Geometry and intensity based culvert detection in

Geometry and intensity based culvert detection in mobile laser scanning point clouds

Yi Lin Juha Hyyppä

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Journal of Applied Remote Sensing, Vol. 4, 043553 (1 November 2010)

Geometry and intensity based culvert detection in mobile laser scanning point clouds Yi Lin and Juha Hyyppä Finnish Geodetic Institute, Department of Remote Sensing and Photogrammetry, Geodeetinrinne 2, Masala, Kirkkonummi 02431 Finland [email protected] Abstract. Mobile laser scanning (MLS), which recently has been developing so quickly as a promising technology for mapping and remote sensing (RS), offers a good means to measure the fundamental geographic data, e.g. culverts, for urban planning and road engineering. This study as the first try presents a new automatic method to detect culverts in MLS point clouds, in which actually only partial characterization of this category of objects can be presented due to the restricted scanning zenith of MLS. The schematic is based on the raster-form of the data, and the digital terrain models (DTMs) with multi-leveled resolutions are first yielded by local minimum filtering. Then, the common layout of the expanded areas containing culverts is generalized as the theoretical basis, and the schematic components are derived to deploy the concrete judgment. The geometry and intensity information about culverts are both utilized to determine the real locations from coarse- to fine-scales. Numerical analysis based on the real-measured MLS data at the Espoonlahti test site has basically validated the proposed approach. Concretely, the statistical errors of the retrieved lengths and widths of the pedestrian culverts are less than 9% and 16% compared to the real ones individually, notwithstanding the inner heights innately in-accessible. Keywords: culvert detection, MLS, geometry, intensity.

1 INTRODUCTION Mobile laser scanning (MLS), technically parallel to airborne laser scanning (ALS) as well as terrestrial laser scanning (TLS), in recent several years has been developing quickly as a new research focus [1]. As opposed to ALS with lower sampling density and TLS in subject to laborious relocation, MLS with higher sampling frequency and flexible mobility is more appropriate for delicately and efficiently surveying, mapping and remote sensing (RS) urban and road environments. MLS-collected point clouds have been explored in terms of a variety of applications, e.g. road surface modeling [2], vertical wall extracting [3], and foliage growth monitoring [1]. Automatic detection of the bridge-shaped objects, e.g. flyovers, culverts, and ramps in MLS data, however, has been paid less attention compared to the relevant ALSbased work [4], and the associated extraction approaches based on MLS data are almost in blank as far as we know. In fact, this category of entities is essential for surveying as the fundamental geographical data for urban planning and road management. Therefore, how to automatically detect the bridge-shaped objects in giant 3D point clouds can be deemed to be an important study branch for extending MLS applications. As the typical artificial objects of interest, bridge detection based on diverse RS metrics has long been studied in retrospect. Various satellite optical images based researches have been deployed, and the specific methods refer to visual interpretation of the panchromatic images [5], GIS assisted detection [6], multi-properties based exploitation of the multispectral images [7], and etc. The high spatial-resolution satellite images further promoted this field in progress [8-11]. Moreover, with the multi-competences of fine resolution and active imaging, synthetic aperture radars (SARs) have also been employed for bridge extraction [12]. The latest reports introduce, e.g. multi-aspect high-resolution interferometric SAR data based

© 2010 Society of Photo-Optical Instrumentation Engineers [DOI: 10.1117/1.3518442] Received 1 Jun 2010; accepted 27 Oct 2010; published 1 Nov 2010 [CCC: 19313195/2010/$25.00] Journal of Applied Remote Sensing, Vol. 4, 043553 (2010) Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 1

