A Hierarchical Quality-Dependent Approach Toward Establishing A Seamless Nationwide Topographic Database

Transcription

1 A Hierarchical Quality-Dependent Approach Toward Establishing A Seamless Nationwide Topographic Database Sagi Dalyot, Arieal Gershkovich, Yerach Doytsher Mapping and Geo-Inormation Engineering Technion, Israel SDH /05/010

2 Presentation Contents Introduction Problem deinition Proposed algorithm Results Conclusions and summary

3 Introduction DTM (Digital Terrain Model) - topographic computerized model representing the terrain relie (alterations in the topography). Spatial Map Slope Map Shaded Relie Map These models serve as a continuous, constant, reliable and homogenous inrastructure. 3

4 Introduction DTMs utilized in varied GI sciences domains and applications: Earth sciences: modeling and analysis o geo-morphologic phenomena; hydrologic and hazards applications; Civil engineering: urban planning; environmental management; 3D landscape design, modeling and visualization; Remote sensing and mapping: geo-rectiication; orthophoto creation; retrieving o thematic and semantic inormation. Military applications: visibility analysis; terrain analysis; 3D visualization; simulations; 4

5 Introduction Seamless DTMs GI sciences inrastructure 1 : Data acquisition (photogrammetry, ield surveying, Radargrammetry) Applications (environmental and resource management, urban planning) DTM Computation and modeling (computer graphics, computational geometry, image processing) Data manipulation and management (data structuring, computer graphics) 1 Li et al., 005 5

6 Introduction DTMs rom dierent sources and o various qualities: Traditional data acquisition Photogrammetry: utilizes stereo pairs o aerial or space imagery that cover approximately the same area Mostly produce a grid DTM (raster like) DTM presents constant resolution Height accuracy is usually constant within a speciic campaign Probably the most common technique nowadays 6

7 Introduction DTMs rom dierent sources and o various qualities: Traditional data acquisition Field Surveying: utilizes TS and GPS receivers or direct ield measurements Accuracy o a position acquired extremely high Deliver much ewer data samples Used to measure and map small areas Technique is rarely used or DTM production Can deliver missing data other techniques can not measure Typiied by irregular and sparse position o sample data 7

8 Introduction DTMs rom dierent sources and o various qualities: Traditional data acquisition Cartographic digitization and scanning: utilizes raster vectorization techniques o existing topographic/contour maps Semi-manual digitization and quality assurance are sometimes required Available in o-the-shel GIS packages Height accuracy is usually constant Mostly produces irregular data samples (contour) Was commonly used or DTM production nowadays mainly in developing regions via utilizing mediumscale maps 8

9 Introduction DTMs rom dierent sources and o various qualities: Modern data acquisition Radar based systems: utilizes radargrammetry techniques and ISAR imaging Radar imagery are very sensitive to terrain variations Large accuracy deviations sometimes exist Height accuracy within a DTM is usually constant Eicient or acquiring data o large regions Not aected by the lack o sun light and extreme meteorological conditions DTMs produced are mostly regular 9

10 Introduction DTMs rom dierent sources and o various qualities: Modern data acquisition ALS (LiDAR) Systems: utilizes laser ranging techniques or producing 3D point cloud Randomly Not aected distributed by the lack data o sun (irregular) light Data DTM sample production is already requires geo-reerenced additional Accuracy algorithms o - iltering, a position interpolation acquired is -high Eicient usually perormed acquiring on the data raw o mediumsized (raw/sample regionsdata include o-terrain data objects - vegetation and buildings) Produces the densest DTMs 10

11 Introduction Wide coverage DTMs rom dierent sources and o various qualities: vertical accuracy assessment Technique/Technology Vertical Accuracy (m) Aerial photogrammetry Satellite photogrammetry 1 10 Field surveying Digitization 1/3 o contouring interval Aerial radargrammetry 5 Satellite SAR inteerometery 5 0 LiDAR

12 Problem Deinition Need - integration o DTMs is essential or obtaining computerized topographic inrastructure. Status - multi-source DTM: Produced via various technologies and techniques; Inluenced/aected by rapid data-updates. Accuracy polygon map Result integrated DTM might present: Changing qualities and precisions o coverage area; Varied data characterizations and structures; Dierent magnitude o internal data-relations and correlations. 1

