KN OWLED GE-BAS ED INTERPRET/ffION OF AERIAL IMAGES FOR UPDATING OFROADMAPS

112 Pages·2006·9.53 MB·English

NETHERLANDS GEODETIC COMMISSION
PT.'BLICATIONSO N GEODESY NEW SERTES
ISSN0 1651 706 NUMBER/H
KN OWLEDG E-BASE D INTERPRET/ffION
OF AERIAL IMAGES FORU PDATING
OFROADMAPS
MARLIESD E GUNST
1996
NEDERLANDSEC OMMISSIEV OOR GEODESIE,T HIJSSEWEG1 I,2629J A DELFT, THE NETHERLANDS
TEL. (3l )-(0)15 -278281F9,A X (31)-(0)51- 278247s
Thb publicationi s accomplishedw ith the supporto f the Faculty of GeodeticE ngineering,T U Delft
hinted by Meinema BV., Delft, The Netherlands
CONTENTS
t. INTRODUCTION , .
l.l Why photogrammetryn eedsc omputerv ision I
t.2 Contribution of computerv ision to aerial image interpretation aJ
1.2.1 Complicatinfga ctorsfo r interpretatioonf aeriailm ages. ....... J
1.2.2 Consequencefso r interpretationo f aerial imagesb y computer
vision . 4
1.3 Thesiss copea nd contribution 5
1.4 Thesiso rganization
PART I THEORY AND CONCEPTS
2. CoNCEPTS IN KNOWLEDGE-BASED IMAGE INTERPRETATION . ll
2.1 Image interpretationb y computerv ision ll
2.1.1 Traditionals trategiefso r imagei nterpretation ll
2.1.2 Knowledge-baseidm agei nterpretation t2
2.2 Levelso f processinga nd representation l3
2.3 Control strategies l4
2.3.1 Hierarchicacl ontrol 14
2.3.2 Heterarchicacl ontrol t6
2.4 Types of knowledge t7
2.4.1 Declarativeknowledge t7
2.4.2 Proceduralk nowledge l8
2.5 Techniquesf or knowledger epresentation. . l9
2.5.1 Productionr ules t9
2.5.2 Semanticn etworks 2l
2.5.3 Framesa nd schemas ZJ
2.5.4 Discussion 25
3. REVIEW OF PREVIOUS WORK ON ROAD EXTRACTION 27
3.1 Overview of characteristics. . 27
3.1.1 Road appearance . 27
3.1.2 Roadcontext.... 29
3.1.3 Knowledge-basedr oad extraction 29
3.2 Control strategies for road extraction 29
3.2.1 Bottom-up control in road extraction 3l
3.2.2 Top-down road extractionb y utilization of maps 32
3.2.3 Top-down road extractionb y human interaction 33
3.2.4 Hybrid control in road extraction 34
3.2.5 Heterarchicacl ontrol in road extraction 35
3.3 Road characteristics 38
3.3.1 Road-specifigc eometricp roperties 38
3.3.2 Road-specificr adiometricp roperties 39
3.3.3 Contextuali nformation 4l
3.3.4 Functionalf eatures 4l
3.4 Resultsanddiscussion... 42
4. Coxcnprs FoR KNowLEDGE-BASERDo AD ExrRAcrroN 45
4.1 Requirementsfo r interpretationo f aerial images 45
4.1.1 Controls trategy 45
4.1.2 Low level imagep rocessing. 46
4.1.3 High level reasoning 47
4.1.4 Typeso f knowledge 48
4.1. 5 Knowledger epresentationfo rmalism 48
4.2 Requirementsfo r updatinga nd utilization of maps . 49
4.2.1 Data structuret o storem aps . 49
4.2.2 Mapguidance.... 49
4.3 Object-orientedm odel for road networks 50
4.3.1 Small scaleo bjects 5l
4.3.2 Medium scaleo bjects 52
4.3.3 Larges caleo bjects 53
4.3.4 Relationsb etweens pecializedo bjectt ypes
)J
4.4 Conceptsf or map-guidedi nterpretation 54
4.4.1 Changed etection 54
4.4.2 Componentdetection 54
4.4.3 Contextualreasoning 55
4.4.4 Map-guidedi nterpretations trategy 55
4.5 Realisationo f the interpretations trategy 56
4.5.1 Hypothesisg eneration 57
4.5.2 Segmentatio.n. . 60
4.5.3 Objectrecognition 60
4.5.4 Inconsistencdye tr:ction 60
4.6 Knowledger epresentation. . . . 6l
4.6.1 Basicr epresentatiopnr imitives 61
4.6.2 Objectd efinition 6l
4.6.3 Objectr elation 62