bridge detection [13], and space-borne and airborne SAR data processing method [14]. SAR data integrated with optical images have also been explored in [15, 16]. These methods based on the traditional RS apparatuses theoretically are all involved in how to examine bridges from the top view images, with the associated ground locations as the focuses. As another state-of-the-art surveying technology, ALS also known as light detection and ranging (LiDAR) can provide accurate geo-referenced point cloud datasets to represent the objects on earth surfaces, and bridges can be delineated by grouping the related echoes with definite 3D coordinates. Thus, ALS technique has been much exploited for bridge detection. Actually, the representative work of bridge extraction based on ALS data is referable for composing new methods proper for MLS samplings, in terms of their similar data structures of points. The classifier cascade based algorithm [17] applied a series of basic criteria to tag bridges. The profile-geometry based detection [4] took advantage of the topology of the orthogonal cross lines to identify the bridge-echoed points, and the complex bridges with interchanges or flyovers could also be distinguished. The road detection based algorithm [18] applied the planar property of road surfaces, and then multi-gradients and local minima were tackled to determine the possible bridges. As to TLS, the relevant research emphases on target detection lie more on how to acquire structural parameters for geometric modeling after infield investigations, e.g. for shape-variation monitoring [19]. Although MLS seemly occurred as the result of a simple assemblage, namely mounting TLS on mobile platforms, the relevant applications based on MLS data have been widely extended [1, 2, 20], of course involved in the bridge-like objects. The MLS-sensed contexts for bridge-family detection have too many differences from RS images and even from ALS metrics. During vehicles moving, the received points reflected from bridges, flyovers, ramps and culverts are mixed in the 3D point corridors along roads. The over-crossing bridges such as flyovers can be recognized by plane extraction with the criteria of e.g. the higher altitudes and the non-parallel directions compared to the roads. Nonetheless, for the culverts undercrossing roads, no matter draining culverts or pedestrian culverts, their entrances cannot be fully sampled in most cases, since the zenith angles of MLS pulses are generally in a limited range (see Fig. 4). This situation makes it harder to locate culverts, and this paper mainly discusses about this issue, namely how to detect culverts with incomplete coverage in MLS point clouds. Thus, the objective of this study was to develop an automatic method for culvert detection in MLS point clouds. Intuitively, the expanded areas containing culverts, as a composition of multi-plots e.g. pedestrian roads, slopes and entrances, can exhibit a common layout with a variety of spatial features, and the peculiar combination of these features can somehow be counted on as the theoretical basis. First, the scanning model of MLS measurement from side view was retrieved systematically, and the schematic components for uniquely embodying culverts were summarized. Next, digital terrain models (DTMs) were generated from MLS point corridors to reduce the shading influences of trees and vegetations, and the possible locations of culverts were coarsely estimated after blank zone searching. Then, the false blank zones not related with culverts were removed based on the following step of profile analysis. Last, segmentation of all the topological plots, also based on intensity information, was deployed to determine the real culverts, and the geometric parameters were obtained by measuring the schematic components. The new method was finally tested based on the real MLS-measured data, and the associated performances were also assessed.

2 MATERIALS 2.1 MLS system MLS systems comprising laser scanners of high-performance on penetrating through canopy are ideal for this study, which can help reflect culverts more. Sensei MLS system (see Fig. 1)

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 2

was applied as such a modular system, which was developed at the Finnish Geodetic Institute (FGI). Sensei comprises two different laser scanners, a GPS/IMU positioning system, a CCD camera, a spectrometer and a thermal camera. Different operation modes can be optionally adopted, e.g. only laser scanning or laser scanning combined with hyperspectral imaging. The study solely employed the georeferenced point clouds with four variables of 3D coordinates and intensity, which were output after combining the records of an Ibeo LUX laser scanner and a GPS/IMU position/attitude system. The system theoretically is able to measure up to 38000 points/second if only one return per pulse per layer is assumed. Actually, maximum three echoes per pulse can be recorded, which can penetrate the crowns more or less. The sampling density keeps decreasing from about 40 points/m2 till zero, and trees can make some local densities up to thousands of points/m2. The distance-measurable range of Sensei is from 0.3 to 200 m (50m-targets with 10% remission), and the ranging accuracy in average is 10 cm. The divergences of laser beams are 0.08° with respect to the scanner axes as references. These setups were appropriate for supplying the basic data to validate the new automatic method for culvert detection.

Fig. 1. The Sensei MLS system mounted on a car. For this study, only the Ibeo LUX laser scanner and the GPS/IMU positioning system were assembled.

2.2 Study area and data The data for numerical analysis was collected in the Espoonlahti test site of Finland on May 6, 2009, which also served as the basic data for the cooperative European Spatial Data Research (EuroSDR) experiments. EuroSDR is an organization that undertakes collaborative applied research projects, which help contribute to the development of international specifications and standards. Two typical streets with the under-crossing pedestrian culverts were chosen as the study areas (see Fig. 2). The main roads as well as the pedestrian roads both have the cement-materialized surfaces, and are surrounded by meadows and trees. The corresponding MLS-measured point corridors were collected (Fig. 3), and the spatial distributions of the objects in the scenarios were characterized explicitly. The two scenes are both represented by hundreds of thousands of echo points, and automatic extraction of culverts directly from these 3D point clouds turns out to be a time-consuming and complicated task. The aforementioned layouts after artificial interpretation did emerge. The spatial morphologies can also be exploited, e.g. the main roads casually with shrunk widths over culverts. The trees close to culverts have a certain effect of shading, and this may add more difficulties for testing the applicability of the new method. Therefore, the study areas

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 3

with MLS samplings and field-measurements can be applied as the typical fundamental data for experiments.