13 Problem Deinition Dierent DTMs can vary and present dierent datacharacterizations: structure, data-density, level-o-detail, accuracy, resolution, datum, Same coverage area dierent models Varied-scale geometric discrepancies and inconsistencies dierent acquisition epochs and diverse data-sources; Global-systematic incongruity and local-random inaccuracies; Dierent magnitude o internal data-relations and correlations has to be addressed. 13

14 Problem Deinition Simultaneous use o several multi-source DTMs introduce, such as integration or change detection, intensiies the beore mentioned problem. Thus, It is essential to a-priori extract and quantiy a reliable spatial modeling o DTMs correlations 14

15 h Problem Deinition Reliable integration (usion) o multi-source DTMs is required to: Apply morphologic and accuracy adjustments thus spatial modeling is assured; Provide continuous height and topological representation; Address locally the varied irregularities and inaccuracies that exist within the DTM and between DTMs; Ensure continues and semantic modeling. 15

16 Slide 15 h hp 6/04/010 להוסיף התייחסות לדיוקים פנימיים משתנים hp, 6/04/010

17 Proposed Algorithm Implementing a hierarchical modeling algorithm: Phase 0 producing smooth and continuous accuracy polygons maps. Phase 1 Global registration (mutual rame work): Identiication and extraction o topographic unique interest points; Spatial mutual quality-dependent skeletal registration. Phase Spatial modeling and matching: Quality-dependent local Iterative Closest Point (ICP) matching; Establishment o mutual modeling matrix. Phase 3 integration: Designated data-handling interpolation concepts; Quality-dependent height calculation o integrated DTM - continuous, seamless and homogenous. 16

18 Proposed Algorithm Complete data available Phase local spatial modeling Phase 1 global registration Phase 1 global registration Phase local modeling Phase 0 smooth polygon maps 17

19 Proposed Algorithm Schematics o hierarchical mechanism: IP extraction Global registration Local (rame) matching Mutual modeling matrix storing spatial correlations parameters 18

20 Proposed Algorithm Phase 0 Producing smooth and continuous accuracy polygon map: Polygon A D/ D/ Polygon B Automatic process that generates this inormation: Topologic relations extraction o geometric objects that comprise the accuracy polygon map: polygons polylines vertices (nodes); Vertices topology indexing: map borders; two polylines; three polylines; etc.; Buer width (D) required or given joint polylines (derived by accuracy dierence). 19

21 Proposed Algorithm Phase 0 Producing smooth and continuous accuracy polygon map: Creating new trapeze and triangular shaped accuracy polygons (derived rom existing polygons topology); Accuracy values in new polygons comprise o original accuracy values. 0

22 Proposed Algorithm Phase 0 Producing smooth and continuous accuracy polygon map: δ = 1 ( Yp Y1 )* D Dx D ( Xp X )* Dy 1 L D L Dy = Y Dx = D X Y 1 X 1 L = Dy + Dyy = Y Dxx = D α cos( ) Dx A = Dy * Dyy + Dx * Dxx B = Dyy * Dx Dy * Dxx α = a tan ( A, B) DD = β = a tan ( Dx, Dy) + 90 YR YL XR XL = Y = = Y = 3 X X X Y 3 + DD *sin( β ) + DD *cos( β ) DD *sin( β ) X o DD *cos( β ) α + 1

23 Proposed Algorithm Phase 0 Producing smooth and continuous accuracy polygon map: Trilinear coordinates P S t t t Acc = = _ x y S P = x y ( x 30 y 40 x 40 y 30 ) ( y 30 y 40 ) ( x 40 x 30 ) ( x 40 y 0 x 0 y 40 ) ( y 40 y 0 ) ( x 0 x 40 ) ( x y x y ) ( y y ) ( x x ) 0 x y Acc _ 0 t Acc _ 30 t Acc _ 40 t 40 0 x y 1 P P

24 Proposed Algorithm Phase 1 Identiication o topographic unique interest points: Identiied interest point Examined grid-point 1. Four computed polynomial unctions Polynomial unction. Area calculation Proile view L 3