4.6.4 Representationo f segmentedo bjects 64
4.7 Completei nterpretationp rocess 65
tv
CONIENIS
4.8 Examples 66
4.8.1 Exampleo f altemativeh ypothesegse neration 67
4.8.2 Exampleo f an alternatives earcha rea 68
4.8.3 Exampleo f componentd etectiona nd contextualr easoning 72
4.8.4 Exampleo f changed etection 72
4.9 Discussion IJ
PART II CASE STUDY: EXTRACTION OF NEW ROADS LINKED TO
EXISTING MOTORWAYS 75
J. CoNTENTS OF THE KNOWLEDGE BASE AND THE DESIGNED INTERPRETATION
77
STRATEGY
5.1 Objectives 77
5.2 Input data 78
5.2.1 Images et 78
5.2.2 Roaddatabase.... 8l
5.3 Choiceo f objects 82
5.3.1 Generalizedro ad networkm odel . 83
5.3.2 Specializedro ad networkm odel 83
5.4 Defined conditionsf or recognitiono f objects . . . . 84
5.4.1 Geometricc onditions 84
5.4.2 Radiometricc onditions 87
5.4.3 Spatialc onditions 88
5.5 Interpretations trategy 89
5.5.1 Relationsb etweeng eneralizedo bjects 89
5.5.2 Relationsb etweens pecializedo bjects 92
5.6 Image processingt echniquesa nd their parameters. . 94
5.6.1 Changed etection 94
5.6.2 Contextualr easoning 95
5.7 Parameters ettings 99
5.7.1 Settingso f all parametersu sedi n the cases tudy . 99
5.7.2 Exampleso f determinationo f parameters ettings 102
5.8 Discussion lo4
6. RESULTSA NDE VALUATION. IO5
6.1 Organisationo f experimentsa nd analysis . . . 105
6.1.1 Experimentasl et-up. 105
6.1.2 Presentatioonf results 106
6.2 Visualisationo f results 106
6.2.1 Resultso n learnings et . . . 106
6.2.2 Resultso n tests et . . 1ll
6.3 Recognitiono f specializedo bject types tl2
6.3.1 Classificationo f the type of carriageway ll3
6.3.2 Discriminationo f Y-junctionsf rom fly-overs tt4
6.3.3 Classificationo f the type of link road 115
6.4 Detectionb y a generalizedv ersusa specializedr oad network model . . . ll5
6.4.1 Detectiono f changedp arts of carriageways ll5
6.4.2 Detectiono f the first part of a link road ll8
6.4.3 Trackingl ink roads 120
6.5 Detectiona nd classificationo n low versush igh resolution. . . 121
6.5.1 Detectiono f changedp artso f caniageways 122
6.5.2 Detectiono f the first part of a link road r22
6.5.3 Trackingl ink roads 123
6.5.4 Classificationo f the type of carriageway t23
6.5.5 Discriminationo f Y-junctionsf rom fly-overs 123
6.5.6 Classificationo f the type of link road 124
6.6 Discussiona nd evaluation. . . . 124
6.6.1 Generalperformance t2s
6.6.2 Generalizedve rsuss pecializedro ad model t25
6.6.3 Low versush igh resolution. . . t26
6.6.4 Detectionc omparedto classification 126
6.6.5 Remarksa bout practicalp roblems 127
PART III CONCLUSIONS AND RECOMMENDATIONS 129
7. CONCLUSIONS AND RECoMMENDATIONS I3I
7.1 Generalc onclusions l3l
7.1.1 Main contribution.s l3l
7.1.2 Main shortcomings 132
7.2 Evaluation of the designedi nterpretations trategy for road extraction
and contentso f the knowledgeb ase 133
7.2.1 Hypothesisg eneration 133
7.2.2 Goal-directeds egmentatio.n . . 134
7.2.3 Objectrecognition 135
7.2.4 Inconsistencdye tection r36
7.3 Potentialf or putting the conceptsi nto practice 137
7.3.1 Potentialf or GlS-guidedm ediums caler oad extraction 137
7.3.2 Potentialf or others cales r37
7.3.3 Potentialf or semi-automaticp rocessing r39
7.3.4 Potentialf or other topographico bjects 140
7.3.5 Future prospectso n automatizeda erial image interpretation. . . t40
