Mar 11, 2001 - A high-speed counter measures the time of flight from the start pulse to the return pulse;. 4. ... After establishing the spatial vector from the laser sensor to the point of reflection, the 3-D coordinates (X, Y, ..... Each laser point (X, Y, Z) defines a plane (Eq. 2) in the cluster space. ..... reflected by a bird in the air).
Analysis and Processing of Airborne LIDAR Data
Yong Hu
Department of Geomatics Engineering University of Calgary March 11, 2001
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Table of Contents 1
STATEMENT OF THE PROBLEM.................................................................................................................. 1
2
RESEARCH OBJECTIVES.............................................................................................................................. 1
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LITERATURE REVIEW..................................................................................................................................... 2
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METHODOLOGY AND PROCEDURES ...................................................................................................... 14
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OUTLINE OF THESIS CONTENTS .............................................................................................................. 17
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PROPOSED SCHEDULE ............................................................................................................................... 17
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BIBLIOGRAPHY AND REFERENCES ........................................................................................................ 18
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1 Statement of the problem Airborne LIDAR (LIght Detection And Ranging) or LADAR (LAser Detection And Ranging) represents a new and promising technology for the highly automated acquisition of digital surface models (DSMs) with high accuracy comparable with traditional land surveys and photogrammetry. A LIDAR system uses a laser sensor, a geodeticquality Global Positioning System (GPS) receiver and an Inertial Navigation System (INS) unit. After establishing the spatial vector from the LIDAR platform to the point of reflection for each laser shot, 3-D coordinates of the footprint can be obtained with overall accuracy usually in dm order. Many post-processing techniques have been developed to interpret and model this irregular 3-D point cloud into useful representation for geo-referenced applications, such as 3-D landscape visualization that is an important part of the digital earth. Although LIDAR systems are already in a fairly mature state, the post-processing of laser range data is still in an early phase of development because no single technique currently is considered optimum or satisfactory for all conditions and few commercial software packages are available till today. There are problems with typical production workflow and existing post-processing algorithms, mainly: 1. 2. 3.
4. 5.
The production procedures heavily depend on manual operation, which is very labour intensive (Vosselman, 2000). As a direct by-product of laser scanning, intensity data is seldom used in the processing of LIDAR data. The qualities of the results are subject to many parameters of the algorithms. The optimal values for these parameters are difficult to determine and vary with the terrain type and point density. For example, commonly used morphological filters may successfully remove points from treetops, but may inadvertently cut off important ground features such as the edges of steep embankments, rock outcroppings. Various combinations of terrain type and LIDAR point density can cause surface estimation algorithms to fail in certain areas. For example, large rock outcroppings, dense tree stands, dam walls, bridges and buildings may have similar elevation cross-sections. The quantitative analysis of classification errors is difficult to formulate and has not been investigated in the past. To guarantee a certain level of accuracy, LIDAR-derived DTMs should be verified by human operators, which is also labour intensive.
2 Research objectives The most direct application of LIDAR technology concerns the generation of DEMs. It needs to identify the laser points corresponding to positions on the ground, and to remove other points hit on buildings and other constructions. In some cases, detailed terrain features and structures are discernible and can be derived from the geometric information alone which is provided by laser scanning of high sampling density. For instance, breaklines of the terrain can be extracted to some extent. I will focus my study on post-processing algorithms for building extraction and reconstruction in urban areas, reduction of DSM to DTM in wooded areas, and topographical feature extraction. The objective is to design and develop better strategies, algorithms, and engineering scenarios in using LIDAR technology for the applications where airborne LIDAR mapping may offer significant advantages. The following tasks are scheduled: 1. 2. 3. 4. 5. 6. 7.
DEM generation from range data in urban and wooded areas. DEM generation from range and intensity data in urban and wooded areas. Blunders removal and adaptive parameters selection. Building detection and reconstruction in urban and mountainous areas. Planar face intersection and modelbased recognition may be used to reconstruct buildings. Automated and manual topographical feature extraction along valley, ridge, and incorporation of topographical features into the processing of LIDAR data. Processing at special spots (e.g., bridges). Examination of the quality of the derived DEMs as a function of the terrain types (e.g., flat or hilly) and the point density. •1•
3 Literature review 3.1 Airborne LIDAR technology Airborne LIDAR is an aircraft-mounted laser system designed to measure the 3-D coordinates of an object. This is achieved by combining a laser with positioning and orientation measurements. The laser measures the range to the ground surface or objects, and yields the 3-D position when combined with the position and orientation of the sensor. It is an active sensor-based mapping system as opposite to passive photogrammetry. Airborne LIDAR systems use accurate direct geo-referencing technology so it collects the elevation data directly and digitally. The high point sampling densities, fast turn-around time and lower cost are among the most attractive characteristics for many terrain-mapping applications.
3.1.1 Airborne LIDAR Systems A typical airborne LIDAR system (ALS, see Figure 1, adapted from http://www.ifp.uni-stuttgart.de/ifp/sensor/laser.gif), is composed of a laser range finder (LRF), a positioning and orientation system (POS), realized by an integration of the differential GPS, an inertial measurement unit (IMU) and the control unit (Wehr and Lohr, 1999). Most LIDAR systems use a scanning mirror to generate a swath of light pulses. Swath width depends on the mirror's angle of oscillation and flight height, and the ground point density depends on factors such as aircraft speed, the oscillation rate of mirror, laser pulses rate and fight height. Ranges are determined by computing the amount of time that it takes laser beam to leave an aeroplane, travel to the ground and return to the sensor. The 3-D positions of terrain points are determined by the position and attitude of the sensing unit, instantaneous mirror angle and the collected ranges. The minimum, maximum and typical values for some major technical parameters of LIDAR systems are summarized in Table 1 to allow an easy overview (Baltsavias, 1999a). LIDAR systems are less sensitive to environmental conditions such as shadows, weather, sun angle, leaf on/off condition. LIDAR can also work at night without the degradation in performance. Due to these characteristics, Figure 1. A typical airborne LIDAR system LIDAR has found applications where ground survey is limited or risky to field crews or aerial photogrammetry is prohibited or not cost effective. Table 1. Overview of major technical parameters of LIDAR systems Parameters Scan angle (o) Pulse rate (kHz) Scan rate (Hz) Flying Height − h (m) GPS frequency (Hz) IMU frequency (Hz) Bean divergence (mrad) Swath width (m) Across-track spacing (m) Along-track spacing (m)
Minimum value 14 5 20 20 1 40 0.05 0.25 h 0.1 0.06
Maximum value 75 83 630 6100 10 200 4 1.5 h 10 10 •2•
Typical values 20−40 5−15 25−40 200−300 (H), 500−1000 (A)* 1−2 50 0.3−2 0.35−0.7 h 0.5−2 0.3−1
Range accuracy (cm) Elevation accuracy (cm) Planimetric accuracy (m) * H = helicopter, A = aeroplane. 3.1.1.1
2 10 0.1
30 60 3
5−15 15−20 0.3−1
Laser Range Finder
The laser range finder works a lot like ordinary radar, except that it sends out narrow pulses or beams of light rather than broad radio waves. It consists of two units: an opto-mechanical scanner and a laser ranging unit. The scanner comprises the laser transmitter and the electro-optical receiver. The LRF works as follows: 1. The laser scanner generates an optical pulse; 2. The pulse is reflected off an object and returns to the receiver; 3. A high-speed counter measures the time of flight from the start pulse to the return pulse; 4. The time measurement is converted to a distance. The laser ranging unit depends on knowing the speed of light, which is approximately 0.3 meters per nanosecond. Using that, one can calculate how far a returning light photon has travelled to and from an object (see Figure 2). 3.1.1.2
