermeasurementtechniques,such
asstereo-photogrammetry,theusersofdifferentspecialfieldsoftechnologyappreciatetheadvan-
tagesofthelasermethod. ALSisanactivemeasurementsystem,meaningitcanbeusedwithout
the utilisation of sunlight. Therefore, it is operable during the nighttime, which is not possible
with aerial photography. Furthermore, the upcoming of laser scanners produced a change of
theparadigmsinphotogrammetry. Whilewithaerialphotogrammetryalwaystheviewfromtwo
different perspectives is essential for the 3D reconstruction of surfaces, in laser scanning obser-
vations from one direction are sufficient. Hence, ALS distinguishes itself from the traditional
methods by being very effective, versatilely applicable and cost-saving at the same time [Kraus,
2007].
PointcloudsstemmingfromALSprovidethebackgroundofawideareaofdifferentapplications.
Especiallythederivationofdigitalterrainmodels(DTM)pusheditselfintothecenterofattention
in the recent past as more and more daughter products became dependent on it. The increased
occurence of flood waters once more pointed out the necessity of adequate hydraulic modeling
and flood water prevention [Mandlburger and Briese, 2007]. Stem volume estimation [Hollaus,
2006]takesacentralpositionindiscussionsaboutbiomassandrenewableenergysources,which
are matters of universal concern. 3D city modeling [Rottensteiner and Briese, 2002] is also
populartopicthatbenefitsfrombetterterrainestimationandservesbyitselfasstartingpointfor
otherfieldsoftechnology,suchascityandregionalplanning. Thesethreeexamples,tonamebut
afew,dependonhighaccuracyDTMasafundamentaldatabasis.
However,thederivationofanadequateDTMisfarfromtrivial. Alreadyduringdataacquisitiona
lotoflimitingeffectscanoccurandtounderstandtheseeffects,itisnecessarytoknowaboutthe
physicsbehindlaserscanning. Theseprinciplescanbedescribedquitesimple,yetthedifficulties
are in the details. A laser generator repetitively emits a short laser pulse that travels through
different media until it finally hits an object where the pulse is partially or completely reflected
1
1 Introduction
back towards the scanner and is registered by a detection unit. The round trip time of the pulse
from the scanner to the object and back is measured. With the knowledge about the velocity of
thetravellingpulsethedistanceitcoveredcanthenbecomputed[Wagner,2005].
A characteristic of the laser measurement is that because of the widening of the laser beam one
shot might hit more than one object along its way, resulting in more than one returning signal.
Current detection units are able to differentiate between such multiple echoes if they feature a
certain minimum difference in their range (section 2.1). The distance to consecutive targets can
thereforebedetermined. Suchsystemsarecommonlyusedinairbornelaserscanning. Hereinlies
thepossibilitythatapartoftheemittedlaserbeampenetratessmallgapsofasurface(e.g. atree
canopy) and is reflected by lower branches, bushes, near-ground vegetation or even the ground
itself. Usingalgorithmstoseparatesuchgroundpointsfromothersmakesitpossibletocalculate
aterrainmodeleveninovergrownareas[KrausandPfeifer,1998]. Hence,itcomesdowntothe
task of identifying ground hits and classifying the point cloud into terrain and off-terrain points.
In areas densely covered by vegetation this is mostly a very difficult matter. Often returns origi-
nating in lower vegetation are by mistake classified as ground hits and have a highly disturbing
effectontheestimationofaDTM,mostoftenapositiveshiftoftheresultinginterpolatedsurface
with respect to the point cloud. The DTM might run through the lower vegetation instead of
belowitandthereforebetoohigh[Gorteetal.,2005].
One of the latest developments in this area are systems that cannot only differentiate a first,
several intermediate and a last echo, also referred to as discrete ALS systems, but are able to
digitize and record an entire backscattered waveform, called full-waveform ALS systems. From
theserecordedfull-waveformsmorethanonlytherangefromscannertotargetcanbecomputed.
Other physical quantities can be derived from the recorded waveform, such as the width of the
backscattered echo, its amplitude (intensity of the recorded signal) or, subsequently, the cross
sectionofthebackscatteringtarget(section2.1.3)[Wagner,2005]. Discreteechosystemsaswell
offer intensity measurement. But due to the fact that most system designers like to keep quiet
abouttheirdefinitionofintensity,itsusagewithoutpriorcalibrationofthemeasurements[Höfle
and Pfeifer, 2007] is not advisable. Therefore the measured intensity values from discrete scan-
nersarenotusedveryoften.
Using full-waveform data, the user is given the opportunity to apply self-designed or adapted al-
gorithms in post processing, thus adapting the determination of echoes for his special needs and
making it more robust for his type of applications. At the same time, the derivation of surface
models from data gathered with full-waveform scanners is not only based on geometric princi-
ples. The question of how the additional observables from full-waveform laserscanning can be
usedtoimproveterrainmodelingisthemaintaskofthiswork.
2
1 Introduction
1.1 Objectives
Full-waveform ALS is a rather new technology. The amount of information stored in the full-
waveform carries a high potential for a derivation of more adequate products than it was ever
possible with discrete echo digitization. The outlooks are promising. However, the full profit for
thegenerationofDTMsisyettodiscover.
Theassumptionisthattheadditionalobservablesfromfull-waveformairbornelaserscanningcan
beusedtoseparateterrainfromoff-terrainpoints. Theseobservablesare
1. intensity(whichreferstotheamplitudeoftherespectiveecho)and
2. widthofbackscatteredecho.
The amplitude gives information about the reflectivity of the scanned surface, whereas the echo
width describes the range variations within the hit surfaces that contributed to a certain echo.
The more variation takes place, the wider it becomes, e.g. on rough surfaces with respect to the
laser footprint. Subsequently, the backscatter cross section (section 2.1.3), whose computation
referstothetaskofcalibration,canbecomputed[Wagneretal.,2006].
As mentioned in the introduction, the detection of near-ground vegetation is a problem so far.
Echoes that stem from small trees, bushes or shrubs can lead to undesired results in the final
DTM. These echoes shall be identified and filtered to receive a more adequate model of the real
terrainsurface.
Inordertoachievethisobjectivethefollowingaimsofthisdiplomathesiscanbeformulated:
• Onesubjectofanalysisistheinteractionofthelaserbeamwithdifferentlandcoverclasses.
The assumption is, that some of them are very well distinguished in the above mentioned
parameters while others are not. The magnitude of the appearing differences is a critical
aspectandneedstobeexplored.
• The knowledge about the discrimination of the point features is then exploited for the
classification of the point cloud. This is done in two ways. First, hard thresholds for the
full-waveform observables will be used to classify the individual points into terrain and
off-terrain. Secondly, a weight function based on the correlation of amplitudes and echo
widthswillbedevelopedandusedtoassignweightstotheindividualpoints. Theseweights
describewhether apointis morelikelyto beaterrain pointor not. Theyarethen usedfor
apre-classificationofthepointcloudinthefilteringprocess.
• Another approach using a segmentation algorithm based on seeded region growing will
be tested. The aim is to find out whether this method is able to gather points with equal
attributesrepresentingterrainandoff-terrainpoints.
• To examine if the obtained classification of the ALS point cloud is useful for the purpose
of generating an improved DTM, the results of the different approaches are compared.
3