7.4 Recommendations t4l
CONIENIS
REFERENcEs. .. 143
APPENDIX A PHoTOGRAMMETRIC PROCESSINGO F DIGITAL IMAGERY 153
A.l Manual tasksi n analyticalp hotogrammetricp rocesses. . . 153
4.2 State-of-thea rt of automaticp rocessing. . . 155
A.3 Bottlenecksf or automaticp rocessing 157
APPENDIX B GT,OSSINY OF ROAD TERMS 159
AppnxurCx ADDITIoNAL SUBJECTS ON DETERMINATION OF PARAMETER SET-
TINGS FOR IMAGE PROCESSINGT ECHNIQUES 163
c.l Influence of a change in road width on the cross-correlation 163
c.2 Sensitivity of parameters for detection of junctions . . . 167
APPENDIX D NorATroNS AND ABBREvIATIoNS 173
Suvrulny 175
SAMENVATTING 179
ACKNOWLEDGf,MENTS 183
CURRICULUM VITAE 184
vt,
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CHAPTER 1.
INTRODUCTION
In the photogrammetricp ractice there is a need to automatize acquisition of topographic
information from aerial photographs. However, especially tasks involving interpretation
capabilitieso f human operatorsa re hard to automate.D igital photogrammetryc an benefit from
experiencesw ith knowledge-basedco nceptsi n computerv ision. Within this field our goal is to
investigatet he potential of knowledge-basedim age processingt echniquesf or interpretationo f
aerial images for the purposeo f updating road maps.C onceptsa re concretizeda nd testedo n a
case handling the extraction of new roads linked to existing motorways in large-scalea erial
photographs.I n the first chapter the demandf or automationo f photogrammetricp rocessingi s
discusseda s well as complicationsw hen using traditional image processingt echniquesf or this
task. As a result the needf or knowledge-basecdo nceptsb ecomesc lear'
1.1 WHY PHOTOGRAMMETRY NEEDS COMPUTER VISION
Developmentsin photogrammetryh ave always been closely relatedw ith developmentsin other
fields of sciencea nd technology,a s was pointed out by several authors, [e.g. Schenk 1988,
Torlegird 19881.P rogressi n cartographya nd computer sciencea ccelerateda nd increasedt he
interest in the current transition from analytical to digital photogrammetryR. elevantc hangesi n
cartographyw ill be discussedf irst, followed by their consequencefso r photogrammetryw, hich
will lead to reasonsw hy new techniquesin computers ciencen eedt o be investigated.
In modern map production a shift took place from maps stored in analoguef orm on paper or
film to digital databasesc ontaining topographici nformation. Digital topographicd atabasesa re
an essentialp art of GeographicI nformation Systems( GIS). GIS supportst he integration of
topographic information with other types of information, like administrative and thematic.
Besidesi t provides a number of sophisticateds oftwaret ools, for instancef or analysisa nd pre-
sentation of spatial data. This makes GIS into a powerful instrument for the purpose of
planning, monitoring tasks and managementM. oreover it makes new technologicald evelop-
ments possible like in-car navigation, where a car is equippedw ith a small computer which
plans and displayst he route to a certaind estinationb y using digital maps and other information
like locations of traffic-jams. Becauseo f these advantageso f GIS comparedt o paper maps,
there is a wider use of topographici nformationa nd thereforea larger demand.