25 Proposed Algorithm Phase 1 Identiication o topographic unique interest points: x x

26 Proposed Algorithm Phase 1 Spatial mutual quality-dependent skeletal registration: orward Hausdor distance - Interest points a h( A, B) = maxmin a b a A b B Interest points b Max Min [dx 1, dy 1, dz 1,,dx n, dy n, dz n ] Mean (dx, dy, dz) Std (dx, dy, dz) 5

27 Proposed Algorithm Phase 1 Spatial mutual quality-dependent skeletal registration: orward Hausdor distance - h( A, B) = maxmin a b a A b B 6

28 7 Quality-dependent local spatial ICP matching: 3D aine matching (transormation) model is implemented on mutual zonal overlapping rames 6 registration parameters or each rame. + = dz dy dx Y Y X X R Y Y X X M M M g M g g M g g M g ),, ( ω κ ϕ 3 rotations 3 translations Proposed Algorithm Phase

29 8 Quality-dependent local spatial ICP matching: Proposed Algorithm Phase (x,y,z) i g t (x,y,z) i g(x,y,z) i X Y Implementing 3 geometric constraints to assure coregistration o two corresponding points - one rom each DTM y x D y D x D z = ( ) ( ) ( ) ( ) = = D y z y x D x D y y D z D x z y x D y D x x D z g t g t g t g t g t g t DTM source I (g) DTM source II ()

30 Proposed Algorithm Phase Quality-dependent local spatial ICP matching: Due to varied accuracies each co-registered point { & g } has dierent accuracy value. DTM source II DTM source I g Weight p g or each co-registered points is introduced into adjustment process: Acc _ 0 P g = ( Acc _ 3) + ( Acc _ 7) _ x= _ x = T 1 T ( A P A) ( A P l) { dx, dy, dz, ϕ, ω, κ} 9

31 Proposed Algorithm Phase Establishment o mutual modeling matrix: DTM source I Mutual modeling (registration) matrix (cell = rame) Phase local spatial modeling {dx i, dy i, dz i, ϕ i, ω i, κ i } Phase 1 global registration DTM source II 30

32 Proposed Algorithm Phase 3 Quality-dependent height calculation o integrated DTM: {h sourceii, m sourceii } DTM source I h {h sourcei, m sourcei} DTM source II {6 reg val, m sourcei } {6 reg val, m sourceii } Mutual modeling matrix Integrated DTM 1. Final (integrated) DTM planar coordinates (X, Y). Calculation o 6 registration values via designated interpolation 3. Weighted transormation rom integrated DTM to sources 4. Calculation o two heights: h 1-sourceIl and h -sourceii 5. Calculation o weighted height in integrated DTM 31

33 Results Accuracy polygon map I I Accuracy polygon polygon map II II A A B B Relative weight 3

34 Results Accuracy polygon map I Accuracy polygon map II A B C A B C D Smoothed accuracy polygon map I Smoothed accuracy polygon map II 33

35 Results Smoothed accuracy polygon map I Smoothed accuracy polygon map II Relative weight 34

36 Results Accuracy polygon map I 30 A Accuracy polygon map II A B SDH B /05/010 35

37 Results Accuracy polygon map I A B A B C SDH 010 Accuracy polygon map II C D /05/010 36

38 Results Proposed hierarchical algorithm Common height averaging mechanism SDH /05/010 37

39 Conclusions and Summary Proposed algorithm principles and advantages: Process that ensures preservation o inner morphology: geometric characterizations and topologic correlations; Implementation o several separate working levels that enables to monitor and model global as well as local data incongruities; Autonomous algorithm; Spatial data-handling as opposed to common mechanisms that attend only the height dimension; Not dependent o data s resolution, density and datacharacterization o sources; 38

40 Conclusions and Summary Outcome: Accurate, reliable and continuous terrain representations reeo-gaps, seamless and with no distortions; Morphology representations and characterizations are preserved in the integrated model; Integrated DTM is more precise than any o the sources signiicant and more accurate data available is preserved ( chosen during the process); Hybrid model that is dependent on the data s accuracy. 39

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