CN107092020A - Merge the surface evenness monitoring method of unmanned plane LiDAR and high score image - Google Patents

Merge the surface evenness monitoring method of unmanned plane LiDAR and high score image Download PDF

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CN107092020A
CN107092020A CN201710257172.0A CN201710257172A CN107092020A CN 107092020 A CN107092020 A CN 107092020A CN 201710257172 A CN201710257172 A CN 201710257172A CN 107092020 A CN107092020 A CN 107092020A
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point cloud
road
point
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CN107092020B (en
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张显峰
高仁强
孙权
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Peking University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders

Abstract

The invention discloses a kind of fusion unmanned plane LiDAR and high-resolution remote sensing image highroad pavement planeness monitoring method, including：The LiDAR point cloud and high score image data obtained to unmanned plane is pre-processed；The multiple dimensioned geometric properties of the spectral signature of high score image and cloud data are carried out to construct validity feature variables set, so as to realize the Optimum Classification of road surface point cloud and non-road surface point cloud；Further more accurate road surface cloud data is obtained using Filtering Analysis；Build fine road surface model；Calculated using IRI indexes and obtain surface evenness.Existing surface evenness monitoring method inefficiency, the automaticity of the invention of effectively overcoming is low, is unsuitable for a wide range of system-wide section monitoring, can only obtain the road vertical section information of only a few, it is impossible to reflect the defects such as road surfaces three-dimensional structure information comprehensively；It is practical, it can be widely applied to urban road and the flatness monitoring of other inferior grade roads.

Description

Merge the surface evenness monitoring method of unmanned plane LiDAR and high score image
Technical field
It is to utilize unmanned plane laser radar (Light the present invention relates to highroad pavement planeness remote sensing monitoring technology Detection And Ranging, LiDAR) and high-definition remote sensing technology for highway pavement carry out a wide range of, high efficiency, The surface evenness monitoring and evaluation of low cost.
Background technology
Airborne laser radar (Airborne LiDAR) is a kind of active earth observation systems, collection global navigational satellite system System, inertial navigation, laser ranging and computer processing technology, can directly obtain high accuracy, high-resolution numeral The three-dimensional spatial information of ground model and atural object, the superiority that can not replace with traditional photography measuring method, and in landform The fields such as mapping, Road Alignment Survey Design, vegetation parameter inverting, disaster monitoring, urban planning and three-dimensional modeling have obtained more wide General application.Highway plays more and more important effect as a kind of transport channel of modernization in social economy, to edge The logistics of line, development of resources, invite outside investment, the effect actively promoted is played in industry restructuring, inter-regional economic cooperation etc., thus Its quality directly influences the economic development of area and country.Document [1] (Sun L, Zhang Z M, Ruth J.Modeling indirect statistics of surface roughness[J].Journal Of Transportation Engineering-ASCE,2001,127(2):In 105-111), nineteen sixty National Highway with The actual road test that communications and transportation administration federation carries out shows：About 95% road surface service performance depends on the flat of road surface Whole degree.Surface evenness, as one of leading indicator for evaluating pavement quality, is also the important of execution maintenance of highway pavement management Decision-making foundation.According to《2015 year traffic carrier statistical communique of developments》Statistics, Chinese Highway total kilometrage reaches within 2015 457.73 ten thousand kilometers, 446.56 ten thousand kilometers of highway maintenance mileage accounts for total mileage of highway 97.6%, and become in rapid growth year by year Gesture.With developing rapidly that highway in China is built, a wide range of, high efficiency, the highway pavement health status of low cost how are realized Monitoring just turns into a urgent problem to be solved.
Flatness monitoring is one of conventional highway pavement health monitoring and the important indicator evaluated.Flatness is evaluated The measurement of index has 3 meters of rulers to determine maximal clearance (h), international roughness index (International Roughness Index, IRI), exercise performance figure (Running Quality Index, RQI), jolt accumulated value (Bump Cumulative Value, VBI), flatness standard deviation (σ), power spectral density (Power Spectral Density, PSD) Deng.Existing traditional surface evenness monitoring method has：3m rulers, continous way smoothness measuring equipment, hand propelled profiler, vehicular Jolt accumulating instrument and the laser evenness detector (road detection vehicle for including integrated laser smoothness measuring equipment) that occurs in recent years, Its each the characteristics of be summarized in table 1.One of general character of these methods is exactly that can only be obtained by ground survey as the seldom road of number Road vertical section information, it is also relatively rough to the description of road evenness, and can not quick obtaining road surface three-dimensional geometry letter Breath so that understanding of the administrative department to road integral status is very limited.
The conventional surface evenness monitoring method of table 1.
The appearance of low latitude unmanned plane LiDAR technologies be not only obtain road surface three-dimensional information provide may, and should Technology is less to be limited by landform, high accuracy, high efficiency, low cost (especially a wide range of monitoring), can be while obtaining ground remote sensing The advantage of image is even more to provide a kind of new effective solution for surface evenness monitoring.Enter currently with LiDAR technologies The CROSS REFERENCE of walking along the street surface evenness monitoring and research are rare.Document [2] (Chin A, Olsen M J.Evaluation of Technologies for Road Profile Capture,Analysis,and Evaluation[J].Journal of Surveying Engineering,2014,141(1):4014011) describe using ground laser radar technology to public at a high speed Road has carried out flatness monitoring, still, and the do not satisfy the need spatial visualization method of surface evenness of this method is attempted, also not The construction method of a cloud classification method and road surface model is explored, another the heel of Achilles of the technology is exactly Small range (radius can only be obtained in some fixing point position vicinity<Cloud data 50m).If obtained on a large scale Information of road surface then needs a series of complex, the errors such as cloud, coordinate adjustment, point cloud registering, Coordinate Conversion constantly to accumulate at substantial amounts of Tired last handling process, not only flow is complicated but also very poorly efficient.Document [3] (path formations of the Guo Jiao based on vehicle-mounted laser Spend detecting system research [D] Capital Normal Universitys, 2013) describe and put down using mobile lidar technical Analysis road section Whole degree feature simultaneously positions the exceeded position of road damage, and still, this method is explored also without to a cloud classification method, But work of classifying is completed by traditional points cloud processing platform.In addition, this method relies solely on single number carry out table Reach, the spatial distribution characteristic for failing road pavement quality is explored, and require that detection car traveling velocity-stabilization, gps signal are steady It is fixed, and this is some buildings are blocked, rugged complicated highway section is difficult to apply.
In summary, there is inefficiency, automaticity in existing traditional highroad pavement planeness monitoring method mostly It is low, it is impossible to carry out a wide range of system-wide section monitoring, and can only obtain as several seldom road vertical section information, it is impossible to obtain The three-dimensional structure information of road surfaces.The flatness monitoring method of existing laser radar technique then has that measurement range is small, operation Efficiency is low, lack the defect that spatial visualization is expressed, data source is single, and can be limited by orographic condition.But highway road Surface evenness monitoring improves a wide range of surface evenness monitoring efficiency and accuracy in the urgent need to exploring new technological approaches.
The content of the invention
For the deficiency in existing highway surface evenness monitoring method, the present invention proposes a kind of fusion unmanned plane LiDAR With the surface evenness monitoring method and its embodiment of high resolution image.First, unmanned plane LiDAR systems are obtained Cloud data and high score image data are pre-processed, and then extract spectral signature from high score image data, cloud data is carried Take multiple dimensioned geometric properties.Using resulting geometric properties and spectral signature as point cloud classifications characteristic variable, and to these Characteristic variable carries out de-redundancy and dimensionality reduction operation, two class data is merged in feature rank, using in machine learning Random forest sorting algorithm carries out point cloud classifications；Further corrected by filtering operation and rough error and obtain comparing accurately road Waypoint cloud data set.Then high-precision fine road surface digital surface model (Digital is built using three-dimensional interpolation method Surface Model,DSM).Surface evenness is calculated based on IRI indexes principle, recycles GIS to carry out spatial visualization expression With flatness quality evaluation.Whole section can only be expressed compared to conventional surface evenness monitoring technology with single IRI values For information, party's rule can provide the spatial distribution of the different track IRI values of certain road different sections of highway, improve maintenance of surface The science of management work decision-making, effectively increases practicality of the unmanned aerial vehicle remote sensing technology in highway maintenance management application, can It is widely used in urban road and the surface evenness monitoring needs of other inferior grade roads.