However, to be effective, GIS is dependenot n accuratea nd up-to-datai nput data.T his does not
only apply for information usersa dd themselvesb, ut also for topographici nformation. The next
examplesw ill make this clear:
- If a traffic accident happenso n a newly constructedr oad, the service that registerst hese
accidentsw ill needu p-to-dater oad databases.
- Drivers who use in-car navigation will demand new roads to be included in their route-
planninga s soona s possible.
CHAPTER 1
- Public utilities will needn ewly constructedh ousesto be presenti n their topographicd ata-
basew hent hey delivera nd map their services.
These are reasons why users strongly ask a more frequent updating cycle of topographic
information.
Photogrammetrye stablishedit self during this century as an efficient surveyinga nd mapping
method. High-quality and up-to-date topographic information can be extracted from aerial
photographs.H owever, photogrammetricp rocessingf orms the bottleneck in speedingu p the
topographici nformation supply. Especiallym easuringt hree dimensionalc oordinatesb y outlining
manuallye very object in the photographis very labour-intensivaen d time-consumingT. here-
fore, further automationo f photogrammetricp rocessingis highly desirable.
One approachf or automationi s to considere very task in the photogrammetricp rocessingc hain
and try to automatee ach of them [Heipke, 1993]. An extendedo verview of the state-of-the-art
of automationo f photogrammetritca sksi s given in appendixA . Summarizingg, eometrict asks,
such as aerial triangulation and orientation,c an at present nearly be solved automaticallyb y
transferring experiencef rom analytical to digital photogrammetry.H owever, tasks involving
interpretation capabilities of human operators are very difficult to solve by computers.I n
particulari nterpretationta sksa re very labour-intensivaen d time-consumintga sksi n the mapping
process.I n this thesis the notion "interpretation"r efers to determinationo f the location and
outliningo f objectsi n the imagea s well as recognitiona nd classificationo f topographico bjects.
Even unexperiencepde oplec an immediatelyr ecognizef or examplem ost roadsa nd housesi n
aeriali mages,b ut nobodyc an tell exactlyh ow they did it. Peopleu nconsciouslrye ly on knowl-
edge about propertieso f objects and their appearancein the aerial image. In order to perform
this task by computert his knowledges houldb e formulatede xactly,t ogetherw ith techniquesto
measuret hem in the image, since for a computera digital image is only an array with numbers,
representinggr ey values.
A current developmenti s to skip the mapping processa nd to integrateu p-to-datei mage data
directly in GIS [Ehlerse t al. 1989,F ritsch l99l]. In this way the user immediatelyp ossesses
new imaged ata.O ld GlS-informationc an be comparedw ith the new situationa nd if necessary
updatedb y the userh imself.A s a resulto f the growinga warenestsh at up-to-dateim ageryo ffers
good prospectsf or GIS to be more effective, many commercialG IS productsh ave beena dapted
to offer imaged isplayc apabilitiesa nd somet ools for imagea nalysis[ e.g.L aan 1991].T hanks
to thesep ossibilitiesf or integrationo f GIS and image data anothera pproachf or automationo f
photogrammetricp rocessingb ecamef easible:u tilization of informationf rom GIS to improve the
automatice xtractiono f new information from the image data for GlS-updating.V arious research
shows that ancillary geographic information can improve satellite image classification for
thematicm apping,l ike land cover classification[ e.g. Wilkinson/Burril 1991, Janssen1 994].
Also topographicm appingc an benefit from GIS information[ Cleynenbreugeelt al., l99l]. It
seemsto be a promisinga pproachto solvet he very hardt asko f interpretatiobny computers.
Interpretationo f digital imagesi n generali s the subjecto f computerv ision.A definitiono f this
disciplineg iven is by Haralicka nd Shapirol l992al: