Processing of airborne laser scanning data

Transcription

1 GIS-E1020 From measurements to maps Lecture 8 Processing of airborne laser scanning data Petri Rönnholm Aalto University 1 Learning objectives To realize error sources of Airborne laser scanning To understand how errors of airborne laser scanning can be corrected To know typical post-processing of laser data 2 1

2 Call of bids, airborne laser scanning For what kind of products data will be needed, and what kind of accuracy is demanded When data/final product has to be delivered? Detailed specifications of scanning campaign Demand for a flight plan (including locations of reference surfaces) Quality assurance plan Timing of laser scanning Demand of complete data (no gaps) Demand of across strips Minimum point density Maximum opening angle of sweep (e.g. max 40 degrees) 3 Call of bids, airborne laser scanning Demands for data processing, e.g. Laser scanning data must be examined immediately after data acquisition in order to detect errors or gaps Demand for a strip adjustment Quality verification by utilizing ground control information (also, who should make field measurements) Demand for filtering erroneous points away or classify into the (error) class 4 2

3 Call of bids, airborne laser scanning Requirements of the end-product, e.g. Laser scanning data processing reports, including applied corrections, applied correction parameters and location accuracy Demand for calibrated and strip adjusted 3D laser point clouds In which coordinate and height systems data should be? What level of copyright is needed? Full? Is there a need to give data to third partners? How data is to be delivered? (Media and format) Is raw laser point cloud data with calibration parameter needed, or is it enough to get only the final product? Is it allowed to use subcontractors? (Usually yes, but it s possible to demand an approval from the customer) 5 Error sources of Airborne laser scanning Δκ Errors of range finder(δr) Errors from a mirror of a laser scanner (Δβ) Position error (ΔX 0, ΔY 0, ΔZ 0 ) Attitude errors of laser scanner (Δω, Δϕ, Δκ) ΔX 0, ΔY 0, ΔZ 0 Δω Δβ ΔR Η Δ Z Δ across track Δ along track Δϕ along track across track 6 3

4 Errors: Range Finder Only a small effect on X and Y coordinates (airborne case) Note! that affects only to across track directions Influence to planimetric coordinates is smaller than to altitudes 7 Accuracy of range measurements Uncertainty of true speed of light at the atmosphere ΔR Δc = R c For example, if the speed of light has 0.01 % error and the distance to the ground is 1 km, we get 10 cm error to range value Uncertainty in time measurements c ΔR = Δt 2 Accuracy of time measurement. Is typically ns. 0.1 ns corresponds to ca. 1.5 cm 8 4

5 Errors in range finding Selected method how to define where a laser pulse should be measured (see the previous lecture) Echo intensity time/distance 9 Effect of errors in range finding Laser strip bends up or down from the sides depending whether ranging error is positive or negative 10 5

6 Errors in range finding If the footprint of the laser ray is large, a range measurement is not necessarily coming from the center of the footprint Therefore, the height might be correct, but not when associated with the XY-value calculated using the center point of a laser footprint Causes errors that are depending on the shape of the terrain Highest point Center of the light ray 11 Multiple reflections The traveling time of the laser ray increases causing error in range measurement 12 6

7 The mirror in laser scanner causes errors The outcoming angle of a single laser ray is usually depended on the mirror of the laser scanner Errors in the mirror can cause the device to measure wrong outcoming angles If the outcoming angle is wrong (and range measurement is right), the scale will be wrong 13 Errors in position Errors of DGPS GPS receiver makes some error Non-optimal satellite geometry causes errors Errors of reference station or VRS service Unmodelled shift between GPS and laser scanner (its mirror), a lever-arm error For practical reasons, GPS and the mirror of the laser scanner can t locate at the same position DGPS Laser scanner DGPS Laser scanner mirror 14 7