The present invention principle be：Highway and periphery atural object are included in the cloud data that unmanned plane LiDAR remote sensing systems are obtained Three-dimensional coordinate information, can effectively remove noise jamming by the suitable point cloud denoising means of selection, and the high score that the same period obtains Resolution image data is rich in the spectral information of atural object and is highly susceptible to visual interpretation.Cloud data then because point set it is scattered without Interpretation easily directly perceived is this, and merged high score image with cloud data just can be while by spectral information and space geometry information Progress is integrated, and more available informations are provided for point cloud classifications.At present, there is one for the terrain classification of high score image Challenge, exactly cannot be distinguished by the ground object target of " foreign matter is with spectrum ", and the space geometry information of cloud data can then rise to this To good supplement.Likewise, cloud data itself is very discrete and cause in some demarcation lines and several due to point set Obscure between what the closely similar atural object of architectural feature, and high score image then can be to this good booster action of progress.Base In this, on the other hand one aspect of the present invention extracts allusion quotation from the spectral signature of high score Extraction of Image typical feature classification from a cloud The geometric properties of type atural object classification, strengthen the spatial shape difference of type of ground objects by building multiple dimensioned geometric properties, then A cloud is classified using efficient and the anti-noise sound intensity machine learning classification algorithm, then sorted cloud carried out thick Difference is corrected and the i.e. available more accurately road cloud data of classification filtering, and fine road is built in conjunction with a cloud interpolation algorithm Road DSM.On this basis, a series of vertical profile surface curves are chosen from road surface, segmentation calculates international flatness value and carries out sky Between Visualization, finally to flatness value carry out grade determination, so as to realize the evaluation to road evenness quality.This hair Bright method proves to can be utilized for the rapid automatized flatness quality condition prison of a wide range of highway pavement by precision evaluation Survey.
The technical scheme that the present invention is provided：
Fusion unmanned plane LiDAR and high score image a kind of surface evenness monitoring method, including：Data acquisition and pre- place The sport technique segments such as reason, feature extraction are with merging, point cloud classifications, the structure of road surface DSM models and Evaluation of Pavement Evenness.
Wherein, data acquisition and the purpose of pretreatment are that the cloud data that low latitude unmanned plane LiDAR systems are obtained is carried out Denoising, carries out registration and splicing to the high score image that the same period obtains, obtains the orthography data of test block, be data fusion Prepare.The purpose of data fusion is to carry out the spectral information of the geometric space information of cloud data and high score image data It is integrated, so that cloud data is provided simultaneously with space geometry information and spectral information.The purpose of point cloud classifications is to obtain Road cloud data, the process of data fusion provides available information for point cloud classifications, and typically species are carried out to test block Do not divide, build a series of geometrical characteristic parameters to express the difference of type of ground objects, and just constitute with reference to spectral signature parameter The characteristic variable of grader, then realizes the classification to cloud data using random forests algorithm, obtains road surface cloud data. The structure of road surface DSM models can provide basis, and each grid of DSM models for the expression of road surface three-D space structure Unit storage is all elevation information, is easy to flatness to calculate, and also provides condition for the spatial visualization expression of flatness. The purpose of Evaluation of Pavement Evenness is exactly that the flatness situation progress quantization by choosing certain roughness index road pavement is retouched State, and set up grade determination system and objective evaluation is carried out to the condition of road surface of test block, so that the current integral status of road pavement Provide prospective analysis and decision-making.
The surface evenness monitoring method that the present invention is provided specifically includes following steps：
1) using low latitude unmanned plane LiDAR systems (including：Unmanned aerial vehicle platform, LiDAR scanners, multispectral camera, posture The units such as positioning unit, automatic pilot it is integrated), obtain test block LiDAR point cloud data and high resolution image number According to；
2) the LiDAR point cloud data to acquisition are converted to current general cloud data file format (* .las), then right The high resolution image data that cloud data and unmanned plane are shot are pre-processed, including the denoising of a cloud, the splicing of image and The generation of registration process and orthography data；
3) to step 2) in the obtained wave band of orthography analyzed and extract the spectral signature of reflection atural object difference Parameter, together with the original wave band of high resolution image, constitutes multiple Spectral Properties comprising spectral information and collects；
4) Fusion Features are carried out to obtained spectrum characteristic data and cloud data, and entered by georeferencing benchmark of a cloud Row geo-spatial registration；
5) the major surface features type to test block is divided, and in step 4) on the basis of obtained cloud data by taking out Dilute cloud data vacuated, then chooses a series of geometrical characteristic parameter to the cloud data vacuated and calculates, and The cloud data comprising multiple dimensioned geometrical characteristic parameter and spectral signature parameter is obtained by changing the yardstick of spatial analysis, is connect The cloud data of the corresponding type of ground objects of therefrom selected part as the training sample of follow-up point cloud classifications, is vacuated remaining Cloud data is as treating divided data.The process that vacuates of point cloud reduces the operand in geometrical characteristic parameter calculating process, simultaneously The data volume of processing, substantially increases data-handling efficiency needed for also reducing classification；
6) step 5) in obtained characteristic variable would generally be a lot, inevitably occur between some variables exist compared with Strong correlation, causes characteristic variable redundancy.In order to find key characteristic variables, so that data are played with dimensionality reduction effect, and then Simplify the complexity of grader building process, in feature rank, merge the spectral information and laser point cloud number of multispectral image According to space geometry information, constitute new point cloud classifications feature set；
7) using Random Forest model be based on step 6) feature set to step 5) training sample carry out model training, lead to Cross constantly adjustment model parameter to obtain after satisfied classifying quality, classified applied to sample cloud data to be sorted, so The category attribute of sample point cloud is assigned to the cloud data after denoising according to space arest neighbors Interpolation Principle afterwards, so as to complete All put cloud classification.Classification results will may now enter also comprising the point cloud for being not belonging to road surface to sorted cloud data Row hand inspection and rough error are corrected, and further obtain road cloud data by category attribute filtering；
8) to step 7) the obtained elevation information of road cloud data, risen and fallen and become using TIN simulated roadway Change, the DSM for the fine pavement of road of high accuracy that regular grid is then built by natural neighbor interpolation method；
9) from step 8) in choose a series of buttock line in obtained DSM model datas, run IRI exponential model meters Calculate the corresponding IRI values of each hatching and by rasterizing processing, obtain the spatial distribution map of the IRI values on road surface；
10) to step 9) in obtained IRI values, the grade determination standard according to road evenness is classified, so that must To the corresponding flatness quality evaluation result in section to be measured.
For above-mentioned highroad pavement planeness monitoring method, further, step 2) in the LiDAR point cloud data that are related to, Specially meet the cloud data of following 4 conditions：
A) coordinate system：The coordinate system of cloud data is WGS84 coordinate systems (longitude, latitude) or the ground matched with locality Square plane projection coordinate system (X, Y)；
B) attribute information：Cloud data attribute information should also include elevation, scan angle, reflection by force in addition to longitude, latitude Degree；
C) cloud density is put：Point cloud density (i.e. laser footpoint radius is within 50mm) more than 400 points/square metre；
For above-mentioned flatness monitoring method, further, to step 2) in the process of data preprocessing that is related to, mainly Refer to following 4 kinds：The noise spot filtering of LiDAR point cloud, the abnormal elevation filtering of LiDAR point cloud, the scan angle of LiDAR point cloud Filter with the splicing of high resolution image with it is registering.For above-mentioned flatness monitoring method, further, to step 2) in relate to And high resolution image data, refer to the high-definition digital photo on the market captured by multispectral digital camera of good performance, And unmanned function provides the posture matched and positioning (POS) message file.For above-mentioned flatness monitoring method, enter one Step ground, to step 3) in the spectral signature parameter that is related to, be following 3 class：
A) gray value of the red, green, blue wave band of high score image.
