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Ayman F. Habib Photogrammetric & LiDAR Mapping Hybrid Approach for Automatic Building Model Generation by Integrating LiDAR and Image Data Chapter 6 Ayman F. Habib Photogrammetric & LiDAR Mapping 2 To avoid social and environmental problems arising from this rapid urbanization, federal and local governments must have access to accurate and current geo-spatial information in a timely manner and at a reasonable cost. • Global population is expected to rise to almost 5 billion inhabitants by 2030. • 62% of the global population at that time will be dwelling in urban areas. Globally Canada metropolitan areas rural areas (1971 – 2001) Population growth in Canada Introduction Ayman F. Habib Photogrammetric & LiDAR Mapping Introduction Natural disaster planning & evaluation Urban planning Military simulation 3D building models Telecommunication network planning 3 • 3D building models are among the key components of geo- spatial information in urban areas. Ayman F. Habib Photogrammetric & LiDAR Mapping • Current technology/tools (i.e., Google Earth, Microsoft’s Virtual Earth, etc.): – Good for navigation purposes – Level of accuracy is not high enough for design and engineering applications Examples of 3D building models in Calgary downtown (Source: Google Earth) Introduction 4 Need for automated tools that provide accurate and up-to-date 3D building models Ayman F. Habib Photogrammetric & LiDAR Mapping Existing DBM Generation Methodologies Data sources Imagery LiDAR Multi-source Processing strategy Data-driven Model-driven Hybrid Amount of user interaction Manual Semi- automatic Fully- automatic 5 Proposed Research Ayman F. Habib Photogrammetric & LiDAR Mapping 6 Reconstruction Strategy Model driven Data driven • Parametric models ‒ Few models • Generic models – Can deal with any model type • Building database – Prior knowledge about the buildings’ shapes • Sensory data – Only source of information • Procedure – Generation of multiple building hypotheses – The model achieving the best fit is the selected one • Procedure – Extraction of building primitives – Reconstruction of surface topology – Construction of the building model • Can only handle simple models such as flat-roof and gable-roof buildings • In theory, it can handle any type of buildings. Ayman F. Habib Photogrammetric & LiDAR Mapping Model-Based Approach 7 l w h l w h l w h rh l w h rh Box Wedge Gable-roof Shed-roof Model base Model-image fitting Adjusted model parameters (Wang and Tseng, 2003) Initial model parameters Adjust the initial model parameters until the model fits well with the edges extracted from the corresponding images Edge detection Research objectives: • Automatic generation of the initial model parameters • Reduce the number of models while being able to deal with box, wedge, gable-roof, and shed-roof types Ayman F. Habib Photogrammetric & LiDAR Mapping Proposed Workflow 8 LiDAR data Data-driven approach Hypothesized building rooftops Initial model parameters Model-driven approach Refine the necessary model parameters Stochastic properties of the model parameters Multiple images Edge detection Model-image fitting LiDAR plane para. Ayman F. Habib Photogrammetric & LiDAR Mapping 9 P ro p o se d W o rk fl o w Ayman F. Habib Photogrammetric & LiDAR Mapping Data-Driven Stage (LiDAR) 10 Building Hypothesis Generation •Terrain/off terrain •Planar/non-planar point classification •Grouping •Area/height computation Segmentation Boundaries for Hypothesized Buildings •Neighbourhood & attribute comp. •Clustering (Habib et al., 2010 & Lari and Habib, 2011) LiDAR Point Cloud Processing •Local point density estimation Ayman F. Habib Photogrammetric & LiDAR Mapping • Orthophoto over the test area Data-Driven Stage (LiDAR) 11 Ayman F. Habib Photogrammetric & LiDAR Mapping • Original LiDAR data 12 Data-Driven Stage (LiDAR) Ayman