8 Errors in attitude Errors of inertial device (IMU=Inertial Measurement Unit, INS=Inertial Navigation System) Errors of a receiver frequency During longer period, the errors of INS accumulate causing quickly increasing errors Drift errors between the coordinate systems of IMU and laser scanner (bore-sight error) IMU Laser scanner mirror 15 Errors of integrated use of DGPS and IMU Unmodelled shifts between coordinate systems of DGPS and IMU Different measurement frequencies IMU DGPS Laser scanner mirror 16 8

9 Errors of integrated use of DGPS and IMU Time synchronization is not accurate GPS provides 1-10 observations per second IMU provides 200 observations per second During the post-processing, observations have to be interpolated If there was turbulence during the flight, the interpolation may be insufficient to provide accurate positions and attitudes Typically, much less GPS observations than IMU observations are obtained Positions between GPS observations are calculated according to the acceleration information from IMU (Kalman filtering) 17 Kalman Filtering (discrete) measurement noise covariance process noise covariance

10 GPS observation rate compared with IMU/INS observations and laser scanning rate T. Schenk, Modeling and Analyzing Systematic Errors in Airborne Laser Scanners, Technical Notes in Photogrammetry No 19, The Ohio State University 19 Positions between GPS observations are calculated according to the acceleration information from IMU IMU provides information of positions between GPS observations If positions are interpolated using only GPS observations, positions of laser ranging may differ from the real ones 20 10

11 Errors of GPS and IMU Δκ Errors of attitude Δω (roll) causes error in across track direction Δϕ (pitch) causes error in along track direction ΔX 0, ΔY 0, ΔZ 0 Δω ΔR Δβ Η Δϕ Δ Z Δ across track Δ along track along track across track 21 Altitude (and planimetric) error If the position of a laser scanner is wrong (ΔX 0, ΔY 0, ΔZ 0 ), similar error can be found from 3D point clouds 22 11

12 Errors of airborne laser scanning Scanning strips should have enough overlap in order to avoid gaps in data 23 Drift of an aircraft and Δκ error Δκ Because of a drift, the laser scanner is not scanning perpendicularly to flying direction Laser scanning swath becomes more narrow than planned Δκ error from INS observations (heading) is difficult to solve Flying direction ΔX 0, ΔY 0, ΔZ 0 Δω Δϕ Δβ ΔR Η Δ Z Δ across track Δ along track across track along track 24 12

13 Errors of airborne laser scanning Effects of the atmosphere E.g. air humidity and small particles distort range measurements Physical properties of the target Different materials have different reflectance 25 Effect of reflectance to range measurements The distribution of reflectance under the laser footprint has effect to range measurements Surface that is visualized with black color has very low reflectance. As an opposite the blue area has very high reflectance. Most probably, the range value is measured to the blue area

14 Effect of reflectance to range measurements Smallest detectable targets are defined partially by the reflectance For example, power lines are well visible from laser scanning data 27 Coordinate transformations to the local coordinate systems Typically, a laser point cloud is in the geocentric coordinate system (e.g. WGS84) Local coordinate systems usually use plane coordinate systems (e.g. ETRS-TM35FIN, KKJ, YKJ in Finland). Therefore, a transformation is needed. The height system needs special attention (the correct height datum is essential)

15 Height datum (set of parameters that define reference surface of height system) Geodetic datum defines the reference ellipsoid surface which is a mathematical model of the ground surface (GPS heights) Reference ellipsoid heights = geodetic heights Geoid is such equipotential surface which would coincide exactly with the mean ocean surface of the Earth Topographic height = ortometric heights = - 29 The geodetic height system in Finland Currently in Finland, the N2000 geoid is in use (accuracy is ±5 cm) In many cases, the older N60 geoid is still in use Differs ca cm from N2000. Difference is caused mainly by upthrust 30 15

16 How to eliminate errors of airborne laser scanning? We can use a data driven approach Use overlapping flying strips and compare 3D point clouds We can use sensor models that are associated to each original observation of 3D points Also uses overlapping flying strips, but the strategy is to correct system parameters 31 If only 3D points are used For each flying strips, the correction parameters are solved p ' i, j p i, j c j ( p i, j ) p ' i j = pi, j + c j ( pi,, j is a 3D point after the correction is the i th 3D point from the j th flying strip before correction is a function with which the flying strip is corrected ) 32 16