Position and Orientation System
An integrated position and orientation system consists of a differential global positioning system (DGPS) and an inertial measuring unit (IMU). The GPS is a constellation of 24 satellites that orbit the Earth and transmit signals. It allows one to calculate one's position on the Earth to the accuracy of better than 10 cm. The IMU consists of three gyroscopes, sensing angular velocity that should be measured in a platform-fixed coordinate system. Inertial techniques are based on a simple principle that the orientation of an object can be determined by a single integration of angular velocity, which can be used to calculate the three rotation angles. As shown in Figure 2, the GPS provides the scanner position ( x0 , y0 , z 0 ) ; the IMU provides the platform
rotation angles ω (the angle around the flight direction), ψ (the angle around the across-track direction) and κ (the angle around the vertical axis). 3.1.1.3
Figure 2. Laser range, scanner position, flight height, and three rotation angles
Control unit
The control-, monitoring- and recording-units manage the digital interfaces between LRF and POS. Geocoding of laser scanner measurements requires an exact synchronisation of both LRF and POS. The LRF measures only the spatial vector from the laser scanner to a ground point on the Earth's surface shot by a laser bean. The 3-D position of this point can only be computed, if at any time, the position and orientation of the scanner is known with respect to a global coordinate system, e.g., WGS84 − a world geodetic system. The recording-unit combines the laser range data with GPS position and IMU orientation information at the epoch of each laser shot, then performs a series of transformations to rotate and translate the laser range from a local aircraft coordinate system to WGS84. 3.1.1.4
Existing systems and firms
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3.1.2 LIDAR survey projects 3.1.2.1 3.1.2.2 3.1.2.3
Calibration of scanning strips Determination of tie and control point information Estimation of transformation parameters
3.1.2.4 LIDAR Data After a laser sensor head is rigidly fixed inside an aircraft, a precise calibration will be carried out to determine the offset between the sensor and the GPS antenna. During a flight mission, an aircraft rotates on three different axes, commonly known as roll, pitch and yaw. These axes determine an aircraft's attitude, and this orientation affects the antenna's spatial relationship relative to sensor origin as an aircraft moves through the air. An IMU inside a LIDAR system calculates attitude, which allows GPS-derived positions to be transferred from the GPS antenna to the sensor system's origin - the center of the mirror. IMU combines fibre-optic/ring-Laser gyroscope technology and an orthogonal accelerometer triad. Data are recorded regularly at a certain rate (e.g., 50Hz), yielding the sensor's position and attitude at the same rate. Combining GPS and IMU information using advanced Kalman Filtering techniques allows for the determination of more precise position and attitude. The outcome is a complete set of exterior orientation data (i.e., X0, Y0, Z0, ω , ψ , κ ) for sensor origin and output. Point positions (first or last return) then are determined by combining the measured ranges, scanning angles of mirror and exterior orientation data in the post-flight processing. After establishing the spatial vector from the laser sensor to the point of reflection, the 3-D coordinates (X, Y, Z) of a specific laser footprint can be calculated by Eq. (1) (Kilian et al., 1996). This equation assumes no errors and perfect system synchronization.
éX ù éX0 ù é0ù ê Y ú = ê Y ú + R(ω ,ψ ,κ ) ⋅ ê 0 ú ê ú ê 0ú ê ú êë Z úû êë Z 0 úû êë D úû
(1)
The parameters of flying height, swath angle, scanning rate, flight-strip side lap and aircraft velocity, determine the point density, and these parameters are tailored to accommodate the project requirements. At present, claimed vertical accuracies of commercially available LIDAR systems typically are on the order of 15 centimetres. A need for accuracy validation and possible adjustment stems from the existence of residual systematic biases in GPS, IMU and LIDAR systems. Adjustment techniques vary in sophistication, depending on the application and size of the project. Techniques range from simple vertical translations of mass points to remove vertical bias to complex block adjustments that involve ties among strips, ground controls and modeling systematic errors on a time-varying basis. Further, for high accuracy applications such as two-foot contouring, an adjustment to a network of ground control points may be required to guarantee a desired accuracy (Sapeta, 2000). The direct products of LIDAR systems are so-called digital surface models (DSMs) that are formed by the point clouds returned from the top of the earth surface partly covered by manmade or natural ground objects. In addition to range data, some LIDAR systems provide information on the intensity of the recorded signal, information for multiple echoes from each pulse and image information taken by digital video cameras (Haala et al., 1996; Baltsavias, 1999a).
3.2 LIDAR Mapping Applications The most direct application of Airborne LIDAR technology concerns the generation of high quality topographic DEMs. Buildings and other constructions are obstructions that need to be removed in the DEM generation. A promising application of LIDAR technology concerns the 3-D reconstruction of buildings for 3-D city modeling. Other applications include mapping of electrical transmission lines, measurement of coastal areas, mapping of surfaces with very little or no texture (e.g., snow/ice-covered areas, sand, swamps and wetlands), landscape design, generation of 3-D models (e.g., visualization and fly- or walk-throughs), video and computer game production, derivation of vegetation parameters (e.g., tree height, crown diameter, tree density, and biomass estimation) etc. (Airbornelasermapping.com, 2000; Hill et al., 2000; Wehr and Lohr, 1999; Axelsson, 1999).
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DEM generation. Airborne LIDAR mapping is a cost-effective source of high-density elevation data for many traditional topographic mapping applications such as contouring. It allows large area topographic surveys to be completed significantly faster and at a reduced cost compared traditional survey methods.
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3-D city model construction. Accurate digital models of urban environments are required for a variety of applications including telecommunications, wireless communications, urban planning, microclimate modeling, propagation of noise and pollutants, and disaster response. Since LIDAR systems provide dense measurements, detection and extraction of 3-D objects with sharp discontinuities, especially buildings, are easier than in DSMs obtained by stereo matching, or faster than manual collection. With the 3-D city model, different perspective views can be generated from different positions and directions for landscape analysis. Telecommunication operators can optimise antenna sites with the line-of-sight analysis. The derived building footprints or polygons can be used to model the movements of varying wind conditions. As air movements are better understood, the behavior of chemical and pollution plumes also can be studied.
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Forestry. LIDAR systems can provide measurements on the ground. The penetration rate mainly depends on the types of trees and season. Information on tree heights and densities is also difficult to collect using traditional methods. Multiple returns allow the data to be analyzed and classified as vegetation, while the ground return allows DTMs of the bare ground to be generated and accurate tree heights to be calculated. Thus the derivation of other important parameters like biomass estimation, tree type etc. are possible. Accurate information on the terrain and topography beneath the tree canopy is extremely important to both the forestry industry and natural resource management. So it is a more effective and suitable technology for forestry companies when compared to photogrammetry or extensive ground surveys.
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Corridor or right-of-way mapping. Airborne LIDAR mapping provides rapid range data collection of long, linear objects such as roads, railway tracks, pipelines, waterways, coastal zone or power lines. Since LIDAR systems have a narrower swath in comparison to optical sensors, they are more cost-effective in capturing information needed for above applications. A major market is mapping power line corridor to allow for proper modeling of conductor catenary curves, sag, encroachment and accurate determination of tower locations.
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Change detection. LIDAR can be used to efficiently locate areas of change, which is invaluable information for subsequent update mapping. Original LIDAR DSMs taken during the first mapping phase are compared to later datasets, and areas of change can be quickly located by superposition. This is important in some cases, e.g., involving natural disasters.