B) standard deviation of the gray value of the red, green, blue wave band of high score image；
C) (laser pulse of LiDAR scanners transmitting falls within one kind of electromagnetic wave to the reflection intensity values of laser point cloud, together Sample has wavelength information).
For above-mentioned flatness monitoring method, further, to step 4) in the Fusion Features that are related to, specifically refer to point Cloud data are merged with the spectral signature parameter of high resolution image data, and the process merged is with cloud data On the basis of georeferencing, the spectral value of each pixel on image is matched by cloud data using affine inverse transformation model and sat Mark system；
For above-mentioned flatness monitoring method, further, to step 5) in the geometrical characteristic parameter that is related to, be following 3 Class：
A) local roughness (Local Degree of Roughness, LDR).The concrete meaning of this feature variable refers to The best-fitting plane that the neighborhood point under some space scale where some reference point to the reference point in point cloud is formed Distance；
B) local dimension feature (Local Dimension Feature, LDF).Document [4] (Brodu N, Lague D.3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology[J].ISPRS Journal Of Photogrammetry and Remote Sensing, 2012,68:121-134) describe local dimension Feature LDF, the concrete meaning of this feature variable be give directions some reference point in cloud and this refer between neighborhood of a point point in sky Between on be, with one-dimensional, two-dimentional or distributed in three dimensions characteristic measure value, to belong to these three dimensions with a numerical value to describe it Size degree, the value is exactly local dimension characteristic parameter；
C) local depth displacement (Local Height Difference, LHD).The concrete meaning of this feature variable is to give directions The reference point of some in cloud is concentrated with all points that neighborhood point of the reference point under some space scale is constituted, and reaches the point The difference of the maximum and minimum range in the formed best fit face of collection, is local depth displacement, the bigger plane value of fluctuating It is bigger.
For above-mentioned flatness monitoring method, further, to step 5) in the multiple dimensioned geometric properties that are related to, its is specific Implication refers to, when calculating some geometrical characteristic, its spatial analysis yardstick has that multiple (fixing some spatial analysis yardstick can obtain To a corresponding space geometry characteristic parameter), different types of ground objects its geometrical characteristic under different spatial analysis yardsticks Have difference.
For above-mentioned flatness monitoring method, further, to step 5) in the cloud data classification that is related to, mainly include Road waypoint cloud (contain traffic marking) and short vegetation (crops and meadow), soil, trees, move target, power line (including Electric pole) this 6 major class.
For above-mentioned flatness monitoring method, further, to step 5) in the point cloud that is related to vacuate process, be in space In sampled according to certain space interval, the process will ensure distribution of a cloud in space for original point cloud It is uniform (rather than messy distribution).
For above-mentioned flatness monitoring method, further, to step 6) in the feature selecting that is related to, mainly including feature Two processes of the search of subset and the evaluation of character subset.The search of character subset uses Greedy strategy, i.e.,：Initial Optimal subset is sky, and all characteristic variables to be assessed then constitute alternative features collection, and screening interpretability is concentrated from alternative features Most strong characteristic variable is added in optimal feature subset, and one cycle only adds a variable；A certain characteristic variable addition knot Need to concentrate from alternative features after beam and reject this feature variable, circulate next time again.The evaluation criterion of optimal feature subset： Correlation between the characteristic variable newly added and the characteristic variable chosen is relatively low.
For above-mentioned flatness monitoring method, further, to step 8) in the resolution ratio of DSM models that is related to will to the greatest extent can Can be high, and its height accuracy should be controlled within 15mm.Specific level and height accuracy assessment level are：From scene Select some that there is the object of obvious characteristic (such as pit) in road surface and record its corresponding gps coordinate, grown by measuring it Wide high attribute, then finds corresponding object in high resolution image, utilizes merging for image and LiDAR point cloud, it is easy to The correspondence position of the target is found in DSM models, and then interactively determines using dimensional measuring instrument the length of the target It is wide high, then statistical analysis completion is to the level and the accuracy evaluation of elevation of DSM models.
For above-mentioned flatness monitoring method, further, to step 9) in the road buttock line that is related to, its spacing gets over Small then corresponding surface evenness spatial distribution is finer.The theoretical foundation of IRI exponential models is a quarter vehicle model, The length (10m or 20m) of strict control sampling interval (within 50mm) and measuring unit is needed in calculating process.Optimal adopts Sample interval and measuring unit length can by Monte-Carlo Simulation Method quantitative analysis the timing IRI of vertical error one error Determined with the changing rule of sampling interval and measuring unit length.
For above-mentioned flatness monitoring method, further, step 9) in the rasterizing that is related to refer to road surface vertical section The IRI values of line are assigned to the grid cell of surrounding according to closest principle, so as to obtain the flatness spatial distribution in section to be measured Figure.
It is further, a series of comprising different smooth by being selected in section to be measured for above-mentioned flatness monitoring method The road surface sample of credit rating is spent as reference data, can carry out accuracy assessment to the evaluation result of this method, specific steps are such as Under：
1) according to the flatness quality classification standard of setting, the same period is a series of (comprising flatness matter in section to be measured selection Amount preferably with poor, covering health and Damage Types, every kind of road surface selects a number of ground actual measurement sample) road surface sample This is used as reference data；
2) choosing outward appearance, surface roughness, rutting depth, disease area, damaged condition this 5 indexs to reference data makes Flatness quality evaluation is carried out to it with expert point rating method, and is used as reference value；
3) actual measurement sample progress flatness value in this road surface is calculated with this method is same, according to flatness quality Grade scale, obtains the evaluation result of the surface evenness quality of this method；
4) evaluation to flatness quality results can be analogous to the evaluation of classification of remote-sensing images result, and confusion matrix is For evaluating the most common form of classification of remote-sensing images precision, therefore this method is also satisfied the need surface evenness matter using confusion matrix Amount result is evaluated.Precision evaluation index overall accuracy (Overall Accuracy, OA) and Kappa coefficients are introduced to knot Fruit is analyzed, and obtains the precision of this method.
Compared with prior art, the beneficial effects of the invention are as follows：
LiDAR data and high-resolution remote sensing image that the present invention is obtained based on low latitude unmanned aerial vehicle platform, propose to melt first The method that the spectral signature of picture of taking a group photo and the multiple dimensioned geometric properties of LiDAR point cloud build point cloud classifications feature set, is solved current The problem of road surface point cloud and non-road surface point cloud classifications；Road surface point cloud is recycled to build subtle three-dimensional road surface digital surface model (DSM) it is quick to calculate the flatness parameter for obtaining highway pavement, by introducing the Computing Principle of international roughness index, realize The quick monitoring and evaluation of pavement quality.Present invention firstly provides a kind of fusion low latitude unmanned plane LiDAR and high score image The flatness monitoring method of highway pavement rapidly and efficiently, efficiently solve inefficiency in existing surface evenness monitoring method, Automaticity is relatively low, is not suitable for a wide range of system-wide section monitoring, and can only obtain as several seldom road vertical profile surface evenness letters Breath, it is impossible to obtain the deficiencies such as road three-dimensional structure information.This method can quickly grasp large-scale surface evenness situation, from And provide scientific basis for pavement maintenance ＆ rehabilitation decision-making.Meanwhile, the Point Cloud Processing flow that the present invention is designed is simple easily real Existing, effect is notable.In addition, the flatness mass space distribution map that the present invention is provided can reflect the flatness of highway pavement comprehensively Situation, by single number can only express the flatness information in whole section compared to conventional flatness monitoring technology and Speech, effectively promotes the informationization and the development that becomes more meticulous of road maintenance management, improves pavement maintenance management job acceptance decision It is scientific.
Fusion evaluation and LiDAR of the present invention carry out surface evenness monitoring, and compared with prior art, one is to employ low latitude Unmanned plane LiDAR data source, and propose fusion spectral signature information first with multiple dimensioned geometric properties information to optimize waypoint Cloud classification, builds fine DSM on this basis, and rapid extraction has the surface evenness information of spatial distribution；Two be data Processing procedure succinctly easily realizes that the mathematical modeling used is it can be readily appreciated that effect is notable, and all operations can be in personal common electricity Completed on brain, to the less demanding of hardware；Three be that in the operating process for providing feasibility, furthermore present optimization data The method of processing, and checking is completed by testing, with good repeatability.