(a)

(b)

Fig. 2. The airborne RS images of the two study areas. It can be learnt that trees shade the culverts to some extent. (©2010 Microsoft Corporation, ©2010 Blom)

(a)

(b)

Fig. 3. Demonstrations of the Sensei-acquired point clouds. It can be interpreted that the reconstructed scenes present the explicit characterizations of the two streets.

3 METHODS 3.1 Scanning model analysis Laser scanners on mobile platforms usually conduct surveying by consecutively scanning the parallel profiles from side view, and the movement of vehicles helps to integrate the resulted parallel profiles into 3D representations of road environments. Thus the theoretical scanning model associated with MLS-based culvert detection is analyzed systematically based on this kind of mapping mechanism, and the brief diagram can be achieved (see Fig. 4). The flyovers above roads can be detected by seeking the plane-distributed points echoed from their lower surfaces. As to culverts, however, few echoes directly returned from the entrances of culverts can be received, and consequently blank zones are formed. This phenomenon is caused by the fact that MLS laser scanners generally emit pulses with zeniths less than 180°, and natural or

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 4

artificial altitude differences are necessary for the main roads to overpass the lower pedestrian roads. These two factors result into the scanned culverts mostly with only parts of the sided slopes and the extended pedestrian roads, and thus culverts have to be predicted from the surrounding point sets. Hence, the associated schematic components uniquely characterizing the layouts of culvers need to be extracted and summarized as the algorithmic foundation of culvert detection. Pulse

Zenith

Flyover MLS Road

Culvert

Fig. 4. The generalized scanning model for MLS-based culvert detection. The flyovers above the main roads can be steadily detected out, while recognizing culverts needs more studies due to the phenomenon of blind zones existing.

3.2 Schematic components derivation The common layout of the expanded areas containing culverts is firstly retrieved to establish the judgment basis. After synthesis of the real scenes (see Fig. 2), the distributions of points (see Fig. 3) and the brief model (see Fig. 4), the targeted areas can be divided into three symmetric rectangles with different properties. The first central rectangle covering the main road has dense points at the scanning side, and the second one occupies almost no points. The points in the third rectangle can construct a local flat plane in geometry, and also display consistency in intensity decreasing from near to far. The profiles of the three rectangles are also distributed in a regular arrangement, namely a higher line segment, a gap and a lower line segment, and this trends of variation can be exploited as the particular feature of culverts for further confirmation. Top view

Moving Direction

Blank Zone Main Road

Pedestrian Road

Profile Fig. 5. The schematic components reflecting the geometry and intensity characteristics of culverts during MLS surveying.

The schematic components indicating culverts are derived (see Fig. 5), which comprises the plots related with the main road surfaces, the blank zones and the pedestrian road

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 5

surfaces. The trapezoidal topology of their profiles is also derived, and there is a random distance horizontally and vertically between the two categories of line segments. The concrete sizes of these components depend on the altitude differences and the practical setups of MLS systems. The vertical projections of the segmented results can estimate the lengths and widths of culverts, while profile analysis can give the altitude differences. For the inner heights of culverts between the pedestrian road surfaces and the lower surfaces of the main roads, there is still no ideal solution, since the inner structures or even the upper edges of culverts in the blank zones cannot be sensed by MLS.

3.3 MLS-based culvert recognition Although the schematic components have been generalized, the specific detection process still encounters so many restrictions, e.g. the huge amount of points in the long road corridors and the messy mix-ups of the interfering objects. Sometimes a foliage-flourishing tree even may prevent the pedestrian road on one side from sensible. Therefore, DTMs which can embody the true morphological distributions of earth surfaces and road infrastructures are utilized as the basic data. For efficiency improving, DTMs with a lower spatial resolution will be firstly yielded to obtain the possible locations of culverts.

3.3.1 DTM-based coarse-locating The automatic production of DTMs is performed by the ground rasterization of point clouds in two levels. First, DTMs with the horizontally rastering unit of 1 m are yielded in order to reduce the calculation workload within the goal areas, and the symmetric blank zones can be sought to give the possible locations of culverts. Second, the expanded rectangles separately covering the resulted locations are further rastered with a finer resolution of 10 cm, and the schematic components can be segmented based on the more detailed geometry and intensity properties. Then the combination of the achieved components can be checked to verify the final locations of culverts. DTMs can convert the messy 3D data processing into 2D, and with the determined locations of culverts the rastering unit can be refined further to parameterize the accurate models of culverts. The concrete procedures of DTM generating are based on the lowest point seeking in each raster, and the resulted heights are assumed as the altitudes of the new rasters, which can reduce the influences of trees and vegetations to some extent. To avoid the disturbances of points with gross errors, the minimum number of points in each raster is restricted, and the unqualified rasters can be classified into the blank zones. Given the horizontal density of MLS data mostly larger than tens or even hundreds of points/m2, the thresholding number can be valued with, e.g. 3. Actually, the plots with a large number of blank rasters and with large distances from the main roads can be omitted for efficiency, and this is based on the fact that culverts are right under the main roads.