17 What kind of correction function we can use? Correction can include simple transformations (ΔX, ΔY, ΔZ) Crombaghs et al. (2000) and Kraus and Pfeifer (2001) used a function, which was depended on tilt and vertical shift, to correct only heights Kilian et al., 1996 ja Vosselman and Maas, 2001 use a function that had vertical and horizontal shifts, time depended drift and three rotations ) (, j c j p i 33 If the sensor model is used Each point requires known position of the laser scanner, outcoming angle of the laser ray and the observation time To calculate 3D points, laser scanners use the equation: p = f ( O( t ), R( t ), r, α, ) i, j i i i i s ti O t ) ( i R( t i ri αi ) is time of observation is origin of a scanner is attitude of a scanner is range observation is outcoming angle of laser ray s is a vector that describes system parameters. E.g. shift between GPS and laser scanner

18 In adjustment, the equation is p ' i, j = f ( O( t ) + ΔO, R( t ) + ΔR, r + Δr, α + Δα, s + Δs) i i i i Δ parameters can be constants, time depended functions, scale factors etc. 35 Correction parameters Burman, 2002 Constant shift and time depended ΔO and Filin, 2003 ΔR In addition, errors of IMU and mirror angle error Kager, 2004 Time depended polynomials for ΔO and ΔR, constants for shifts of IMU and mirror angle error in along and across track directions p ' i, j = f ( O( t ) + ΔO, R( t ) + ΔR, r + Δr, α + Δα, s + Δs) i i i i 36 18

19 Unknown parameters are soved in strip adjustment Requires observations between overlapping flying strips Coordinates of tie points Point distances from tie patches Tie points that are forced to locate on planar surfaces within tie patches Original observations (angles and range measurements) 37 To model errors, the area should contain enough good features Height errors are easy to correct, if planar areas exist Solving planimetric shifts require enough features Before corrections After corrections (C) Terrasolid Oy 38 19

20 Use of TIN surface models (Triangulated Irregular Network) in adjustment Create TIN models of each flying strips Compare 3D points (or TIN model) of another flying strip to TIN model Amount of data can be reduced by selecting every n th point The method can be used also with known control points The main goal of an adjustment is to solve sensor errors 39 Using grid models in adjustment Select the optimal size for one square within the grid Give the height value for each point of the grid using the laser point cloud (typically requires interpolation) The resulting raster model is used in adjustment 40 20

21 Post-processing of laser data Raw laser data describes a DSM (Digital Surface Model) Includes terrain surface, trees and buildings Usually we want to find a DTM (Digital Terrain Model) Trees and buildings are filtered away Sometimes we want to find an ndsm (normalized Digital Surface Model) ndsm=dsm-dtm (the effect of terrain surface is eliminated) 41 Various elevation models DSM=digital surface model Includes ground surface, trees and buildings DTM = digital terrain model Only the ground surface (buildings and trees are not included) 42 21

22 - DSM DTM = ndsm Applications_of_Laser_Scanning_in_Forestry.pdf 43 Post-processing of laser data Data filtering and classification Remove outliers Remove points that belong into the class that is not relevant for your application Creation of elevation model TIN Grid Contour lines 44 22

23 Clouds and small particles on the air cause outliers Birds and other small flying objects cause outliers Data filtering 45 Filtering in commercial software In TerraScan, potential air points are filtered by computing a median elevation and a standard deviation within a given distance. All points above the standard deviation multiplied by a given factor are considered air points LasTools classifies outliers by creating cells of adjustable size around a point in focus and by searching if neighboring cells have fewer points than a threshold 46 23

24 Filtering methods Outlier detection categories distribution-based depth-based clustering distance-based density-based methods 47 Ringing effect cause outliers below the true ground Appears usually with strong intensity echoes Rönnholm et al., Nordin, L.,