Brief description of the drawings
The flow chart for the surface evenness monitoring method that Fig. 1 provides for the present invention
Fig. 2 is local roughness computational methods schematic diagram.
Fig. 3 simplifies expression schematic diagram for local dimension feature；
Wherein, (a) is one-dimensional space distribution characteristics, and (b) is two-dimensional space distribution characteristics, and (c) is that three-dimensional spatial distribution is special Levy.
Fig. 4 is that local depth displacement calculates schematic diagram.
Fig. 5 is the natural neighbor interpolation algorithm schematic diagram of progress point cloud interpolation in this method.
Fig. 6 is the algorithm schematic diagram of the international roughness index (IRI) used in this method；
Wherein, ZsRepresent the vertical displacement of spring carried mass, ZuRepresent the vertical displacement of nonspring carried mass, msRepresent spring charge material The size of amount, muRepresent the size of nonspring carried mass, KsRepresent the stiffness system of the spring of connection spring carried mass and nonspring carried mass Number, CsRepresent the linear damping coefficient of connection spring carried mass and nonspring carried mass, KsRepresent the precision system of the spring of connection tire Number, can simulate the damping effect produced by tire, and y (x) represents road surface elevation, and what b was represented is containing length of the tire to ground (part that tire is contacted with ground).
Fig. 7 is the road surface sample photo of the different flatness features of ground acquisition in the embodiment of the present invention
Wherein, (a1) pit, is characterized as building stones exposure and in big block distribution, shape is approximately circular or ellipse, Flatness quality evaluation is extreme difference (Failed)；(a2) pit, is characterized as that building stones are exposed but area is relatively small, shape is approximate Circular or ellipse, flatness quality evaluation is extreme difference (Failed)；(b1) collapse, be characterized as ribbon distribution and depth compared with (building stones do not expose), area coverage are big greatly, and flatness quality evaluation is extreme difference (Failed)；(b2) collapse, be characterized as band Shape is distributed and depth smaller (building stones do not expose), area coverage are small, and flatness quality evaluation is poor (Poor) or extreme difference (Failed) depression depth and area coverage, are specifically dependent upon；(c) crack (including minute crack and thick crack), is characterized as narrow Long shape distribution, crack gaps are within 10mm, and flatness quality evaluation is medium (Fair) or poor (Poor), is specifically dependent upon Fracture width and area；(d) pitted skin, is characterized as that road surface is very coarse or has many trickle pits, flatness quality evaluation For medium (Fair) or poor (Poor), the degree and area of surface breakdown are specifically dependent upon；(e) sandstone are covered, and are characterized as road surface Occur block damaged and covered by sandstone, flatness quality evaluation is poor (Poor)；(f) the general old pavement of flatness, feature Rougher but integral surface is smooth for pavement texture, flatness quality evaluation is medium (Fair)；(g) flatness is preferably old Road surface, is characterized as that road surface color and luster more light, texture densification, surface are very smooth, flatness quality evaluation is preferably (Good).
Fig. 8 changes LiDAR point cloud data to be obtained in test block in the embodiment of the present invention and passing through form.
Fig. 9 is the multispectral orthography in test block of the embodiment of the present invention through image joint and registration process.
Figure 10 is that (blue ripple is shown in the LiDAR point cloud data of fusion evaluation band class information in the embodiment of the present invention in figure Section).
Figure 11 is to carrying out feature using 5 spectral informations and 36 multiple dimensioned geometric properties information in the embodiment of the present invention Property value characteristic distributions of 23 key characteristic variables and training sample retained after selection under these characteristic variables；
Wherein, 5 light such as reflected intensity, R-G-B wave bands gray standard deviation, R-G-B band grey datas of (a) different atural objects Spectrum signature；(b) local roughness (LDR) and local dimension feature (LDF) of different atural objects；(c) the local dimension of different atural objects Spend feature (LDF) and local depth displacement (LHD)；(d) the local depth displacement (LHD) of different atural objects.Length digital generation in parantheses Table space analyzes scale size, and LDF_1 represents one-dimensional local dimension feature, and LDF_1 represents the local dimension feature of two dimension
The DSM that Figure 12 expresses for the millet cake cloud interpolation generation TIN that satisfied the need in the embodiment of the present invention.
Figure 13 is the road DSM that is obtained using natural neighbor interpolation algorithm in the embodiment of the present invention.
Figure 14 is that the embodiment of the present invention calculates obtained surface evenness spatial distribution map using IRI exponential models.
Figure 15 is that the surface evenness mass space that the embodiment of the present invention to flatness value obtain after reclassification is distributed Figure.
Figure 16 is the spatial distribution statistical chart of different pavement quality types in the embodiment of the present invention.
The confusion matrix schematic diagram for the flatness quality evaluation result that Figure 17 is set up by assessment case study on implementation of the present invention.
Embodiment
Below in conjunction with the accompanying drawings, and by instantiation the specific implementation process of the present invention is further described, but not with any Mode limits the scope of application of the present invention.
Fig. 1 is the flow of the surface evenness monitoring method of fusion unmanned plane LiDAR proposed by the present invention and high score image Block diagram, comprises the following steps：
Step 1：LiDAR point cloud data in section to be measured and high resolution image are obtained using unmanned plane LiDAR systems simultaneously Data；
Step 2：(production firm is mostly for the cloud data form difference provided due to different LiDAR systems production firms Have a set of customized storage format), in order to be able to be handled on some common softwares, it is therefore desirable to LiDAR point cloud number According to row format conversion is entered, the field attribute of selection and the reservation of projected coordinate system is noted in output；
Step 3：Denoising is carried out to the universe point cloud file (* .las) obtained in step 2, interference noise is tentatively removed Information, thus obtain it is dry after cloud data.Specific denoising mode includes：
A) noise spot is filtered.Mainly the refraction in LiDAR point cloud due to laser pulse or multipath effect are produced Abnormity point rejected；
B) abnormal elevation is filtered.Local abnormal elevation (rough error) is mainly rejected, specific determination methods are：Set Certain search radius threshold value r, if the elevation average value of the neighborhood point where certain point and the point in radius r is more than 3 Standard deviation again, then can be considered abnormal elevation and rejected；
C) angular filter is scanned.Due to when unmanned aerial vehicle flight path is planned, in order to be able to the surface information in Overall Acquisition section, nobody The flight path of machine should directly over section, therefore using point cloud scan angle attribute can to data carry out filtering so as to Data processing amount is reduced, unnecessary data processing time is saved.Assuming that the horizontal range immediately below certain point to aircraft is D, the flying height of aircraft is h, then the scan angle theta of the point is defined as formula 1：
θ=arctan (d/h) (formula 1)
The maximum scan angle θ of road can be determined according to the bounds of roadmax, therefore suitable scan angle threshold is set Value can separate road area.Have in view of course line needs as far as possible to set scan angle threshold value in certain deviation, experiment It is somewhat larger；
Step 4：Utilize the unmanned plane image joint software (business developed by Pix4D companies of Switzerland used in such as this example With software Pix4D Mapper), image joint is carried out to the high resolution image data that unmanned plane in section to be measured is shot and matched somebody with somebody Quasi- processing, can obtain section to be measured just penetrates remote sensing image；
Step 5：Spectra feature extraction is carried out to high resolution image, the spectral signature parameter that the present invention is used includes following 4：The gray value of red, green, blue wave band and the standard deviation of these three wave bands, the mathematical modeling of standard deviation is formula 2：
Wherein, std. represents standard deviation, gi(i=1,2,3) be the wave band of red, green, blue three gray value.