3.3.2 Component segmentation After the expanded rectangles possibly containing culverts are solved by coarse locating, the schematic components can be segmented respectively based on the DTMs in the second level. The concrete procedures are to divide the rectangles into several plots with different terrain properties, and the arrangements of these entities can be utilized to verify culverts eventually. Actually, the spatial distributions of the points are not uniform in the local rectangles as demonstrated by the theoretical diagram (see Fig. 6), and the position-fluctuations of points on the edges sometimes may make the schematic components unrecognizable. Luckily, the directions of the main roads can be attained preliminarily according to the outputs of MLS systems, namely the direction of AC achievable in advance. Thus, the work of component segmentation is transformed into fitting the scattered points on the edges into parallel lines

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 6

(e.g. edge EF) and orthogonal lines (e.g. edges AB and CD), and implementing this process is easier than local plane segmentation. For overcoming the gross errors e.g. locations with great shifts, the robust line fitting method [21] can be applied. Top view

Blank Zone

A’

Slope

A E

C

F

Main Road

B

Pedestrian Road

D

Slope

Fig. 6. The concise geometric diagram regularized for component segmentation. The concrete operation of segmentation is fulfilled by line extraction, e.g. of edge EF.

3.3.3 Intensity-gradient based segmentation The edges AB and CD cannot be precisely determined just based on the blank zones, and in reality EB maybe is not in the same line with AE (see Fig. 6). Thus, the edges between the slopes and the pedestrian roads must be reexamined by exploring more other attributes. As we know, the pedestrian roads generally have the reflectance-consistent surfaces, while the slopes filled with diverse objects e.g. grasses, bushes, and gravels may behave inversely. Hence, intensity variation is used as a criterion to distinguish the slopes from the pedestrian roads. For the slopes, the intensity-gradients with radiuses increasing exhibit no regular rules. For the pedestrian roads, the intensity-gradients along the direction of the main roads remain about zero, while the gradients across the moving direction will be similar with the rule of laser attenuation with distance adding. The two higher-resolution images, formed with the gradients as the raster values on the two orthogonal orientations individually, are processed to segment the different plots. The procedure starts from the centers of the blank zones, and expansions on both sides are carried out along the orthogonal lines passing the centers of the blank zones. If the difference between two neighboring rasters is larger than the given threshold, the two rasters are marked as the possible edge-points and the related expansion stops. After all the edge-points are found, line fitting is implemented to acquire the lines of EB, FD, EF, and BD.

3.3.4 Multi-criteria based determination The determination of culverts is based on a multi-criteria combined plan. The symmetry of the components, the planarity of the road surfaces, and the topology of the line segments in profiles are the kernel criteria for verifying culverts. If the three criteria are all satisfied, the top view area as Fig. 5 can be determined to be a culvert. The widths of culverts are same to the lengths of AC, and the lengths are valued with the distances of AA’. The bottom surfaces of the culverts beneath the main roads can be derived by interpolating the two extended pedestrian road surfaces. There exist other specialties capable of strengthening the fidelity of culvert detection. The parapets on both sides of the main roads can be added as an important type of signs, and seeking these protecting walls along roads may serve as an ancillary measure to verify culverts. The shrinkage of road widths are casual phenomena when over-

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 7

crossing culverts, and observing width variations during moving also can supply a means to initially verify the possible rectangles of culverts.

4 RESULTS AND DISCUSSIONS 4.1 Coarse-locating After DTMs with ground resolution of 1 m were yielded, the relevant height-transformed gray images could be applied to seek the coarse locations of culverts. The results showed that some pixels higher than the earth surfaces are mixed in the blank zones (see Fig. 7), since the referred DTM-producing approach cannot eliminate the influences of tree shading completely. The related top-view images, however, still present the implicit clues to reveal the underlying culverts. The blank zones near to the entrances of culverts on both sides show up obviously, and they can be utilized to obtain the rectangles possibly containing culverts. Some blank zones non-involved in culverts also emerge, which are probably triggered by the shading of crown-dense trees, e.g. pines. Besides the large blocks with distinguishable areas, many isolated blank rasters exist as well, which are the natural results of sampling instability and shading. Smooth filtering thus was performed on the DTM images to delete the isolated blank rasters, and the theoretic fundamental was based on the common sense that all the diameters of the pedestrian culverts are generally larger than 1 m.