There are other cases where detailed terrain features and structures are discernible and can be derived from the geometric information alone which is provided by laser scanning of high sampling density. For instance, breaklines of the terrain can be extracted to some extent (Kraus and Pfeifer, 1998; Wild and Krzystek, 1996), and infrared laser light provides an excellent discrimination along water boundaries (DeLoach, 2001). Other examples are dunes, hedges, walls ditches, dams etc., which can be delineated from laser point clouds, especially on flat terrain (Ackermann, 1999). Since airborne LIDAR is a relatively new technology, applications are still being identified and developed as end-users start to work with the data. Numerous research efforts are underway to investigate other areas where LIDAR technology may provide significant advantages such as allowing value-add products to be generated and offering great cost reduction over traditional methods (Airbornelasermapping.com, 2000).
3.3 Post-processing Algorithms The existing LIDAR technology does not provide a real-time solution, it needs a quite amount of work in the postprocessing stage. As LIDAR systems only acquire the digital surface models (DSMs) including many non-terrain features (e.g., buildings, trees, cars, etc.), the post-processing work mainly involves the separation of bald terrain surface and non-terrain features. There are many algorithms developed for the generation of bald earth DEM from LIDAR data. Some algorithms (e.g., filtering and model-based recognition) are purely based on range data itself such as single return or multiple returns range data (Petzold et al., 1999; Maas and Vosselman, 1999; Kraus and Pfeifer, 1998); other methods are based on the use of range data and auxiliary data such as available 2-D GIS map or •5•
additional imagery (Haala and Brenner, 1999). In addition to the bald surface DEM generation, automatic analysis and classification of various application-specific features (e.g., building detection, powerline and railroad processing) are being developed in the post-processing packages (TerraSolid, 1999) although extensive manual operations are needed and the development is still rudimentary. To the knowledge of us, six main techniques are being used for processing LIDAR data: filtering, smoothing, interpolation, segmentation, classification and modeling. A summary of some algorithms is given in Table 2. §
Filtering. Filtering refers removing unwanted measurements, or finding ground surfaces from a mixture of ground and vegetation measurements. To distinguish points situated on buildings and on the vegetation from those that are expected to be on the ground, special filtering algorithms are needed.
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Smoothing. Smoothing can remove random noise and produce a smoother surface so that the contours may look nicer. It is often an iterative process, comparing a point with nearby points and adjusting its elevation. Usually, a best-fit facet model (e.g., a plane equation) is computed for a group of nearby points, and the elevation of the centre point is adjusted to better match the facet model.
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Interpolation. There are several interpolation methods to resample the scattered points into a raster format surface, or to recover the natural terrain surface under non-terrain objects. The most commonly used interpolation methods are nearest neighbor, bilinear interpolation and cubic convolution. Others involve inverse distance weighting, TIN based on Delaunay triangulation, and Krigging.
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Segmentation. Segmentation means the separation or cluttering of a point cloud into homogeneous groups that describe different geometric or statistic structures, e.g., buildings, and vegetation.
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Classification. Classification allows for the extraction of a DEM that presents the ground and other features i.e., buildings, trees, towers etc. Statistical pattern recognition methods often are used to discriminate among several categories of ground objects, such as buildings, roads, grass-covered regions, and trees.
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Modeling. Models based on geometric properties of an object's visible surfaces or silhouette are commonly used. They describe objects in terms of their constituent shape features and covert them into a vector format that can be used by CAD systems. Table 2. Overview of several post-processing algorithms for LIDAR data Authors Nature of method Single return range data Kraus & Pfeifer, interpolation, filtering, iteration 1998 Petzold et al., filtering, interpolation, iteration 1999 Murakami et al., subtracting, filtering, segmentation 1999 Maas & segmentation, subtracting, Vosselman, 1999 modeling Maas & segmentation, cluttering, surface Vosselman, 1999 intersection Multiple returns range data Kraus & Rieger, filtering, interpolation, subtracting, 2000 segmentation Range and auxiliary data Haala & filtering, subtracting, classification, Brenner, 1999 iteration Haala & segmentation, modeling Brenner, 1999
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Summary of method DEM generation for wooded areas General-purpose DEM generation Change detection of buildings Derivation of house model parameters by invariant moments House model reconstruction by intersection of planar faces Distinguishing between forested and nonforested areas Ground objects classification combining range data and multi-spectral imagery Building reconstruction using range data and 2-D ground plan
3.3.1 Single Return Range Data Many post-processing algorithms developed are solely or mainly based on single return range data (usually the first or last echo of the laser pulse). Baltsavias et al. (1995) use an edge operator, mathematical morphology, and height bins for the detection of objects higher than the surrounding topographic surfaces. Weidner and Forstner (1995) develop an algorithm to extract parametric and prismatic building models automatically from dense DEM generated by photogrammetric techniques or airborne laser scanning. Hug et al. (1997) propose a morphological filter to separate terrain and non-terrain points by iteratively computing the probability values of each point indicating the probability to be a terrain point. Brunn and Weidner (1998) discriminate buildings and vegetation based on a Bayesian nets classification algorithm using local geometric properties. They also extracted roof structures as a first step toward the reconstruction of polyhedral building descriptions. Kraus and Pfeifer (1998) apply interpolation and filtering against the LIDAR data in wooded areas based on the fact that laser footprints often are not on the terrain but on the tree tops. To generate DEM, Petzold et al. (1999) propose a filtering algorithm to distinguish points situated on buildings and on the vegetation from those expected to be on the ground. Maas and Vosselman (1999) develop two new techniques for the determination of building models. Based on the computation of invariant moments, closed solutions are formulated for the determination of parameters of simple building models, and asymmetric deviations like dorms on roofs are also modeled. Models of more complex buildings are determined using a data driven technique based on the intersection of planes fitted into triangulated point clouds. Murakami et al. (1999) has developed an algorithm for detecting building changes by computing a difference image of DSM data acquired at different occasions over the same area. In the following, several typical algorithms are discussed. § Petzold, Reiss and Stossel (1999) - DEM Generation by Filtering A standard procedure for handling and verification of range data has been developed based on this approach. It depends on the scanning technique used and the point density. Data. The area selected in Bavaria comprised two map sheets in the scale of 1:25000 with a total area of about 310 km2. The number of points received from the laser scanner and classified as ground points amounted to about 16.5 million, which corresponds to an average point distance of 4.1 m. Method. First, a rough terrain model is calculated by the lowest points found in a moving window of a rather large size; next, all points with a height difference exceeding a given threshold are filtered out, and a more precise DEM is calculated. This step is repeated several times, reducing the window size and finally leading to the DEM. Discussion. The results were surprisingly good, especially for wooded areas. The accuracy is equal or better than the one achieved by the photogrammetric stereo compilation. The misclassifications are normally on really large houses or are generated in connection with small and complicated terrain forms. § Maas and Vosselman (1999) - Derivation of House Model Parameters This approach is based on the analysis of invariant moments of point clouds. Closed solutions for the parameters of a standard gable roof type building model are derived from 0th, 1st and 2nd order moments of point clouds. In addition, the analysis of deviations between point cloud and model allows for detecting and modeling asymmetries such as dorms on a gable roof. The computation of the building model parameters from moments assumes a homogeneous point distribution. Inhomogeneity in the point distribution will lead to biased parameters. Data. An FLI-MAP laser scanner dataset covering an urban area of 500 x 250 m2 in the Netherlands was used. The average point density within the laser scanner strips is in the order of 5 points/m2. The original LIDAR points are used without the requirement of an interpolation to a regular grid. Method. First of all, the dataset was segmented by thresholding, binning, morphological filtering and connected component labelling techniques. The application of height histogram analysis, morphological filtering, connected component analysis and the assumption of a minimum height and area per building turned out to be appropriate for segmentation of the data used. In the first approach, a standard gable roof type house model contains the coordinates of the centroid, length, width and height of a building as well as its principal axis direction, roof type and roof slope. In the case of