F. Habib Photogrammetric & LiDAR Mapping • An adaptive cylinder, which changes its orientation with the local planar surface, is used to decide whether the point belongs to a planar or rough surface. Point Classification & Local Point Density 13 Ayman F. Habib Photogrammetric & LiDAR Mapping If the iterative plane fitting procedure does not converge within a pre-specified number of iterations, the point of interest is classified as a rough point. First Plane d Removed as outliers Derivation of the Adaptive Cylinder Point Classification & Local Point Density 14 Ayman F. Habib Photogrammetric & LiDAR Mapping Planar Non-planar • To classify the point as being part of a planar surface, the following conditions should be satisfied: The point of interest should be inside the adaptive cylinder. The majority of the points in the spherical neighbourhood should be inside the adaptive cylinder. Point Classification as Being Part of a Planar Surface Point Classification & Local Point Density 15 Ayman F. Habib Photogrammetric & LiDAR Mapping 2 kr n LPD n Number of the points that belong to the adaptive cylinder. sphere)of pnts ink (number cylinder) adaptive of pnts inn (number Estimation of the Local Point Density Point Classification & Local Point Density 16 Ayman F. Habib Photogrammetric & LiDAR Mapping Planar Surfaces Rough Surfaces • Planar & rough surface classification Data-Driven Stage (LiDAR) 17 Ayman F. Habib Photogrammetric & LiDAR Mapping • Point density map Data-Driven Stage (LiDAR) 18 0.07 1.63 pts/m2 Ayman F. Habib Photogrammetric & LiDAR Mapping Each colour represents a group of planar points. The groups are derived using a region growing algorithm based on the estimated local point density. • Grouping Data-Driven Stage (LiDAR) 19 Ayman F. Habib Photogrammetric & LiDAR Mapping Segmentation Neighbourhood Definition Attribute Computation Aggregation/Clustering Segmentation Parameter-Domain Segmentation 20 Derived from the adaptive cylinder Octree-based peak detection Ayman F. Habib Photogrammetric & LiDAR Mapping • Segmentation Data-Driven Stage (LiDAR) 21 Ayman F. Habib Photogrammetric & LiDAR Mapping Data-Driven Stage (LiDAR) • Boundary detection 22 Hybrid approach for outer/inner boundary detection Ayman F. Habib Photogrammetric & LiDAR Mapping Non-ground, Rough Ground, Rough Non-ground, Planar Ground, Planar Data-Driven Stage (LiDAR) • Ground/non-ground classification 23 Ayman F. Habib Photogrammetric & LiDAR Mapping 24 Customized Parameters for Building Hypothesis Generation Building Hypothesis Planar / Non-Ground Group Height > Threshold (e.g., 3m) Group Area > Threshold (e.g., 10 m2) Building Hypothesis Generation Ayman F. Habib Photogrammetric & LiDAR Mapping 25 Terrain/Off-terrain Classification Segmentation/ QC Process LiDAR Point Cloud Data-Driven Stage (LiDAR) Ayman F. Habib Photogrammetric & LiDAR Mapping 26 Building Hypotheses Generation Boundary Detection Aerial Photo Irregular boundary due to low sampling rate needs to be regularised Data-Driven Stage: Building Boundaries Ayman F. Habib Photogrammetric & LiDAR Mapping 27 P ro p o se d W o rk fl o w Ayman F. Habib Photogrammetric & LiDAR Mapping Model-Driven Stage (LiDAR & Imagery) 28 Boundary Regularization & Refinement •Minimum Bounding Rectangle (MBR) – simple box model •Terrain/off terrain •Planar/non-planar point classification •Grouping •Area/height computation Segmentation •Neighbourhood & attribute comp. •Clustering (Habib et al.,2010 & Lari and Habib, 2011) LiDAR Point Cloud Processing •Local point density estimation We will start with a simple box model? •Recursive MBR – complex models •Model-image fitting Ayman F. Habib Photogrammetric & LiDAR Mapping Model Choice 29 Box model Rectangular model Can represent all the desired building models l w h l w • Building footprint might not be visible in the imagery. • Building height can be derived from