(a)

(b)

Fig. 7. DTMs generated with horizontal resolution of 1 m. The black color signifies the lowest points while the white color indicates the highest altitude.

With the knowledge of vehicle moving direction based on the output parameters of MLS systems, the central lines of the main roads could be solved and the distant sampling points out of the reasonable ranges were removed. Next, the remaining points were grouped into different blank zones, and the rectangle tightly containing each blank zone with the parallel or orthogonal edges compared to the moving direction was constructed. The symmetrical rectangle of each zone was projected across the central line, and the situation of overlaying was checked. If the opposite rectangle has no overlaying with any an original blank zone, the associated blank zone can be considered as the false entrances and deleted; otherwise, the two symmetrical groups with overlaying can be retained. The results manifest (see Fig. 8) that there are still false blank zones mixed in the results.

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 8

After comparison with the top view image (see Fig. 2(b)), it can be stated that the two lower blank zones (see Fig. 8(b)) are generated as results of tree shading. Simultaneously, the central blank zone is also a false one, which partly overlays with the projected result of a true blank zone. Apart from these causes, the false blank zones can also be motivated by e.g. the sharp-changing slopes. Therefore, DTMs of the expanded rectangles covering the blank zones were segmented out (see Fig. 9), in which the ground resolution is set as 10 cm in terms of Sensei ranging accuracy of just 10 cm. Actually, the related digital surface models can be applied alternatively, as the existence of trees here can serve as an effective index to exclude the false blank zones. Later, the profiles of the culvert-relevant regions were analyzed, and the correspondences between the line segments of the real blocks and the schematic arrangements (see Fig. 5) were compared.

(a)

(b)

Fig. 8. The blank zone extraction results based on DTMs with ground resolution of 1 m. The black blocks denote the blank zones corresponding to culverts, while the gray blocks mean the disturbing ones. The straight line indicates the central line of the main roads.

(a)

(b)

Fig. 9. The point distributions of the expanded rectangles surrounding the blank zones extracted in Fig. 8. It is obvious that there are false blank zones in (b).

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 9

4.2 Fine-scale determination The procedure of fine-scale determination involves false blank zones removal and schematic components division. Since the topographies of slope zones on both sides of pedestrian roads are not necessarily with uniform signatures, e.g. with either possibility of higher or lower than the pedestrian road surfaces, the altitude attribute alone sometimes is not enough for the EBindicated boundary extraction. The intensity distribution around the culvert shown in Fig. 2(a) is illustrated in Fig. 10(a), from which it can be learnt that the intensity texture of pedestrian road surface is distinguished from its both sides. The sequential values of two parallel profiles perpendicular to the main road on the right side are extracted and listed in Fig. 10(b), in which the zero-valued samples within the 1m-gridded rasters can be omitted. From statistics, the variations of height and intensity variances in each perpendicular profiles are assumed as the criteria to solve the locations of EB and FD. The transect profile moves from the central one of each possible rectangle acquired as in Fig. 9. If the changes of the height and intensity variances are larger than 20% simultaneously, the boundaries can be determined. At least, the intensity variances change more than 20%, the yielded EB and FD can be kept as the possible locations. The blank zones acquired in Fig. 8 can further refine the results.

(a)

(b)

Fig. 10. The intensity-valued image of the target culvert (a) and the intensity profiles on both sides of EB corresponded in Fig. 6.

The profiles of culverts across the main roads behave uniformly as stair-like trapezoidal morphologies with heights going-down, while the blank zones yielded by tree-shading may cause the profiles with permanent peaks, not in regular line segments. After profiles matching (compared with Fig. 5), the rectangles associated with the false blank zones were expelled, and the remaining rectangles were acquired with the intensity amplitudes converted to gray levels (see Fig. 11). Based on the aforementioned analyses about intensity, the pedestrian roads as the extensions of culverts can be used as the kernel factors to give the final confirmation of culverts. The "clean" planar strips fitting to the pedestrian road surfaces and the orthogonal "clean" planar strips modeling the main road surfaces can be used to determine the real culverts, and the widths of culverts can be sought by counting on the width of the component strips. The final results with four lines fitting the ground-projected edges to segment the culverts were achieved (Fig. 12). The lengths and widths of the objected culverts can be calculated, and the simplified geometric models can also be established somehow. The widths of culverts are less than the widths of the relevant blank zones, and are larger than the widths of the

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 10

under-crossing pedestrian roads. The width-related parallel lines were actually sought as the compromised results of these two widths, by thresholding the point numbers met by the gradually expanding lines. The compromised plan was assumed due to the sampling density of Sensei, less than 0.4 points/dm2 in level II, and this was not enough to fully embody the surroundings of culverts. MLS systems with higher density e.g. ROAMER [22] will be tried in the future to improve the results of parameterization.