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irregularly distributed discrete data points the absolute values of moments depend on the number of data points in the segmented region. Based on binarised data for computing binary moments and their shift and rotation invariant form, ground shape, dimensions and orientation of a building can be determined. Using the height as a weight for computing shift and rotation invariant moments, the roof type as well as height and slope of the roof can be determined. All the building parameters are derived from ratios of moments (e.g., moments normalized by division with zero order moment). After the determination of these seven house model parameters, a goodness of fit can be determined by projecting the house model into the segmented point cloud and computing height residuals for every point. This allows for a rejection of the computed house model in case of bad fit and for the elimination of outliers in the data points. The second approach models dorms also by the analysis of moments. A deviation from the standard gable roof type is formed by dorms on the roof. As the first step, a standard gable roof house model was determined. Next, the differences between the points of the original point cloud and the model were calculated. Points above the ridge height were discarded as potentially lying on chimneys or antennae. Points below the ridge but significantly above the roof were assumed to belong to dorms. This new point cloud was segmented into parts belonging to different dorms by binning and connectivity analysis. For each of the subsets, binarised and height-weighted moments were then computed. As the number of points on the dorms was rather small mostly less than 20, the height was assumed to be constant. The orientation of a dorm can be assumed to be perpendicular to the principal axis of the building. Thus a dorm is described by four parameters: a coordinate and a length along the roof axis, a distance from the roof edge and a height, which can be determined as closed solutions from ratios of moments. Optionally, a fiveparameter dorm with an extra gable may be modeled in the same manner. Also for the determination of house models with a non-rectangular ground plan, the derivation of closed solutions based on higher order moments seems not recommended. Instead, solutions for a number of roof types on a rectangular ground plan should be developed and applied to complex ground plans broken up into primitive units, thus going to prismatic building models. Discussion. Most trees were successfully removed, either during the segmentation process or in the model fit analysis. Three of the 51 buildings could not be modeled due to an insufficient segmentation. The approach for modeling dorms turned out to work quite reliably: all dorms containing a minimum of about 10 data points could be modeled. On buildings with their principal axis oriented not perpendicular to the flying direction of the aircraft, a laser scanner will deliver a higher point density on the roof side which is oriented towards the sensor, thus shifting the reconstructed building in this direction. The magnitude of this effect depends on the opening angle of the scanner, the relative position and orientation of the building with respect to the flight path and the roof slope. These problems will not occur when data interpolated to a regular grid is being used. § Maas and Vosselman (1999) - House Model Reconstruction by Intersection of Planar Faces Most buildings can be described using polyhedral models. That is, planar faces describe the surface of a house roof. Because of the high density of laser scanner data, these faces can be recognised clearly and the parameters of the planes can be estimated accurately. The outlines of the faces of a roof are more difficult to determine. If the roof surface is continuous e.g., there are no height jumps on the roof, the edges between adjacent faces can be reconstructed by intersection of the corresponding planes. Whereas the location of these edges will be quite accurate, the precise locations of edges at roof discontinuities and at the roof outline are difficult to derive from LIDAR data. This data driven approach is proposed for the derivation of polyhedral models of buildings with continuous roof surfaces. In order to improve the determination of the roof outline constraints are imposed on the orientation of the edges of the outline. Data. An FLI-MAP laser scanner dataset covering an urban area of 500 x 250 m2 in the Netherlands was used. The average point density within the laser scanner strips is in the order of 5 points/m2. The original LIDAR points are used. Method. At first, the dataset was segmented by thresholding, binning, morphological filtering and connected component labelling techniques. A further segmentation into individual roof planes is either not required or part of the modeling scheme. The algorithm consists of four steps: (1) Detection of planar roof faces. A 3-D cluster space is described by two slope parameters - sx, sy - and a distance - d. Z = sx X + sy Y + d. (2) •8•
Each laser point (X, Y, Z) defines a plane (Eq. 2) in the cluster space. By counting the number of planes that intersect a bin in the cluster space the planes of the roof can be identified as the bins with a large number of laser points. After the clustering the Delaunay triangulation is used to form connected components of the points in the same plane. If the area of such a component exceeds some threshold, a roof face is considered to be found. To improve the precision of the plane parameters, a least squares fit is performed. (2) Intersection of roof faces. The outline of a roof face consists of two types of straight edges: edges that describe a common part of the outline of two adjacent roof faces and edges that are part of the contour of the whole roof. The first type of edge can be accurately reconstructed by intersection of the detected planes. To find these edges all pairs of detected planes are intersected. After the intersection the edge is defined as that part of the line of intersection for which both roof faces contain laser points that are near that line. In the same manner the edges of the roof faces that are part of the contour of the whole roof can be found by intersection of the roof face with the walls. (3) Determination of roof outlines. From the triangulation and the segmentation of the laser points, the connected component of triangles that belong to the roof surface is extracted. Since polyhedral is assumed to give a better description of buildings, the outline can be found by approximating the contour of this connected component by straight lines. Here, an approximation algorithm was used which strictly enforced the lines to be either parallel or perpendicular to the main building orientation. This orientation was obtained from the directions of the horizontal inter-section lines between the roof faces. These ridgelines are usually parallel to lines of the roof outline. The algorithm to extract the roof outline first sequentially processes all points of the contour of the connected component. Starting at an arbitrary point the direction of the roof edge through this point is derived from the direction to the next point and adjusted using the house orientation modulo 90o. Further points of the contour are assigned to this edge until the perpendicular distance between a point and the edge exceeds some threshold. At this point a new edge is introduced which either makes a left or a right turn with respect to the previous edge. In this way all points are assigned to an edge. The resulting initial approximation corresponds to a segmentation of the contour. This segmentation is optimised by changing the assignments of points to the edges such that the least square sum of the distances of the points to the edges is minimised. For a perfect laser scanner, the best choice would be to determine the outlining polygon such that the majority of points are on the inside of the polygon. (4) Reconstruction of the house model. Vertical wall planes are constructed through all edges of the outline. These wall planes are intersected with the roof faces in order to find the remaining edges of the roof faces and to determine the height of the walls. The complete outline of each roof face is reconstructed by intersecting the edges with nearby end points. Since all edges are in the same plane, there are no misclosures at the intersection point. At points where four or more different planes meet the reconstructed points are averaged to form a common node. Finally, the ground level of the walls was set to the height of the lowest point in the vicinity of the building. Discussion. Forty-three out of 51 buildings in the data set could be reconstructed by intersection of planar faces. Due to the comparatively small amount of object knowledge, the algorithm is less robust than the reconstruction with the moment approach described above. Most problems occurred in the determination of the outline of the building, especially when trees are near the building. § Murakami et al. (1999) - Change Detection of Buildings By using a LIDAR system onboard a helicopter, the study demonstrated that building changes could be detected without omission errors by simply computing a difference image of DSM data acquired at different occasions over the same area. Data. A study area in the city of Minokamo, Japan was selected. It has a variety of typical urban features including tall buildings, a railroad station, and commercial areas mixed with densely populated residential houses and apartments. DSMs of the study area were acquired for four times. Method. Since the DSM data sets acquired with the ALS at different occasions have sufficient spatial accuracy and resolution, the building changes can be automatically detected by simply subtracting one of the DSM data sets from the others. Although the edges of the buildings are present because of the horizontal error of the ALS, it clearly shows the changes of ground features. To help reduce the number of commission errors that need to be removed during a manual inspection process, the difference image was processed with a simple shrinking and expansion filter to remove edges of unchanged features. The amount of shrinking and expansion of the filtering process was determined in an a priori manner based on the horizontal error of the ALS.