Step 6：To carrying out Fusion Features comprising multiband high score image and cloud data, with the geographical space of cloud data With reference on the basis of, the pixel coordinate of image is mapped to the geographical coordinate of the pixel center point by affine transformation, so that by shadow As upper corresponding spectral information matches cloud data.The mathematical modeling of affine transformation is formula 3：
But in practical operation, because the pixel quantity of image is far longer than the data volume of a cloud, and both are also endless Full weight is closed, therefore in order to reduce amount of calculation, the present invention uses the process that image coordinate is transformed into from geographical coordinate, that is, imitates Inverse transformation is penetrated, formula 4 is expressed as：
In formula 3, formula 4, G is affine transformation matrix, ai,bi,ci(i=1,2) is affine transformation coefficient, mapX, mapY points It is not X-coordinate, the Y-coordinate of a cloud, row, col are the row, column value of image respectively.GeoTiff or other with geographical coordinate believe Typically just there is affine coefficients information in the metadata of the image file of breath, can also pass through 4 border point coordinates on image in addition And its corresponding ranks number (pixel coordinate) information is solved by building equation and obtained to affine coefficients.
Step 7：Octree is built to cloud data to store, using the length of Octree grid unit as between minimum space Vacuated every to a cloud, the cloud data vacuated.Then the meter of geometrical characteristic parameter is carried out to the cloud data vacuated Calculate, the geometrical characteristic parameter used in the present invention includes three below：
A) local roughness (Local Degree of Roughness, LDR), as illustrated in fig. 2, it is assumed that whole three-dimensional point Cloud point integrates as C={ pi|pi=(xi,yi,zi), i=1,2,3 ..., n }, the current point that calculates is p=(x, y, z) ∈ C, three-dimensional space Between in its radius be R neighborhood point set be P={ pj|||pj-p||≤R,pj≠ p, j=1,2,3 ..., n }, if plane T:Ax+ By+Cz+D=0 is point set Q={ P ∪ p } optimal fitting plane, then LDR mathematical form can be expressed as formula 5：
A in formula 5, B, C, D represent the basic parameter of plane equation respectively, and x, y, z represents the x of current search point respectively, Y, z coordinate.When optimal fitting plane T in the present invention refers to that all points of concentration are projected in x-y plane so that z value errors Quadratic sum S is minimum plane, and specific mathematical form is formula 6：
X in formula 6i,yi,ziRepresent i-th point of X respectively, Y, Z coordinate, other specification implication is with formula 5；
B) local dimension feature (Local Dimension Feature, LDF), as shown in figure 3, three-dimensional point cloud point set and Neighborhood point set is configured similarly to local roughness, it is assumed that current search point is p=(x, y, z) ∈ C, makes point set Q={ P ∪ p } =[X Y Z], carries out principal component transform to Q first, obtains matrix Q three principal component coefficient μ1,μ2,μ3(μ1≥μ2≥μ3), Further these three principal component coefficients are normalized by formula 7：
Wherein, λ1、λ2、λ3Correspond respectively to current search point and one-dimensional, two-dimentional, three-dimensional spatial distribution is obeyed in the neighborhood Degree, namely one-dimensional, two-dimentional, three-dimensional feature value, due to λ1+λ2+λ3=1, therefore only need to the first two characteristic parameter The local dimension feature of current search point is expressed, the geometric properties computational efficiency of cloud data can be so improved；
C) local depth displacement (Local Height Difference, LHD), as shown in figure 4, wherein optimal fitting plane Definition and local roughness calculate in optimal fitting plane definition it is consistent, it is assumed that current search point is p=(x, y, z) ∈ The neighborhood point set that its radius is R in C, three dimensions is P={ pj|||pj-p||≤R,pj≠ p, j=1,2,3 ..., n }, point Collection Q={ P ∪ p } is to include current search neighborhood of a point point set, then LHD mathematical form can be expressed as formula 8：
In formula 9The i-th, distance of j neighborhood point to optimal fitting plane is represented respectively, and other specification implication is same Formula 5；
In experimentation in order to accelerate calculate Searching point local depth displacement speed, to Q carry out SVD decompose can be quick Plane T 4 key parameters A, B, C, D are obtained, are then looked for apart from point maximum plane T and apart from click-through minimum plane T Row distance asks poor, so as to obtain the local depth displacement of Searching point.
By the size for the radius R for constantly changing search neighborhood, and calculate successively each geometric properties (LDR, LDF and LHD) the respective value under different search space radius R, can obtain one group of column vector on these three geometric properties, this Group column vector is exactly the multiple dimensioned geometrical characteristic parameter to ground object target (object where Searching point).
Step 8：After being merged to high resolution image with cloud data, cloud data now contains substantial amounts of spy Variable (geometrical characteristic parameter and spectral signature parameter) is levied, the matrix for forming a higher-dimension (each puts inherently one 3-dimensional Vector), correlation is also likely to be present between these characteristic variables in itself, causes information to produce certain redundancy, redundancy is removed The process of information is exactly the process of feature selecting.The feature selecting strategy that the present invention is used is divided into two processes：Character subset is searched Rope and character subset evaluation.The strategy of character subset search is sweep forward, and the evaluation of character subset is then mainly by phase Relation number assesses the degree of relevancy of intra-subset.Concrete implementation process is as follows：
1) data structure of two aggregate forms is initially set up, it is assumed that respectively P and Q, P represent candidate subset, Q is represented most Excellent subset, category attribute variable is Y, and other attributes in addition to category attribute are X={ X1,X2,X3,...,Xn}.By in addition to Y Other characteristic variables are added in candidate subset, and the relevance threshold for setting intra-subset is δ；
If 2)Into step 4), each characteristic variable X in X is read in otherwise circulationi, and calculate it by formula 10 With Y relative coefficient, the characteristic variable X maximum wherein with Y relative coefficient is foundc, and make δ=max { corr (Xi, Y)}(Xi∈ X), subsequently into 3)；
In formula 10, Xi, Y represent ith feature, categorical attribute respectively,Ith feature, categorical attribute are represented respectively Average value, andThe ith feature and category attribute value of j-th of sample data are then represented respectively.
3) by characteristic variable X to be selectedcRelativity evaluation is carried out with each characteristic variable in Q, if XcWith it is any in Q The correlation of one characteristic variable is respectively less than δ, then by XcIt is added in Q, while deleting this feature variable out of P, otherwise directly This feature variable is deleted out of P, continues step 2)；
4) Q is exported, terminates character subset search.
Special instruction, can also be omitted if characteristic variable number is not a lot (within such as 30) steps.
Step 9：After cloud data after being vacuated, an interactive selection part therein trains sample as training sample This requirement covers all types of ground objects, and training sample can not be concentrated excessively, where being suitably dispersed in a cloud in space, The training samples number of every kind of type of ground objects should not very little, and otherwise grader can not build disaggregated model.Random Forest model has Have adaptable (can be while handling discrete type, numeric type data) to data set, training speed is fast, noise resisting ability is strong, The advantages of being not easy to be absorbed in few over-fitting, model parameter, good classification effect and as most popular calculation in machine learning field One of method, the present invention uses for reference its advantage and come pair while the cloud data for merging multiple dimensioned geometric properties and spectral signature carries out ground Thing category classification.
The basic thought of the algorithm is：Assuming that the number of input sample data is N (each sample correspondence a line), feature becomes The number of amount is M (each characteristic variable correspondence one is arranged, i.e., raw sample data size is N*M matrix), is had at random when initial That puts back to selects n sample as the training of one tree (every one tree is all binary tree in random forest) from N number of sample Collection, the root node of the tree is exactly training set；M characteristic variable is randomly selected from M feature, is then become from this m feature Enter line splitting according to the minimum principle selection characteristic variable of purity in amount, so as to which the tree is divided into two nodes, the two sections A part for each self-contained training subset of point, it is so recursive to be divided, until the tree can not continue division and (reach y-bend The maximum depth of recursion of tree) or this m feature use untill finishing.Repetition built just now tree process can obtain containing times The forest (random forest) of meaning tree, and the training sample of each tree is all not exclusively the same, and the characteristic variable used is also endless It is complete the same.In the prediction classification stage, when inputting a sample to be sorted, each tree in forest can all be obtained to the sample One decision-making classification, the distribution frequency that just can obtain sample to be sorted under each prediction classification is counted to decision-making classification, The maximum classification of output wherein frequency predicts classification as it, so as to complete classification.