(a)

(b)

Fig. 11. The component segmentations of the intensity distributions rastered with ground resolution of 10 cm, after the false blank zones removed from Fig. 9.

(a)

(b)

Fig. 12. The resulted boundaries of culverts. The darker gray parallel lines draw out the edges of the main roads and the culverts respectively, and the lengths and widths of culverts then can be acquired.

4.3 Numerical analysis The parameter estimation (Table 1) and the runtime statistics (Table 2) were deployed for assessing the accuracy and efficiency of the new method individually. The errors of the

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 11

estimated lengths and widths compared to the real ones are respectively less than 9% and 16%. The variable of height in Table 1 means the inner height of any a culvert, namely the altitude difference between the pedestrian road surface and the lower surface of the main road. This variable is hard to be retrieved, as MLS samplings cannot supply the detailed information to reveal the thicknesses of the main roads. Table 1. The structural parameters estimated by the new method. The incapability of inner height estimation still sources from the phenomenon of blank zone existing.

Culvert1 Culvert2

Length (m) Real Resulted 8.65 7.86 9.00 8.72

Width (m) Real Resulted 7.56 6.35 7.02 7.43

Height (m) Real Resulted 3.50 3.35 -

The multilevel detection mode from coarse-locating to fine-determination can enhance the efficiency greatly. The total numbers of the points for processing were reduced by an order of magnitude for both the two study areas after the first level, and the runtimes were also reduced greatly. Extraction of the rectangles possibly containing culverts in the giant point clouds wasted the majority of the operation time. Actually, if detection is implemented directly based on the rastered data with the ground resolution of 10 cm, the runtime will be far more than the sums of the two runtimes listed in Table 2. The workflow assumed in this study is extensively helpful for processing MLS point clouds in such great volumes. Table 2. Statistics of point numbers and runtimes associated with the two culverts. The running environment is as follows: Intel(R) Core(TM) 2, Duo CPU E8500 with 3.16GHz, 2.43 GB of RAM, Microsoft Windows XP Professional V2002, and Matlab R2009b.

Culvert1 Culvert2

Rectangles possibly containing culverts 2 5

Number of points CoarseFinelocating determination

Runtime (s) Coarse- Finelocating determination

245072 321388

14.11 92.69

31358 80393

0.068 0.074

4.4 Discussions The performances of level I (Table 2) virtually are far from satisfying the requirements of engineering applications, and efficiency is still an ill factor for promoting this function of MLS into practices. The situation will substantially get worse when MLS surveys data at the scales of kilometers as expected. To solve this problem, the skeleton-typed information of the point corridors needs to be preliminarily acquired, as the premises to extract the goal areas like the local point clouds shown in Fig. 3. Then the new method introduced in this paper can be run on each goal area to realize a wholly better efficiency. The goal area extraction approach is designed based on the picked-up parallel profiles with spacing of 2 m, which in common sense can basically ensure no omission of culverts. The hundreds of points in profiles after terrain filtering and ground rastering maybe are reduced into tens of rasters, or even just several rasters. The topological relationships between the fitted line segments (see Fig. 5) are explored to seek the goal areas, and this preprocess can retain the efficiency of culvert detection for long distances of scanning. Another question about the symmetrical assumption about blank zones is necessary to be discussed. The real contexts of culverts vary diversely, and the schematic components arranged in unsymmetrical morphologies are also casual. Thus, the blank zones as the basis of