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Discussion. Manual interpretation of and comparison between the aerial images taken in February 1996 and December 1998 confirmed that there is no omission error of building changes. As long as no omission error is included in the result of change detection, manual inspection process can be concentrated only on the detected changes, which would reduce the time required to investigate the whole study area. § Kraus and Pfeifer (1998) - Determination of Terrain Models in Wooded Areas This algorithm has been implemented within a program package SCOP, which is used to compute DEMs (only a grid-based model) and to classify the original points into terrain points and vegetation points. In SCOP, the surface is computed by subdividing it into several patches, and a shift parameter is set in an adaptive way for each patch. These patches overlap strongly to avoid discrepancies between them. The number of points in each patch is about 200 for the examples. The number of required iterations ranges from 3 to 4, even for low penetration rates. Data. Laser scanner data are available as a cloud of points. Method. The algorithm is based on linear prediction with an individual accuracy for each measurement. It works iteratively. In the first step, the surface is computed with equal weights for all points. This surface runs in an averaging way between terrain points and vegetation points. The terrain points are more likely to have negative residuals, whereas the vegetation points are more likely to have small negative or positive residuals. These residuals vi are used to compute weights pi for each measurement. The following weight function (Eq. 3) of residuals is used: 1 , vi ≤ g ì ï pi = í1 1 + a (vi − g ) b , g < vi ≤ g + w ï 0 , g + w < vi î
(
(3)
)
The parameters a and b determine the steepness of the weight function and are fixed for the whole area of interest. Deviating from the standard weight function of robust estimation, a shift value g (for laser scanner data, g is negative) was introduced and the left branch of the weight function was set identical to one. By doing so, points with negative residuals smaller than g obtain the maximum weight of one. For large positive residuals bigger than g+w, the weight is set to zero. The value for g can be computed with a histogram of the residuals. Here three different methods are implemented to determine g. They proceed by analysing the histogram of the residuals of the previous iteration from the left (negative branch, negative residuals) to the right and provide candidates for g. Plausibility rules are applied to choose automatically the best value for g. In each iteration, g is recalculated. Then, the weights (pi) are used for the next computation of the surface. Points with large negative residuals have maximum weights and they attract the computed surface, whereas points with medium residuals have smaller weights and less influence on the computed surface. Points with residuals to the right of g+w are eliminated. After computing the surface without these points, the residuals for these measurements can be computed. If a residual is within the specified range, the corresponding measurement will be used again in the next iteration step. Discussion. If the laser beam is reflected from the top or the lower branches of a tree, a measured vegetation point will lie too high with respect to the terrain. This results in an asymmetric error distribution of the laser scanner points. According to the experiences, the penetration rate can be below 25%. For computing a high quality DEM, it is necessary to eliminate all the vegetation points without deleting ground points. Especially in areas with very low penetration rates, this can become difficult.
3.3.2 Multiple Returns Range Data A laser beam can penetrate partly into and possibly through the vegetation cover of the terrain, thus several separately recordable reflections of one pulse may be produced. Some LIDAR systems can differentiate among these multiple returns from the same pulse, the tops of terrain and bare Earth can be mapped simultaneously amid trees, power lines and high vegetation. Recording of multiple echoes can be useful when the vertical profile of multiple objects (e.g., the highest and lowest objects) within the footprint, as in case of trees, is needed. In general, the last return for each pulse represents a point on the ground. Analysis of tree canopy, power-line measurements, line-of-site analysis as well as contouring and digital ortho-photography all can be accomplished using these datasets. In addition, multiple echoes can also happen at the edges of buildings, thus indicating a very fast change in
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elevation. Kraus and Rieger (2000) present a technique to separate wooded from non-wooded areas even in the special case of off-terrain objects (buildings, rocks) that are normally eliminated from the ground model. § Kraus and Rieger (2000) - Automatic Distinguishing between Forested and Non-forested Areas The filtering and interpolation method described above (Kraus and Pfeifer, 1998) allows to automatically distinguish between areas that need to be processed with a skew error distribution (i.e., forested areas) and regions with a symmetric error distribution (i.e., non-forested areas). However, in the areas characterized by many rock needles within the forested areas, the size of those needles often does not exceed the size of single trees, thus the rock points are eliminated along with the vegetation points. To automatically solve this problem, multiple returns are used. Data. Two laser data sets are needed, 1st reflected and last reflected pulse. Grid DEMs were derived with interpolation from the two data sets with 1 x 1 m2 grid size. Method. First, the DEM is derived using the filtering and interpolation method described above (Kraus and Pfeifer, 1998). Next, the laser data is transformed into object elevations by simply subtracting the terrain elevation from the laser data. This is done for both the 1st and last pulse data. Thus it is possible to distinguish between different ground coverages, since the two pulses exhibit different elevation distributions, penetration rates, and roughness parameters both from one another and between different ground coverages. The difference between 1st and last pulses of the same area shows how far the laser pulse may penetrate into the object on a point-to-point base rather than for a whole area. The areas that exhibit difference larger than a threshold of 1 m in the first data set are assumed to be impenetrable for the laser beans, i.e., buildings or rocks. The threshold value is critical, it should further be tuned to the local slope in order to obtain more reliable results. Discussion. The usage of aerial or satellite imagery may be of great help to better distinct between vegetation and non-vegetation. Another promising approach is to collect information about the intensity of the reflected radiance with the laser scanner. The intensity of the reflected radiance in near infrared (810 mm) differs widely between vegetation and non-vegetation, thus distinction is easily possible.