LiDAR. • Building footprint can be derived from its rooftop & DTM. Ayman F. Habib Photogrammetric & LiDAR Mapping 30 l w h Box l w h Wedge l w h rh l w h rh Gable-roof Shed-roof • The involved parameters change with the model type. Building models Model Choice Ayman F. Habib Photogrammetric & LiDAR Mapping 31 Model Choice Box Wedge Gable-roof Shed-roof Rectangular model Required Model Parameters: (Xm0, Ym0, Zm0, ωm, φm, κm, l, w) Ayman F. Habib Photogrammetric & LiDAR Mapping Rectangle Model Parameters 32 𝓁 Y Z X κ𝑚 (𝑋𝑚0,𝑌𝑚0) W 𝓌 U V (ωm, φm) Zm0 Required Model Parameters: (Xm0, Ym0, Zm0, ωm, φm κm, l, w) derived from LiDAR data derived from LiDAR data •Model parameters to be estimated: (Xm0, Ym0, κm, l, w) •Approximate values can be derived using the Minimum Bounding Rectangle (MBR). •Model parameters are refined through a LSA-based model- image fitting. Ayman F. Habib Photogrammetric & LiDAR Mapping Initial Parameters for the Rectangle Primitive 33 • Minimum Bounding Rectangle – MBR (Freeman and Shapira, 1975) The minimum area rectangle among the rectangles with an arbitrary orientation that contains the vertices of the boundaries Aerial image Segmented LiDAR patch Traced boundary Regularized boundary Ayman F. Habib Photogrammetric & LiDAR Mapping 34 Aerial image LiDAR derived boundary Bounding rectangles with arbitrary orientations MBR Initial Parameters for the Rectangle Primitive Ayman F. Habib Photogrammetric & LiDAR Mapping Edge Detection and Filtering 35 edge linking & removal of short segments Canny edge detection Edge filtering (position and orientation) Ayman F. Habib Photogrammetric & LiDAR Mapping Model-Image Fitting 36 y V U θ 𝑣i 𝑣i+1 Edge pixel (𝑥𝑒𝑑, 𝑦𝑒𝑑) 𝑣i (𝑥i, 𝑦i) 𝑣i+1 𝑥i+1, 𝑦i+1 d x Adjusted line 𝑣i(adjusted) Initial projected line model- image fitting • Least-Squares Adjustment Implementation: 1. Normal distance constraint 𝑑 = 𝑓𝑛 𝑥𝑖 , 𝑦𝑖 , 𝑥𝑖+1, 𝑦𝑖+1, 𝑥𝑒𝑑 , 𝑦𝑒𝑑 2. Modified LSA using pseudo-conjugate points 𝑥𝑒𝑑 = 𝑥𝑖 + 𝐷𝑥 + 𝑒𝑥 & 𝑦𝑒𝑑 = 𝑦𝑖 + 𝐷𝑦 + 𝑒𝑦 𝑥𝑖 , 𝑦𝑖 = 𝑓 𝑋𝑖 , 𝑌𝑖 , 𝑍𝑖 , 𝐼𝑂𝑃, 𝐸𝑂𝑃 𝑋𝑖 , 𝑌𝑖 , 𝑍𝑖 = 𝑓𝑚 𝑚𝑜𝑑𝑒𝑙 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠 𝑒. 𝑔. , 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑝𝑜𝑖𝑛𝑡: 𝑋𝑖 = 𝑋𝑜&𝑌𝑖 = 𝑌𝑜 • The model-image fitting is done using all the rectangle sides and the available images. Ayman F. Habib Photogrammetric & LiDAR Mapping 37 Simple Box / Gable Roof Building How to deal with complex building models? Recursive Minimum Bounding Rectangle Ayman F. Habib Photogrammetric & LiDAR Mapping 38 1st level MBR Recursive MBR 2nd level MBR • non-overlapping initial boundary with 1st level MBR • non-overlapping 1st level MBR with initial boundary Final MBR Subtract • Different levels of MBRs will be adjusted sequentially during model- image fitting Some of the lower-level MBRs’ parameters are utilized for the higher-level MBRs Ayman F. Habib Photogrammetric & LiDAR Mapping 39 LiDAR boundary 1st level MBR Non-overlapping boundary points Projection of non- overlapping points 2nd level MBR Non-overlapping boundary points Projection of non- overlapping points 3rd level MBRs Non-overlapping boundary points 4th level MBR Projection of non- overlapping points 𝐹𝑖𝑛𝑎𝑙 𝑆ℎ𝑎𝑝𝑒 = −1 𝑛+1 𝑘 𝑛=1 𝑀𝐵𝑅𝑛 • The planimetric coordinates of the boundary should be improved • Different MBR levels will be sequentially adjusted during model-based image fitting Some of the lower-level MBRs’ parameters are utilized for the higher-level MBRs Sequential MBR Adjustment: Multiple Levels Ayman F. Habib Photogrammetric & LiDAR Mapping Dataset Description 40 BCIT dataset Photogrammetric data LiDAR data System MFDC Rollei P-65 Leica ALS50 Average flying height 540m and 1,150m 540m and 1,150m GSD for image / Average point density for LiDAR 5cm and 10cm 1.5 and 4.0 points/m2 Strip 1 Strip 2 Strip 3 Strip 4 Strip 5 Strip 6 Strip 1 Strip 2 Strip 3 Strip 4 Strip 5 Strip 6 LiDAR strips configuration (left) configuration of