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 12

coarse-locating need to refer to the relevant altitude information, while not just based on the horizontal projections. Besides, judgment of the symmetrical statuses can be undertaken by adding an allowable rotating range to AB (see Fig. 6), which can somehow overcome the influences of the pedestrian roads not orthogonal with the main roads. The problems of rasterization can degrade the final accuracy of parameter estimation, and the puzzles of valuing each raster exist in the two processing levels. The issues can be derived into two aspects: which value is appropriate for a raster with multi-points inside, and how to value a raster without any a point inside. The terrain filtering methods for DTM generation based on ALS data has been summarized by Sithole [23], and the relevant methods can be referred to process MLS point clouds. As to intensity rastering, the simple principle of seeking the lowest point in each raster to represent the terrain surfaces becomes invalid, since the minimum/maximum intensity probably is not able to reflect the dominant properties of the objects in each raster. More rules need to be explored to acquire more reliable reference information. Calibration of echo intensity, e.g. for different objects, angles and distances, will be the study emphasis in the future. Besides as an independent means of mapping, MLS can also work as a supplementary mode when ALS surveying at the region-wise or stand-wise scales. The several points/m2 densities of current ALS systems may restrict the collected data from some applications, e.g. modeling the terrain surfaces under canopies, while MLS can receive the associated echoes through the spaces between crowns and terrain surfaces. ALS can also serve as a settlement for locating the goal areas during MLS implementing large-scale measurements. Moreover, the alternative stop-and-go scanning mode can help MLS to in-field investigate the objected plots, e.g. the areas containing the detected culverts after this work, and to yield the more accurate geometric models with definite coordinate information. This function is as same as classical TLS systems. Based on these advantages, MLS can be applied as an accurate and flexible technique for more extensive applications, and the performances will be further improved with the aforementioned issues solved.

5 CONCLUSION In this study, a new automatic method for culvert detection based on the state-of-the-art MLS mapping technology has been established and basically validated aiming at the pedestrian culverts. The context of MLS surveying culverts was firstly analyzed, and the fact of MLS data unable to give complete coverage of culverts was found. Therefore, the proposed method completed the detection by analyzing the common layout of the expanded areas containing culverts, and by checking the structural similarity between the real distributions and the derived schematic components. The two-level processing mode saved the runtime indeed, and the combination usage of geometry and intensity information provided more accurate results. If the in-field data measured by the same MLS system under the stop-and-go mode is fused, the accurate 3D geometric models of the objected culverts can be built. As to the draining culverts, the reflectance of water has even more special characteristics than the pedestrian road surfaces, and the same workflow can be applied for detecting this category of culverts. From the literature review, it can be concluded that the work introduced in this paper serves as an effective pioneering strive in this research branch, and the attribution can help to extend the application diversity of this new surveying mode.

Acknowledgments Thanks to the Academy of Finland for funding support with the following projects: Towards improved characterization of map objects, and Economy and technology of a global peer produced 3D geographical information system in the built environment. Thanks also to the

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 13

Finnish Funding Agency for Technology and Innovation through the project: Development of automatic, detailed 3D model algorithms for forests and built environment.

References [1]

[2]

[3]

[4]

[5] [6]

[7]

[8]

[9]

[10]

[11]

[12] [13]

[14]

J. Hyyppä, A. Jaakkola, H. Hyyppä, H. Kaartinen, A. Kukko, M. Holopainen, L. Zhu, M. Vastaranta, S. Kaasalainen, A. Krooks, P. Litkey, P. L. Saarenmaa, L. Matikainen, P. Rönnholm, R. Chen, Y. Chen, A. Kivilahti, and I. Kosonen, "Map updating and change detection using vehicle-based laser scanning," Proc. 2009 Urban Remote Sens. Joint Event, Shanghai, China (2009) [doi:10.1109/URS.2009.5137682]. A. Jaakkola, J. Hyyppä, H. Hyyppä, and A. Kukko, "Retrieval algorithms for road surface modeling using laser-based mobile mapping," Sensors 8, 5238-5249 (2008) [doi: 10.3390/s8095238]. M. Rutzinger, S. O. Elberink, S. Pu, and G. Vosselman, "Automatic extraction of vertical walls from mobile and airborne laser scanning data,"Proc. ISPRS Laser Scanning 38, Part 3/W8, 7-11, Paris (2009). G. Sithole and G. Vosselman, "Bridge detection in airborne laser scanner data," ISPRS J. Photogram. Remote Sens. 61, 33-46 (2006) [doi:10.1016/j.isprsjprs.2006.07.004]. G. X. Ritter, P. D. Gader, and J. L. Davidson, "Automated bridge detection in FLIR images," Proc. ICPR86, 862-864 (1986). H. Sui, J. Gong, J. Xiao, and M. Li, "Automatic recognition of bridges over water and registration in remotely sensed images with GIS data," Proc. ISPRS Commission VII Mid-term Symp. 36, Enschede, Netherlands (2006). D. Chaudhuri and A. Samal, "An automatic bridge detection technique for multispectral images," IEEE Trans. Geosci. Remote Sens. 46, 2720-2727 (2008) [doi: 10.1109/TGRS.2008.923631]. J. H. Jeong and M. Takagi, "Extraction of bridge positions from IKONOS images for accuracy control of bridge database," Proc. Asian Conf. Remote Sens., Kathmandu, Nepal (2002). R. Trias-Sanz and N. Lomenie, "Automatic bridge detection in high-resolution satellite images," Proc. 3rd Int. Conf. Computer Vision Systems (ICVS 03), 172-181, Graz, Austria (2003). N. Lomenie, J. Barbeau, and R. Trias-Sanz, "Integrating textural and geometric information for an automatic bridge detection system," IEEE Int. Geosci. Remote Sens. Symp. 6, 3952-3954 (2003) [doi: 10.1109/IGARSS.2003.1295325]. Y. Fu, K. Xing, Y. Huang, and Y. Xiao, "Recognition of bridge over water in highresolution remote sensing images," Proc. WRI World Congress Computer Sci. Info. Eng. 2, 621-625 (2009). Y. Wang and Q. Zheng, "Recognition of roads and bridges in SAR images," Pattern Recog. 31, 953-962 (1998) [doi: 10.1016/S0031-3203(97)00098-8]. U. Soergel, H. Gross, A. Thiele, and U. Theonnessen, "Extraction of bridges over water in multi-aspect high-resolution InSAR data," Proc. Photogram. Computer Vision (PCV’06), Bonn, Germany (2006). E. Cadario, H. Gross, H. Hammer, K. Schulz, A. Thiele, U. Thoennessen, U. Soergel, and D. J. Weydahl, "Change detection for bridges over water in airborne and spaceborne SAR data," IEEE Int. Geosci. Remote Sens. Symp. 5, 479-482 (2008) [doi: 10.1109/IGARSS.2008.4780133].