The purity Measure Indexes used in the present invention are Gini coefficient, and using document [5], (Chen Huazhou, Chen Fu, Shi Kai wait Fish meal protein Near-Infrared Quantitative Analysis [J] agricultural mechanical journals based on random forest, 2015 (05):233-238) record Computational methods, its mathematical modeling is：
1) assume that training sample set includes P classification, the sample number ratio of i-th of classification is pi, then its Gini coefficient Gini (P) value is：
M subset is divided into according to some characteristic variable to the training sample set, the number of samples of i-th of subset is ni, The total sample number of the training sample set is n, then the Gini coefficient Gini of subset after dividingsplit(P) it is：
Step 9：Point cloud after to vacuating is classified, and filtering is carried out to it according to category attribute can obtain road surface point cloud number According to the point cloud interpolation algorithm for further passing through correlation can be obtained by the DSM data in section to be measured.To reduce elevation noise pair The influence that surface model effect and follow-up flatness are calculated, this method generates dimensional topography first with TIN (TIN) Data, then build the DSM models of road using natural neighbor interpolation method.Document [6] (Sibson R.A brief description of natural neighbour interpolation[J].Interpreting Multivariate Data,1981:21-36) the natural neighbor interpolation method recorded calculates the height value for obtaining interpolation point by formula 13：
W in formula 13iRepresent the weight coefficient of i-th of elevational point in neighborhood, ZiIt is then the height of i-th of elevational point in neighborhood Journey, i represents i-th of elevational point in neighborhood, and Z is the height value of interpolation point.This method determines the principle of neighbor point and apart from nothing Close, but proximity relations is determined with whether Tyson (Voronoi) polygon of interpolation point has common factor, and neighbor point Weight be then interpolation point Thiessen polygon and neighbor point Thiessen polygon public area proportion (such as Fig. 5).Need It should be noted that：Consider in the Thiessen polygon building process of interpolation point be a little, and the Thiessen polygon of neighbor point Then interpolation point is foreclosed in building process.The basic characteristics of the interpolation method are that it has locality, using only to be inserted A sample point subset around value point, weight coefficient ensure that result that interpolation obtains within the scope of used sample value.Its Advantage is to be not in extrapolation trend and will not generate mountain peak, lowland, ridge or mountain valley that input sample point is not yet represented, And obtained surface is smooth in other all positions in addition to input sample position；
Step 10：A series of buttock line of determining deviations (such as 0.2m) is chosen using obtained road DSM, is then utilized International roughness index model tests the flatness value in section to express.In present invention specific implementation, using document [7] (Sayers M W,Gillespie T D,Queiroz C A.The international road roughness Experiment [J] .1986) record method calculate obtain section flatness value.The central principle of IRI indexes be four/ One vehicle model (such as Fig. 6), when a quarter vehicle is travelled with certain speed along road vertical section, in swashing for road gradient Encourage under effect, system will produce vibration, calculate it and hung with (such as 1km) after fixing speed (such as 80km/h) traveling certain distance The accumulation shift value of system is IRI, and unit is m/km.To solve the relative displacement of the suspension, second order vibration is set up micro- Divide equation such as 14~formula of formula 15：
Wherein, Y is road surface elevation, ZsAnd ZuSpring carried mass displacement and nonspring carried mass displacement, μ, C, K are represented respectively1、K2 It is coefficient, and is recorded according to document [7], is had：
Wherein, msRepresent the size of spring carried mass, muRepresent the size of nonspring carried mass, KsRepresent connection spring carried mass and The stiffness factor of the spring of nonspring carried mass, CsRepresent the linear damping coefficient of connection spring carried mass and nonspring carried mass, KsTable Show the quality coefficient of the spring of connection tire.
Structural regime variableThen full scale equation can be turned to：
Wherein：
Formula 16 is a nonhomogeneous linear differential equation, and its corresponding state equation is：
Wherein,
PR=A-1(ST-I) B, (formula 19)
I in formula 18, formula 19 is unit matrix, with reference toFirst state of value and the gradientSequence, passes through the side of recursion Formula can be tried to achieve any time successivelyAgain：It is rightKey can be tried to achieve by being integrated Variable ZsAnd Zu, can be calculated by formula 20 and obtain IRI:
Document [8] (Wang Jianfeng, Song Hongxun, Ma Ronggui Evaluation of Pavement Evenness indexs IRI influence factor [J] Chongqing University of communications's journal (natural science edition), 2012,31 (06):Above-mentioned calculating IRI method 1145-1148) is described, but is made Data are traditional ground survey data.It is the calculating process for realizing IRI indexes in this example, it is necessary first to by DSM data Carry out equidistant intervals sampling processing and obtain road surface elevation point sequence Y={ y0,y1,...,yn, and as the defeated of model Enter data.Then state matrix ST and PR are calculated according to matrix A and B, then carrying out first-order difference computing to Y obtainsAnd make Z (0)=[b 0 of b 0]T, b is the velocity amplitude (if road section length is less than 11m, taking 0m) at 11 meters away from starting point, so that sharp It can be calculated with formula (17) and obtain t=0,1,2 ... the moment is correspondingValue, so it is rightIt is cumulative i.e. available by the period The integral displacement of Each point in time, and to the displacement of Each point in time by carrying out asking difference to take absolute value and sum again formula 20 Suo Shi, Finally average and solve IRI.
Step 11：The result of calculation of step 6 is only the IRI value sequences for having obtained single hatching, whole in order to be able to simulate The flatness value of individual road surfaces, can be to each grid cell on road DSM according to the corresponding IRI of closest principle distribution Value, the error size of this processing mode depends primarily on the spacing size of buttock line, and the smaller then error of spacing is also smaller. The flatness spatial distribution in whole section to be measured is just can obtain by said process.
Step 12：Obtained road surface IRI values are classified with reference to following standard (such as table 2), that is, complete road pavement smooth Spend the evaluation of quality.The classification sets standard to need to combine China《Asphalt highway maintenance technology specification》Maintenance of surface The related request of quality is set, and this method is to combine《Asphalt highway maintenance technology specification》(JTJ 073.2-2001) Specification is determined.Its corresponding distribution proportion can be calculated by counting the pixel number of each credit rating of section to be measured, and The related specifications repaired with reference to road maintenance, so as to need which kind of maintenance measure to make science decision in the section.
The surface evenness quality grading of table 2 sets standard
In order to which the reliability to this method is evaluated, using following step to this method surface evenness quality-monitoring knot Fruit carries out precision evaluation.
1) according to the flatness quality classification standard of setting, section to be measured selection it is a series of (comprising flatness quality compared with It is good with it is poor, include health and Damage Types, every kind of road surface selects a number of ground to survey sample data) road surface sample This is used as reference data；
2) choosing outward appearance, surface roughness, rutting depth, disease area, damaged condition this 5 indexs to reference data makes Flatness quality evaluation is carried out to it with expert point rating method, and is used as reference value；
3) this road surface sample progress flatness value is calculated with this method is same, according to flatness quality grading Standard, obtains the corresponding evaluation result of this method；
4) precision evaluation to flatness quality results can be analogous to the precision evaluation of classification of remote-sensing images result, and mix The matrix that confuses is the most common form for evaluating classification of remote-sensing images precision.Therefore this method also uses confusion matrix road pavement Flatness quality results are evaluated.Result is carried out by nicety of grading evaluation index overall accuracy (OA) and Kappa coefficients Analysis, obtains the precision of this method, specific precision evaluation index calculating method is：
In formula 21, formula 22, N is total sample number, niiThe evaluation result of this method is represented with being i grades with reference to evaluation result Sample size, ni.Then represent with reference to the total sample number that evaluation result is i grades, n.iThen represent the evaluation result of this method as The total sample number of i grades.
Illustrate this method specific implementation and precision evaluation process by taking one, certain city country highway as an example below.