3.3.3 Range and Auxiliary Data The range data provides coordinates and coordinates only. On one hand, this allows fast and highly automated data processing. On the other hand, the interpretability of data is limited due to the fact that no object information is provided because laser scanning is not capable of any direct pointing to particular objects or object features. Other auxiliary data should be used to develop a general post-processing approach, such auxiliary data are intensity image, multi-spectral or hyper-spectral imagery, existing GIS data (such as ground plans, land-use maps and topographic maps). The systematic combination of digital laser and image data will constitute an effective fusion with photogrammetry, from a methodical and technological point of view (Ackermanm, 1999). A similar fusion can be expected by combination with multi-spectral imaging systems. In this way, the integration with photogrammetry would be extended to include remote sensing applications in a wide range. The intensity image can be used for visualization of the surface, also to improve filtering/removal and classification/separation of objects in combination with the range data. Henricson et al. (1996) use information from coloured infrared aerial images to separate elevation blobs detected in a DSM from stereo image matching into the classes buildings and trees. The application of traditional spectral classification techniques to derive surface material information from multi-spectral imagery is presented by Ford et al. (1997). Lemmens et al. (1997) show the fusion of laser altimeter data with a topographical database to derive heights for roof-less cube type building primitives. Haala and Brenner (1997) extract planar roof primitives from dense laser altimetry data (TopoSys sensor, 4 points /m2) by a planar segmentation algorithm, using additional ground plan information for gaining knowledge on topological relations between roof planes. Haala and Anders (1997) demonstrate two approaches aiming on the combination of DSMs, aerial images and existing ground plans for the reconstruction of 3-D buildings. The first approach extracts breaklines from both DSMs and image data. Then the breaklines of high reliability are combined with the gray value edges of high geometric accuracy to reconstruct a rather simple type of buildings. The second approach uses polyhedra as building models to reconstruct very general type of buildings. To do so, It utilizes given ground plans as a priori information and extracts planar surfaces likely to be roof planes from the DSMs. Hug et al. (1997) apply a scanning laser altimeter, which was able
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to measure distance and surface reflectance by processing the return signal energy of the laser beam. Thus, in addition to the range data a reflectance image in the near infrared spectrum is available, enabling the simultaneous usage of geometric and radiometric information for the detection of trees and buildings. They show the detection and segmentation of houses from ScaLARS height and intensity data based on morphological filtering with successive progressive local histogram analysis; in addition, they use the laser reflectivity measure for discerning manmade objects from vegetation via binary classifications. Haala et al. (1998) derive parameters for 3-D CAD models of basic building primitives by least-squares adjustment minimizing the distance between a laser scanning digital surface model and corresponding points on a building primitive; the boundaries of buildings are derived from ground plans. The implementation is limited to four standard building primitives and combinations of those. Further refinement has to be performed interactively. Haala and Brenner (1999) combine multi-spectral information provided by a color-infrared aerial image with geometric information from a laser scanner DSM in one classification step. They also use 2-D ground plan to define the borders of buildings. § Haala and Brenner (1999) - Integrated Classification Combining Range Data and multi-spectral imagery One problem while classifying multi-spectral imagery is the similar reflectance of trees and grass-covered regions. Usually, the same holds true for the distinction of streets and buildings. On the other hand, trees and buildings can be discriminated easily from grass-covered areas or streets using range data, since they are higher than their surroundings, whereas streets and grass-covered regions are at the terrain level. Based on this fact, the basic idea of this approach is to simultaneously use geometric and radiometric information by applying a pixel-based classification for the extraction of buildings, trees and grass-covered areas, whereby the normalized DSM is used as an additional channel in combination with three spectral bands. This classification based on CIR (color infrared) ortho-image and normalized DSM can obtain object classes including shadow, building, tree, grass-covered area and street. Moreover, it can be used to automatically determine the position of trees. Data. Multi-spectral information provided by a color-infrared aerial image is combined with geometric information from a laser scanner DSM. The utilized CIR images of the test site Karlsruhe were taken at a scale of 1:5000 with a normal-angle camera. For digitization, the images were scanned at a resolution of 60 mm, resulting in three digital images in the spectral bands near infrared, red and green at a pixel footprint of 30 cm. Terrain points were measured at approximately one point per 1 m2 with an accuracy of 0.2 meters. The height data and the images were coregistered, i.e., a coloured ortho-image is generated from the original CIR imagery. Method. Based on the DSM, the terrain surface was derived approximately by mathematical morphology using the approach proposed by Weidner and Forstner (1995). This DEM can then be subtracted from the original DSM. Together with the spectral bands the resulting normalized DSM is used as input for the ISODATA (Iterative SelfOrganizing Data Analysis Technique Algorithm). The applied classification detects clusters of pixels in feature space and categorizes the pixels to the clusters based on the minimum distance criterion. With this approach, the optimal number of spectral clusters is automatically determined by iteratively applying split and mergence operations while performing the following steps: (1) Some parameters are initialized by the operator. These parameters are the desired number of clusters, the minimum number of iterations, the minimum number of pixels in a cluster, the minimum standard deviation to initiate cluster split-ting, and the maximum distance in feature space between cluster centres to initiate cluster merging. (2) Each pixel is assigned to one of the predefined clusters by a minimum distance criterion. (3) All clusters containing fewer members than a predefined number of pixels are eliminated. (4) New cluster centres are computed from the pixels assigned in Step 2. (5) Aggregated clusters are split, if the standard deviation is larger than the specified threshold. Neighboring clusters are merged, if the pair-wise distance is smaller than a predefined parameter and if the maximum specified number of clusters has not been reached. (6) Terminated, if the maximum number of iterations is reached, else it is continued with Step 2. For computational reasons only a certain percentage of pixels - for example only the pixels of each 10th row and column - are used for cluster formation in Step 2. After the stop criterion is reached, the detected cluster centres are used to classify the entire image based on the minimum distance criterion. Finally, the feature classes are interpreted and combined to thematic classes or object classes in an interactive step.
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Discussion. The results of the ISODATA algorithm depend on the quality of the input data. If the data in feature space is distributed in almost isolated natural groups, these clusters can be detected very reliably. § Haala and Brenner (1999) - Building Reconstruction using Range Data and 2-D Ground Plan Information Usually, simulations or visualizations require boundary representations of the buildings, which describe spatial objects by their bounding elements, e.g., planar faces. Nodes and edges are defined by intersection of the bounding planes; the topology is additionally captured by a set of relations that indicate how the faces, edges and vertices are connected to each other. Most buildings can be described to sufficient detail in terms of general polyhedra, i.e., their boundaries can be represented by a set of planar surfaces and straight lines. In this approach, additional constraints are obtained for reconstruction by using the assumption that the given ground plan is correct and exactly defines the borders of the roof. The buildings are represented by a combination of one or more basic primitives. Each primitive consists of a cuboid element with a roof, which can be a flat roof, desk roof one inclined plane, gable roof or hip roof. This type of representation corresponds to the well-known CSG (constructive solid geometry) representation used in computational geometry, which combines simple primitives by means of Boolean set operators (union, intersection, subtraction) in order to obtain complex objects. Data. Both laser range data and existing ground plan are used. Method. In the first step, buildings are segmented into basic primitives based on their given outlines, which are automatically decomposed from ground plan. Each rectangle defines the base of one building primitive, which means that position, orientation and horizontal extension of each cuboid are already defined by the parameters of the rectangle. Remaining unknown parameters are the height of the cuboid, the roof type and roof plane slopes. These parameters are then estimated by a least squares adjustment, which minimizes the distances between the DSM surface and the corresponding building primitive, i.e., the building primitives are fit to the DSM surface. Further, the virtual city model is completed by texture mapping of terrestrial images on the building faces. Discussion. The integration of laser data and ground plan was shown to be successful, and detailed reconstruction of buildings can be obtained automatically even for laser data measured at relatively low point densities.
3.4 Summary Airborne LIDAR systems have been in use for many years to measure points on the earth's surface. They can rapidly produce accurate digital surface models and offer significantly lower costs in field operations and post-processing compared to traditional survey methods. This makes the LIDAR technology an attractive alternative for a variety of mapping applications. From scattered 3-D point clouds to useful representations for end-users requires further research and development of post-processing algorithms. Up to now, the post-processing of LIDAR data is still in an early phase of development because no single technique currently is considered optimum or satisfactory for all conditions and requirements. We believe that it is reasonable to merge range data with other auxiliary data in order to develop algorithms to carry the applications of LIDAR technology forward. It is demanding to design and develop new strategies, algorithms, and engineering scenarios in using LIDAR technology for the applications where airborne LIDAR mapping may offer significant advantages. The engineering procedure is preferably automatic, otherwise semi-automatic with necessary human interaction because automatic procedures may fail in extracting information due to the complexity of the tasks and the shortage of information. At present stage, interactive tools for editing the LIDAR data or results may be necessary. For instance, stereo operators can touch up areas where surface estimation algorithms fail and add supplemental break lines and mass points to support final DEMs. Airborne LIDAR technology will certainly continue to proceed technically and to find its new applications. The potential integration with imaging sensors is expected to put airborne data acquisition on a new level in terms of system performance with far reaching prospects.
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4 Methodology and procedures For different types of laser scanning data (i.e., single return range data, multiple returns range data, or range and auxiliary data), some basic techniques (e.g., filtering, smoothing, interpolation, segmentation, classification and modeling) may be integrated to build a set of intelligent post-processing algorithms suitable for main conditions and requirements under a unified framework. We prefer that all data processing is performed on the raw point clouds to avoid errors due to grid interpolation and the final results may be interpolated to regular grids. A highly automated engineering procedure will be proposed. Because of the complexity of this post-processing problem, we will account for some application-specific requirements and parameters, and discover some reasonable assumptions when we obtain some experience in development. In general, the following procedures may be used to accomplish a general processing: 1. 2.
3. 4.