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 14

[15]

[16]

[17] [18] [19]

[20]

[21] [22]

[23]

U. Soergel, A. Thiele, H. Gross, and U. Thoennessen, "Extraction of bridge features from high-resolution InSAR data and optical images," Proc. 2007 Urban Remote Sens. Joint Event, Paris (2007). J. D. Wegner and U. Soergel, "Bridge height estimation from combined highresolution optical and SAR imagery," Int. Arch. Photogram. Remote Sens. 379(B73), 1071-1076 (2008). A. Manfredi and A. Pshenichkin, "Bridge detection from elevation data using a classifier cascade," Proc. Class 2007 Senior Conf., 21-24 (2007). J. Kaufman, "Bridge detection by road detection," Proc. Class 2007 Senior Conf., 42-46 (2007). D. Girardeau-Montaut, M. Roux, R. Marc, and G. Thibault, "Change detection on points cloud data acquired with a ground laser scanner," Proc. ISPRS Work. - Laser Scanning 2005, Enschede, the Netherlands (2005). J. Hernande, and B. Marcotegui, "Filtering of artifacts and pavement segmentation from mobile lidar data," Proc. IAPRS Laser Scanning 2009 38, Part 3/W8, 329-333. Paris (2009). Y. Zou and S. C. Chan, "A Huber recursive least squares adaptive lattice filter for impulse noise suppression," Proc. IEEE ICASSP’01, 6, 3769-3772 (2001). A. Kukko, C. O. Andrei, V. M. Salminen, H. Kaartinen, Y. Chen, P. Rönnholm, H. Hyppä, J. Hyyppä, R. Chen, H. Haggren, I. Kosonen, and K. Capek, "Road environment mapping system of the Finnish Geodetic Institute - FGI ROAMER," Proc. IAPRS 36, Part 3/W52, 241-247 (2007). G. Sithole, Segmentation and Classification of Airborne Laser Scanner Data, Netherlands Geodetic Commission, Delft, the Netherlands (2005).

Yi Lin is a Senior Research Scientist of the Department of Photogrammetry and Remote Sensing at the Finnish Geodetic Institute. He received his PhD degree in Photogrammetry and Remote Sensing from the Peking University in 2009. His current research interests include LiDAR-associated methodology and application. Juha Hyyppä is the head and professor of the Department of Photogrammetry and Remote Sensing at the Finnish Geodetic Institute. He received his Master of Science, the Licentiate in Technology, and the PhD degree from the Helsinki University of Technology, Faculty of Electrical Engineering, all with honors, in 1987, 1990, and 1994, respectively. He has been Senior Research Fellow (at HUT) granted by the Academy of Finland, Finnish delegate to ESA Earth Observation Programme Board, coordinator of the Design Phase of the National Remote Sensing Programme, President of EuroSDR Com II (information extraction of remote sensed data), Co-chair to ISPRS WG III/3 (processing of laser point clouds data), and Principal Investigator in ESA/NASA Announcement of Opportunity studies. He has coordinated/is coordinating more than 10 international projects. His references are represented by over 200 scientific/teachnical papers (more than 80 refereed papers). His research interest is laser scanning.

Journal of Applied Remote Sensing, Vol. 4, 043553 (2010)

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/14/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Page 15