(1) data acquisition
Unmanned plane LiDAR point cloud data
The Highway house at county level is that road width is about near 44 ° 24 ' 47 of north latitude ", east longitude 85 ° 53 ' 47 " in geographical position 8m.The laser scanning that the Scout B1-100 depopulated helicopters that researcher produces on June 23rd, 2016 by Switzerland are carried Instrument system (Rigel VUX-1LR) carries out experimental data collection, the basic parameter letter of the laser scanner directly over test block Breath (source as shown in table 3：http://www.riegl.com/products/newriegl-vux-1-series/ newriegl-vux-1lr/).In order that the cloud data density that must be obtained is sufficiently high, unmanned plane during flying apart from road in experiment Face be highly set to 30m, flying speed be 5m/s, scan angle be 110 °, scan frequency be 550KHz, the laser finally obtained Pin point density is 300-600pts, scanning breadth sunny nothing (few) cloud on the day of being 85.7m, data acquisition.
The critical parameter information of the laser scanning instrument system of table 3
Ground data acquisition
Obtaining LiDAR point cloud data on the same day, researcher carries out characters of ground object point and road surface in flight range The collection of sample data.Characters of ground object point data is mainly gathered along road periphery, by choosing some crucial ground object targets (such as vehicle body, markings, pit, bulk crack) carries out physical dimension measurement, is positioned simultaneously using real time differential GPS equipment Carry out notes archive；Then different types of road surface sample is chosen on flight range area road surface and (includes flatness quality Preferably with poor, covering health and Damage Types) it is sampled and takes pictures and make feature description and statistic of classification, all data Collecting work all carries out position record and preservation of taking pictures.This research acquires 10 crucial atural object characteristic points, 9 kinds of differences altogether The road surface sample of type (sample point sum 52, every kind of road surface sample is about 5-7).Road surface sample type includes：Pit (point big block and small bulk), collapse (a point building stones are completely exposed not to be exposed with building stones), crack (large fracture and gap), fiber crops Face, sandstone are covered, surface evenness is general and flatness is good, as shown in Figure 7.
(2) data processing
The specific process step of data is as shown in figure 1, specific step implementation procedure is as follows.
The first step：Had as the attribute that the LiDAR point cloud packet acquired in Rigel VUX-1LR laser scanners contains：Three Dimension coordinate, scan angle, echo quantity, echo times, laser intensity.The point cloud file format of original point cloud file and non-universal, The software kit provided by Rigel companies enters row format to cloud data and is converted to general .las forms, such as Fig. 8 institutes Show；
Second step：Visual identification is first passed through to the progress denoising of above-mentioned LiDAR point cloud data, in experimentation tentatively will Noise spot is rejected, and it is that 0.2m is rejected to local abnormal elevation then to set search radius, is had a lot of social connections then according to road The scanning angular region for the road surface point cloud that degree and unmanned plane during flying high computational are obtained is in ± 8 °, it is contemplated that course line has certain Set scan angle threshold value to be ± 24 ° in deviation, experimentation to filter cloud data；
3rd step：The commercial Unmanned Aerial Vehicle Data processing software Pix4D Mapper developed using Pix4D companies of Switzerland are treated Survey after the high resolution image data that unmanned plane is shot in section carry out image joint and registration process and can obtain section to be measured Orthophotoquad (Fig. 9).Then feature extraction is carried out to the orthography, the gray value standard for calculating the wave band of RGB three is poor As new characteristic wave bands, and with the band combination of RGB three, obtain 4 characteristic wave bands containing spectral information；
4th step：The point in a cloud is matched with the pixel in image using affine Transform Model, then by image On band value be fused in cloud data, as a result as shown in Figure 10 (by taking blue band class information as an example)；
5th step：Cloud data is carried out after Fusion Features, it is necessary to enter to the fused data with high resolution image data The multiple dimensioned geometrical characteristic parameter of row is calculated.Because cloud data amount is very big, to save the time, while not influenceing result again Reliability, this example is first optimized to the storage organization of cloud data, that is, builds Octree storage organization, is set one and is compared Suitable numerical value (0.3m is used in this example) carries out resampling, the point after being vacuated as space memory cell length Cloud data；
6th step：Centered on each point of above-mentioned cloud data, set search radius in [0.2m, 1.0m] and with 0.1m scans for for spacer unit, and the neighborhood point of search is then apart from small in cloud data after denoising with current search point In the point set equal to search radius, local roughness (LDR) then respectively under calculating current scale, local dimension feature (LDF), local depth displacement (LHD).With the adjustment of space search yardstick, you can obtain different scale geometric properties information Cloud data；
7th step：The cloud data obtained to step 6 selects the partial dot cloud for covering all types of ground objects by visual observation As training sample, dimensionality reduction (such as Figure 11) is then carried out to characteristic variable by feature selecting, it is easy to see that many after feature selecting Separating capacities of the yardstick geometrical characteristic parameter LHD to all kinds of atural objects for other characteristic variables is most strong.Further by Random Forest model carries out grader structure.This example sets the number of tree to be 200 in building process, and the depth of tree is 40.Its It is secondary using constructed Random Forest model to the cloud data after vacuating is classified, so as to complete to vacuate minute of a cloud Class process；
8th step：The point cloud vacuated is classified, it is necessary to take certain side due to up to the present having only completed Method is classified to whole point clouds after denoising.Because two nearer object its attributes of space length are more similar, therefore this example The class classified a little nearest apart from the point is assigned to the category attribute of the cloud data after denoising according to space nearest neighbouring rule Other property value；
9th step：Classification screening is carried out to sorted cloud data, road cloud data is obtained, then pass through irregular three Angle net builds road surface TIN models (Figure 12), and the DSM models of road surface are further obtained using natural neighbor interpolation, are Meet IRI indexes and calculate needs while take into account data processing needs, raster resolution is set to 50mm, as a result such as Figure 13；
Tenth step：By interaction vector quantization mode in Arcgis10, inside road along road direction digital into At intervals of a series of 0.125m buttock lines, the IRI values on these hatchings are then calculated using IRI exponential models.In order to The difference reflected on each section longitudinal direction, need to set suitable computing unit length, this example is set to 10m when calculating IRI. In order to which the IRI values of calculating are carried out the flatness spatial distribution result that rasterizing obtains whole section by effect of visualization again, such as scheme 14；
11st step：Standard is set according to the flatness quality grading that this method is set, to obtained surface evenness sky Between distribution map carry out reclassification, the flatness quality distribution diagram on road surface is obtained, as shown in figure 15, by different quality grade Grid cell carry out statistical analysis, it may be determined that each credit rating distribution proportion statistical chart in the section, as shown in figure 16；
(3) interpretation of result
In general, it is main based on " Fair " for the quality evaluation result of the road south of road section the characteristics of aspect of longitudinal direction, it is few Subregion is " Failed "；Quality evaluation in the middle part of road is worst, and evaluation result is mainly " Poor " or " Failed "；And The quality evaluation result of road north of a road section is then in the majority with " Fair " or " Failed ", and this is consistent with the distribution trend of flatness. The characteristics of lateral aspects is then that the credit rating of approximate centerline is lower than both sides driveway, based on " Failed ", and is driven a vehicle Evaluation result near road is then main based on " Fair " or " Poor ", and the pavement quality grade in road boundary area is then relative It is relatively low, it is " Poor " or " Failed " that this subregion causes rough surface not due to reasons such as pavement depression, sand mixing It is flat.From the point of view of each credit rating distribution map (Figure 15), it is known that the overall flatness quality in the section based on " Fair ", its Distribution proportion is up to 41.1%, and secondly quality occupies for the section distribution proportion of " Poor " or " Failed ", is respectively 27.4% He 24.9%, and quality is minimum for the section proportion of " Good ", only 6.6%.And quality is " Poor " or " Failed " Distribution proportion summation account for whole section more than half, this indicates that the overall mass deviation in the section, it is necessary to make great Maintenance.