Remove blunder points, which are high up in the air or below the terrain (e.g., points under a bridge or reflected by a bird in the air). Generate DEM by identifying ground points, and classify non-ground points into point classes, such as low vegetation, trees, and buildings, progressively and iteratively. A point is identified as a ground point if the height of the point is not larger than that of the eroded surface at that point. The erosion is performed with filters or weight functions, which may specify the allowed maximum height difference as a function of the distance between two points. To make the classification successful in a data set with various combination of terrain and point density, different discrimination criteria and parameter settings would be used within same area. Do application-specific processing, such as building extraction and breaklines extraction. This may heavily involve manual operations. Inspect and edit resulting data, output results.
The procedure is preferably automatic, otherwise semi-automatic with necessary human interaction. It needs iterations at some steps. Interactive tools for editing the laser range data or results will also be developed to support human knowledge. For instance, stereo operators can touch up areas where post-processing algorithms fail and add supplemental break lines (e.g., ridge, building boundary) and mass points to support final DTMs. Comparison between the results of the proposed algorithms and the ground truth will be done to improve the algorithms by heuristic methods. A prototype software package will be developed. Researchers will be able to test and evaluate the algorithms and give comments on the results over the web.
4.1 Automated Filtering for DEM Generation This task actually distinguishes between terrain and non-terrain points. The algorithm for the derivation of DEMs proposed by Petzold et al. (1999) deserves a try. However, the result is influenced by the final window size and the final threshold below which points are expected to be terrain points. A small window size leads to points on the top of large buildings to be classified as ground points, a fairly large window size smoothes the terrain and removes small discontinuities. A high threshold value that is accepted in the final step leads to many vegetation points classified as ground points, and a small threshold again removes small terrain discontinuities. Therefore, the parameters used depend on the terrain type and thus are different for flat, hilly and mountainous regions. The algorithm needs to be improved to avoid elimination of points that are situated on steep embankments along roads and railways or on steep slopes along ridges. Or the geomorphologic structures are compiled in advance and used in combination with the filtering process to achieve hopefully better results concerning the classification of ground points. To prevent the DEM to appear too smooth compared to the real terrain, break-lines are needed in the DEM. These can be carried out using photogrammetric stereo models or through comparison of the results with large-scale topographic maps. So following improvements would be appropriate: § §
Remove blunder points by the least median of squares estimator (Yang, 1993; Umasuthan and Wallace, 1996). Set a priori parameters to assist processing. • Select terrain type - i.e., flat, hilly, mountainous. Then the allowable maximum angle and distance are determined. These parameters determine how close a point should be to a triangle plane so that the
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§
§ §
§ §
point can be accepted to the DEM. The maximum angle limits the allowable angle between a point, its projection on the triangle plane and the nearest triangle vertex. The maximum distance makes sure that the addition does not jump abruptly when a triangle is large. This helps to keep the low building and vegetation points out of the DEM. In flat terrain, small angle and distance should be used, and larger values are used in mountainous area. • For vegetation or wooded areas, select the terrain cover type - i.e., grass covered, light vegetation, median vegetation, heavily vegetated. Automatic determination of the size of the final window - i.e., the size of the possible biggest building (e.g., 100) - by examining the size of the biggest cluster after a segmentation (Hoover, A. et al., 1996; Jiang et al., 2000) or clustering (Jain et al., 1999; Franti et al., 1997; Franti and Kivijarvi, 2000). If the maximum building size is 100 m, the algorithm will assume that any 100m by 100m area has at least one hit on the ground and that the lowest point is a ground point. Identification of DEM points by automated filtering (Petzold et al., 1999; Kraus and Pfeifer, 1998) or classification (TerraSolid, 1999) combining range and intensity data. The algorithm adds new points to the DEM, and each new point makes the DEM follow ground more closely. Smoothing of the extracted DEM points. • Use the adaptive smoothing (Saint-Marc et al., 1991) or robust smoothing (Umasuthan and Wallace, 1996) on the extracted DEM points to remove redundant small undulations. These need to extend the algorithms from regular grid to irregularly scattered points. • Use the screening of extracted DEM points (Tao et al., 2001) to remove false terrain points that are located on the top of buildings and trees. Interpolation of a bald earth surface using the DEM points. There are many methods are available for interpolation (Mucke, 1995; Zhang and Zhang, 1996). Since all the operations will be conducted on scattered points, a TIN method will be used to do this step. Inspection and editing of the resulting DEM.
With the help of appropriate settings, the algorithms can concentrate on particular cases. The settings are given by operators upon local site knowledge, aerial photography or existing mapping productions. This arises a problem that what will happen when the general settings are given incorrectly (i.e., set for flat terrain in a hilly data set). In addition, the effect of filtering for DEM generation in mountainous areas is suspectable because the existing algorithms are mainly concentrated on the processing of urban data. No specific concerns in the literature are drawn on the mountainous areas except the hierarchical surface filtering method that extracts bald DTMs from DSMs acquired by an airborne SAR system (Tao et al., 2001). The idea of self-diagnosis based on the difference map and the problem map is an approach deserving further discussing when applied to LIDAR range data.
4.2 Building Detection and Reconstruction Buildings are artificial surfaces consisting of continuous surface segments of homogeneous material. Therefore, these segments often have constant reflectance values, are in general relatively smooth, and are bounded by distinct edges, both in reflectivity and geometry. In reflectivity, the spectral signature significantly differs for most construction materials and vegetation surfaces (Hug, 1997). In geometry, buildings appear as compact and regular objects rising above the surrounding surface. Flat polygonal segments (i.e., roofs and walls) limit most of them. Thus most buildings can be roughly described by simple geometric shapes (i.e., triangles, and rectangles) or combinations of them. In contrast, natural objects and vegetation have irregular shapes and structures with many disordered elevation jumps. Both range and intensity data will be integrated to fulfil this task. After the generation of DEM, the building detection actually makes distinct between building points and other non-terrain points. According to Haala and Brenner (1999), the ISODATA is suitable for extraction of buildings, trees and grass-covered areas. The invariant moments used by the derivation of house model parameters (Maas and Vosselman, 1999) are only invariant to translation, rotation and uniform scaling. Moreover, the scaling invariance property was not used. In fact, the LIDAR points blindly shot on the buildings may be non-uniform along the scanning and flying directions due to horizontal positioning error and reflections at the building boundaries. So the moments used in Maas and Vosselman (1999) could be modified to obtain moments that are also invariant to non-
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uniform scaling. Such moments have been formulated by Palaniappan et al. (2000), and could be used to modify the formulae that compute the house model parameters. Some general ideas are given in following: § § § §
§
§
First, subtract the generated bald DEM from raw DSM to obtain difference data, also called normalized DSM (Haala and Brenner, 1999). Set a priori parameters, e.g., minimum area, minimum height, steepest roof angle, smallest gap between adjacent buildings. The minimum height keeps the low vegetation points out. The smallest gap is used to determine the maximum gap between points belonging to the same roof. Apply the synthesized multi-scale edge detection algorithm (Hu, 1999) to extract edges on the intensity image, and compute the contours of buildings by snake method. Building detection. Man-made objects are separated from vegetation and other natural features, then buildings are distinguished from other man-made objects. • Building identification by ISODATA (Haala and Brenner, 1999) if multi-spectral data available, or by fusing of edge points into the point cloud to help locate the positions where strong discontinuity or occlusion may occur (Sun, 1994). Then the possible building points are partitioned into groups separated by edges. The point groups that have areas smaller than a given threshold are discarded. In addition, the edges also can help identify the points hit on the walls or dams. • Building detection and segmentation from range and intensity data by filtering with progressive local histogram analysis (Hug et al., 1997) will be improved by fusion of edge points to help cut a threshold. The threshold is adjusted so that no points belong to the building class would reside at both sides of a contour fitted by edges. Building reconstruction. At this stage, the edges can be used to help accurately locate the positions of the building's walls. • Parametric model by modified moment invariants for non-uniformly scaled data • Prismatic model by 3-D object recognition methods (Besl and Jain, 1985) • Polyhedral model by planar face intersection method (Maas and Vosselman, 1999) • CSG (Construction Solid Geometry) model by composition of above models Inspection and editing of the resulting building models.