aerial photos (right) Ayman F. Habib Photogrammetric & LiDAR Mapping • Estimated model parameters & their standard deviations for a simple rectangular building Model Parameters - Results Modified LSA Normal distance constraints Unknown Parameter Initial value Final value after adjustment Standard deviation of adjusted parameters Final value after adjustment Standard deviation of adjusted parameters 500238 500239.626 ± 0.008 500239.646 ± 0.008 5455402 5455404.374 ± 0.007 5455404.317 ± 0.006 10 11.571 ± 0.007 11.567 ± 0.006 42 43.823 ± 0.012 43.882 ± 0.010 -138 -142.133 ± 0.008 -142.044 ± 0.015 41 Ayman F. Habib Photogrammetric & LiDAR Mapping 42 Model-Based Image Fitting: Number of Images • The proposed methodology works with single or multiple images. • Using more images captured from different views will reduce the impact of occlusions. • The impact of the number of utilized images on the quality of the derived models has been examined. Ayman F. Habib Photogrammetric & LiDAR Mapping 43 • The quality of the models can be compared in a qualitative way by projecting the reconstructed points onto one of the images. • When visually checked, all of the projected lines coincide with building edge lines and all the cases show good results. manual reconstruction 1 image 2 images 4 images 5 images Qualitative Analysis Ayman F. Habib Photogrammetric & LiDAR Mapping Quantitative Analysis 44 RMSE [m] manual reconstruction vs. X Y Z Total single image 0.03 0.08 0.23 0.24 two images 0.03 0.09 0.23 0.25 four images 0.05 0.08 0.23 0.24 five images 0.05 0.07 0.23 0.24 • RMSE between the reconstructed corner points using different number of images and the manual reconstruction are compared. • The accuracy of the results is not affected by the number of utilized images based on the comparison. Ayman F. Habib Photogrammetric & LiDAR Mapping 45 Integrated Product (X, Y, Z): 1122.23 m, 3251.53 m, 72.03 m (±10 – ±30cm) (R, G, B): 23, 136, 69 Accurate Enough for Engineering Applications Ayman F. Habib Photogrammetric & LiDAR Mapping 46 • University of Calgary main campus area Applicable Partially applicable Non applicable Total # of buildings 37 10 4 51 Percentage of buildings 72.5 % 19.6 % 7.8 % 100 % Less than 10 % of the buildings cannot be modeled Future work: For partially possible buildings, what can we do? Experimental Results Ayman F. Habib Photogrammetric & LiDAR Mapping 47 Realistic 3D Modeling / Visualization Ayman F. Habib Photogrammetric & LiDAR Mapping Concluding Remarks • The presented approach takes advantage of overlapping image and LiDAR data that has undergone rigorous QA/QC and registration procedures. • The level of automation is increased by utilizing the LiDAR point cloud in a data-driven approach to generate the initial model parameters while using the imagery in a model-driven approach to refine the model parameters. – A general model that can deal with rectangular, gable, wedge, shed rooftops (a building can be a mix of these primitives)– The sequential MBR has been developed to deal with more complex building rooftops. • The stochastic properties of the model parameters are provided by the proposed approach. • The LiDAR DSM after its enhancement with a DBM leads to better 3D visualization of the integrated imagery and LiDAR data. 48 Ayman F. Habib Photogrammetric & LiDAR Mapping 49 Camera QA/QC LiDAR System QA/QC Co-registration Imagery (R, G, B) DSM (X, Y, Z) Preliminary Orthophoto DBM Photogrammetric Data LiDAR Data Realistic 3D Visualization Enhanced DSM Refined Orthophoto Building Hypothesis Extracted DTM OR • For effective integration of photogrammetric/LiDAR data, one must address these basic operations: QA/QC Registration Linkage of spectral/positional information Tight integration should be explored. Concluding Remarks Ayman F. Habib Photogrammetric & LiDAR Mapping 50
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