(4) precision evaluation
Using 52 road surface samples gathering on the spot as analyze data, by choose outward appearance, surface roughness, rutting depth, This 5 indexs of disease area, damaged condition carry out flatness quality evaluation using expert point rating method to it, and as ginseng Examine value.Then to these road surface samples are according to the GPS point coordinate of field survey and aid in high resolution image data in DSM Orientation on map is carried out on model, then according to surface evenness space quality spatial distribution map to the smooth of these road surface samples Degree credit rating is identified, then the recognition result of all road surface samples is counted, and thus obtains being used to carry out precision The confusion matrix (such as Figure 17) of evaluation.Finally calculate the overall classification accuracy for having obtained this method respectively using formula 21 and formula 22 It is 0.65 for 75%, Kappa coefficients, shows that the overall evaluation result of this method is substantially the same with actual result, and uniformity journey Degree is higher.
It should be noted that the purpose for publicizing and implementing example is that help further understands the present invention, but the skill of this area Art personnel are appreciated that：Do not departing from the present invention and spirit and scope of the appended claims, various substitutions and modifications are all can Can.Therefore, the present invention should not be limited to embodiment disclosure of that, and the scope of protection of present invention is with claim The scope that book is defined is defined.

Claims (10)

1. a kind of fusion unmanned plane LiDAR and high-resolution remote sensing image surface evenness monitoring method, it is characterized in that, it is based on LiDAR data and high-resolution remote sensing image that low latitude unmanned aerial vehicle platform is obtained, the spectral signature and LiDAR point cloud of fusion evaluation Multiple dimensioned geometric properties build characteristic of division collection；Road surface point cloud is recycled to build subtle three-dimensional road surface digital surface model (DSM) quick to calculate the flatness parameter for obtaining highway pavement, by international roughness index computational methods, combined ground is tested The evaluation that data carry out surface evenness grade is demonstrate,proved, the quick monitoring and evaluation of pavement quality is realized；Specifically include following steps：
1.1) first with integrated LiDAR and multispectral camera low latitude experiment of UAV remote sensing system, obtain LiDAR point cloud data and High-definition remote sensing image data；The LiDAR point cloud data are converted into .las file formats；To LiDAR point cloud data and Remote sensing image data is pre-processed, and is then based on image data and is extracted spectral signature；
1.2) spectral signature is fused in the attribute of the cloud data, multiple dimensioned geometry is carried out to the cloud data Feature extraction；By resulting geometric properties information and spectral signature information combination into point cloud classifications characteristic variable collection；
1.3) characteristic variable to the point cloud classifications carries out de-redundancy and dimensionality reduction, includes the search procedure and feature of character subset The evaluation procedure of subset, obtains optimal feature subset；The suitable machine learning classification method of reselection carries out point cloud classifications, enters one Step filtering and rough error, which are corrected, obtains more accurately road cloud data；
1.4) high-precision fine road surface model (DSM) is built by three-dimensional interpolation；
1.5) a series of buttock lines are chosen from obtained DSM model datas, international roughness index (IRI) model meter is utilized Calculation obtains the corresponding IRI values of each hatching, and passes through the spatial distribution map that rasterizing handles the IRI values for obtaining road surface；
1.6) combined ground measured data is demarcated, and the surface evenness that satisfies the need distinguishes different grades, represents pavement quality from difference To good type, remote sensing monitoring and the assessment of road pavement quality are realized.
2. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.1) especially by UAV flight LiDAR scanner devices are in flying overhead gathered data near the ground, and obtaining data includes LiDAR point cloud data and the high score of the same period Resolution image data；The high resolution image data are that digital camera shoots obtained high-definition digital photo, and with nobody The corresponding posture that machine is provided and location information file；The LiDAR point cloud data at least meet following condition：
2.1) coordinate system of the LiDAR point cloud data is WGS84 coordinate systems or the local plane projection matched with locality Coordinate system；
2.2) attribute information that the LiDAR point cloud packet contains includes longitude, latitude, elevation, scan angle, reflected intensity；
2.3) the point cloud density of the LiDAR point cloud data is more than 400 points/square metre.
3. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.1) pretreatment includes some cloud numbers According to pretreatment and high resolution image data pretreatment；Main pretreatment process includes：The noise spot mistake of LiDAR point cloud Filter；The abnormal elevation filtering of LiDAR point cloud；The scanning angular filter of LiDAR point cloud；The splicing of high resolution image with it is registering.
4. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.1) parameter of the spectral signature is Reflect the parameter of type of ground objects difference, including：The gray value of the red, green, blue wave band of high resolution image；High resolution image The standard deviation of the gray value of red, green, blue wave band；The reflection intensity values of laser point cloud.
5. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.2) data fusion is with a cloud On the basis of the georeferencing of data, the spectral signature of image is fused in a cloud genera；Specifically use affine inverse transformation model By in the band value information matches of each pixel on image to the cloud data coordinate of correspondence position.
6. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.3) geometric properties include it is local Roughness (LDR), local dimension feature (LDF) and local depth displacement (LHD)；The local roughness (LDR) is given directions in cloud Some reference point to the reference point where some space scale under the distance of best-fitting plane that is formed of neighborhood point； The local dimension feature (LDF) be give directions cloud in some reference point and this refer to neighborhood of a point point between be spatially with One-dimensional, two-dimentional or distributed in three dimensions characteristic measure value；The local depth displacement (LHD) be give directions cloud in some reference point with should All points that neighborhood point of the reference point under some space scale is constituted are concentrated, and are reached the best fit that the point set formed and are put down The ultimate range in face and the difference of minimum range.
7. surface evenness monitoring method as claimed in claim 1, it is characterized in that, the multiple dimensioned geometric properties are specially： When calculating some geometrical characteristic, multiple spatial analysis yardsticks are set；Fix some spatial analysis yardstick to be calculated, obtain one Individual corresponding space geometry characteristic parameter；Geometric properties of the different types of ground objects under different spatial analysis yardsticks are different；By This obtains multiple dimensioned geometric properties collection.
8. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.3) in, specifically, the dimensionality reduction enters The search of row character subset uses Greedy strategy；To in character subset, when the characteristic variable newly added is not only between classified variable Correlation it is high, while when the correlation again between the characteristic variable inside current subnet is relatively low, obtaining optimal feature subset； The machine learning classification method is classified using random forest sorting algorithm to sample point cloud, then by the classification of sample point cloud Attribute enters row interpolation by a cloud interpolation algorithm, obtains the classification of overall cloud data；When the classification of the overall cloud data When as a result comprising the point cloud for being not belonging to road surface, hand inspection is carried out to sorted cloud data and rough error is corrected, and further Filtered by category attribute, obtain more accurately road cloud data；Preferably, described cloud interpolation algorithm is adjacent using nature Domain method.
9. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.4) the fine road surface digital surface The raster resolution of model (DSM) should be controlled within 50mm, and height accuracy is within 15mm；Step 1.5) using international flat When whole degree index (IRI) model calculates evenness of road surface angle value, the sampling interval control within 50mm, measuring unit length for 10~ 20m。
10. surface evenness monitoring method as claimed in claim 1, it is characterized in that, step 1.6) obtain surface evenness i.e. IRI After value, flatness quality classification standard is set, classified according to road evenness quality classification standard road pavement；Further Precision evaluation is carried out using the method for classification of remote-sensing images precision evaluation, commented so as to obtain the corresponding flatness quality in section to be measured Valency result；The method of the precision evaluation comprises the following steps：
10.1) according to the flatness quality classification standard, in section to be measured, selection same period road surface sample is used as reference data；Institute State the same period road surface sample of selection comprising flatness quality preferably with poor, covering health and the road surface sample of Damage Types；Often Plant road surface sample and randomly choose multiple samples, be used as reference data；
10.2) outward appearance, surface roughness, rutting depth, disease area, damaged condition index are chosen to reference data, uses layer Fractional analysis (AHP) expert point rating method carries out flatness quality evaluation, will evaluate score value and is used as reference value；
10.3) the surface evenness monitoring method is used, the road surface sample is calculated and obtains flatness value；According to what is set Flatness quality classification standard, obtains the corresponding flatness classification results in road surface；
10.4) result is calculated using precision evaluation index, finally gives the monitoring of the surface evenness monitoring method Precision；The precision evaluation index includes overall accuracy (OA) or/and Kappa coefficients, and the value of the two is bigger to represent that precision is higher.
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