The building models can be used by other 3-D visualization systems (e.g., GeoEye 3D) for specific applications.
4.3 Evaluation of Accuracy and Precision The overall accuracy and precision evaluation of DEMs generated from LIDAR DSMs will concentrate on the elevation for different terrain type, vegetation type and the point density. To do this evaluation, preferably, groundtruth data is available. Obviously, the field survey data taken at the same time as the LIDAR projects is most idea. But, this is not realistic. So, most recent photographic mapping productions, existing topographic maps or GIS maps are the main sources of ground-truth data. For the bald DEMs derived from LIDAR DSMs, the following quantities may be used to measure the quality of the DEM data and also the performance of the algorithms and procedures: §
§ § §
Mean deviation. To estimate the discrepancy between the bald DEM data and the ground-truth data, and help adjust the values of related parameters of the algorithms when the algorithms are needed to re-run. For example, when a positive mean deviation is present, we should increase the size of the filtering window (Petzold et al., 1999) or decrease the value of the parameter g (Kraus and Pfeifer, 1998). Maximum deviation. This can help identify the blunder points not removed at the most beginning. Standard deviation. It estimates the uncertainty of introduced by the algorithms following the elimination of the systematic bias of the algorithms. RMSE. The root mean squares error estimates the overall accuracy of the bald DEM data.
These quantities may be used to improve the algorithms and procedures. One quality index of the algorithms may be the difference between the precision of bald DEM data and that of the raw LIDAR data. In addition, The
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overall evaluation needs to take into account the accuracy/precision of the ground-truth data and the original LIDAR data themselves. Specially, in the case of building reconstruction by house model, after the computation of model parameters, the goodness-of-fit can be determined by projecting the building models into the corresponding point groups and computing the height residuals for the points. Then the metrics listed above can be computed and this allows for a rejection of a model if it is found to be a bad fit. If the model is accepted, the model could be used to further identify blunders in the point clouds or to judge the result of the blunder removal at the beginning. Last, the algorithms may be run again for refining purpose. The discussion above focuses on the elevation accuracy only. The horizontal accuracy may be simply addressed similarly when the ground plan data of buildings is available.
5 Outline of thesis contents Abstract Introduction Literature Review Airborne LIDAR technology LIDAR mapping applications Post-processing algorithms LIDAR Data Pre-processing Blunders removal Mesh or TIN construction Automated DEM Generation by Filtering General strategy Algorithm design and analysis Experimental results and analysis Building Detection and Reconstruction General strategy Algorithm design and analysis Experimental results and analysis Conclusions and Recommendations References
6 Proposed schedule Tasks Preparative reading and thesis proposal Candidacy exam Research works Theory preparation and algorithm design Algorithm implementation Experimental results analysis and algorithm analysis Algorithm improvement Thesis composition Literature review • 17 •
Duration Jan. ~ Feb. 2001 Mar. 2001 Apr. 2001 ~ Mar. 2002 Apr. ~ Aug. Aug. ~ Nov. Dec. Jan. ~ Mar. Feb. 2002 ~ May 2002 Feb.
Theory and algorithm Experimental results Introduction, conclusion and proofreading Final oral exam Final modification
Mar. Apr. May Jul. 2002 Aug. 2002
7 Bibliography and references Ackermanm, F. (1999). Airborne laser scanning - present status and future expectations. ISPRS Journal of Photogrammetry and Remote Sensing, 54: 64-7. Airbornelasermapping.com (2000). The skinny on airborne laser mapping. http://www.airbornelasermapping.com/. Arman, F., Aggarwal, J.K. (1993). Model-based object recognition in dense-range images - a review. ACM CS, 25(1): 5-43. Axelsson, P. (1999). Processing of laser scanner data - algorithms and applications. ISPRS JPRS, 54: 138-147. Baltsavias, E.P., Mason, S., Stallmann, D. (1995). Use of DTMs/DSMs and orthoimages to support building extraction. In: Gruen A., Kubler O., Agouris P. (1995). Automatic Extraction of Manmade Objects from Arial and Space Images. Basel, pp. 199-210. Baltsavias, E.P. (1999a). Airborne laser scanning: existing systems and firms and other resources. ISPRS JPRS, 54: 164-98. Baltsavias, E.P. (1999b). Airborne laser scanning - relations and formulas. ISPRS JPRS, 54: 199-214. Besl, P.J., Jain, R.C. (1985). Three-dimensional object recognition. ACM CS, 17(1): 75-145. Brunn, A. and Weidner, U. (1998). Hierarchical Bayesian nets for building extraction using dense digital surface models. ISPRS JPRS, 53: 296-307. DeLoach, S. (2001). Photogrammetry: a revolution in technology. http://airbornelasermapping.com/. Ford, S. et al. (1997). Preliminary results on the analysis of HYDICE data for information fusion in cartographic feature extraction. In: SPIE Conference on Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, Orlando, pp. 67-86. Franti, P. et al. (1997). Genetic algorithms for large scale cluttering problems. The Computer Journal, 40(9): 547554. Franti, P., Kivijarvi, J. (2000). Randomised local search algorithm for the clustering problem. Pattern Recognition and Applications, 3: 358-369. Haala, N., Cramer, M., Kilian, J. (1996). Sensor fusion for airborne 3-D data capture. In: 2nd International Airborne Remote Sensing Conference and Exhibition, San Francisco, pp. 344-353 Haala, N., Anders, K.H. (1997). Acquisition of 3-D urban models by analysis of aerial images, digital surface models and existing 2-D building information. In: SPIE Conference on Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, Orlando, pp. 212-222 Haala, N., Brenner, C. (1997). Generation of 3-D city models from airborne laser scanning data. In: EARSEL Workshop on LIDAR Remote sensing on Land and Sea, Tallinn, pp. 105-112. Haala, N., Brenner, C., Anders, K.H. (1998). 3-D urban GIS from laser altimeter and 2-D map data. IAPRS, vol. 32, pp. 339-346. Haala N., Brenner C., (1999). Extraction of buildings and trees in urban environments. ISPRS JPRS, 54: 130-7. Henricson O. et al. (1996). Project Amobe: strategies, current status and future work. IAPRS, vol. 31, 321-330. Hill, J.M., Graham, L.A., Henry, R.J. (2000). Wide-area topographic mapping and applications using light detection and ranging (LIDAR) technology. Photogrammetric Engineering and Remote Sensing, 908-14. Hoover, A. et al. (1996). An experimental comparison of range image segmentation algorithms. IEEE T-PAMI, 18(7): 673-689. Hu, Y. (1999). Target Localization through Point-set Template Matching Techniques (in Chinese). SJTU. Hug, C. (1997). Extracting artificial surface objects from airborne laser scanner data. In: Gruen, A., Baltsavias, E.P., Henricson, O. (1997). Automatic Extraction of Manmade Objects from Arial and Space Images. Basel, pp. 203212. Hug, C., Wehr, A. (1997). Detecting and identifying topographic objects in imaging laser altimeter data. IAPRS, vol. 32, pp. 19-26. Jain A.K., Murty, M.N., Flynn P.J. (1999). Data cluttering: a review. ACM CS, 31(3): 264-323. • 18 •
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