25 Data filtering, classification classification 1. Ground points 2. Buildings 3. Vegetation Low, middle and high vegetation 49 Automatic building extraction from laser point clouds 25

26 Data filtering, DTM Remove all other points but ground class points Create DTM by using these ground points 51 Classification 52 26

27 Strategies for data classification point-to-point, compare 2 points at time Decision whether points are classified into the same object class is based on positions Only one point is classified at a time point-to-points, one point is compared with neighbouring points Only one point is classified points-to-points, We take more points and calculate discriminat function Many points are classified at a time 53 Strategies for data classification slope-based the slope or height difference between two points is measured block-minimum Lowest point within small search space is selected. Other points within search window are compared with lowest altitude and accepted as ground points, if their altitude is close enough to the lowest altitude

28 Strategies for data classification surface-based Lowest points are used to create a parametric surface. Points are classified into the ground class if they are close enough to the parametric surface clustering/segmentation Points belong to an object if their cluster is above its neighborhood 55 Difficult cases in data filtering Very big objects Many filtering algorithms are local. Therefore, very large objects (which doesn t belong to the DTM) may not be filtered out Very small objects Very low objects Filtering algorithm cannot distinguish very low objects from the ground Variety and complexity of objects Disconnected terrain Specially in urban environment, terrain heights can vary significantly e.g. in the opposite sides of a buiding. Specially difficult is the case in which open ground is enclosed by objects 56 28

29 Example of filtering/classification: Axelsson (TerraScan uses it) Calculate initial parameters using all data Select seed points, which are most probably on the ground Create TIN using seed points Iterative densification of the TIN using new points that fulfil set threshold values Calculate parameters for each iteration from points included in the TIN Continue iteration until all points are classified as ground or object 57 Example of filtering/classification: Axelsson (TerraScan uses it) P d α d d γ β TIN facet d = min( d... d if ( d p < d P TIN 1 max ) n ) 58 29

30 Example of filtering/classification: Axelsson (TerraScan uses it) The cutting-off effect of sharp changes in the terrain surface is prevented by looking in the surrounding area 59 Visualization of DTM/DSM TIN models (triangulated irregular network) Grid models (raster models) Contour lines Profiles and cross-sections 60 30

31 TIN model Irregular node points Breaklines 61 Grid model VTT, GLORE, Mikael Holm Dark tones correspond to low elevations and bright tones to high elevations 62 31

32 Grid model Height values of a regular grid need to be interpolated from laser point clouds Inverse distance weighting Krieging Lagrangian interpolation Moving least squares Linear predictions 63 Contour lines

33 Profiles and cross-intersections 65 DTM accuracy Error budget of DTM contains errors in heights, planimetric coordinates, point density and (if GRID height model is used) interpolating σ DTM = σ elevation + σ planimetric + σ point density σ interpolation 66 33

34 The production process of nation-wide DTM in Finland National Land Survey of Finland is producing a nation-wide DTM At first, a grid model (resolution of 0.9 m) is interpolated from laser scanning data and from that contour lines (step is 1 m in elevation) are created for visualization purposes The quality is verified (and data is manually corrected) by superimposing contour lines onto aerial stereo images 67 Päivi Vinni, 2010 The production process of nation-wide DTM in Finland From the corrected 0.9 m grid model, a final grid model (the resolution of 2 m) is interpolated One elevation value is interpolated from 9 closest points with Lagrangian interpolation (polynomial fitting) Päivi Vinni,

35 The production process of nation-wide DTM in Finland Nation-wide laser scanning and production of DTM started in 2008 The elevation accuracy of 2 m grid model is (at least) 30 cm Annually ca m 2 has been scanned 69 DTM product, the National Land Survey of Finland Maa Lasekeilaus, syksymaanmittauslaitos m DEM 70 Paikkatiedon keruu ja muokkaus, Syksy