CN115014224A - LiDAR point cloud and oblique aerial image-based ground surface deformation monitoring method - Google Patents
LiDAR point cloud and oblique aerial image-based ground surface deformation monitoring method Download PDFInfo
- Publication number
- CN115014224A CN115014224A CN202210590180.8A CN202210590180A CN115014224A CN 115014224 A CN115014224 A CN 115014224A CN 202210590180 A CN202210590180 A CN 202210590180A CN 115014224 A CN115014224 A CN 115014224A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- point cloud
- urban
- lidar point
- aerial image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 116
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000007781 pre-processing Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000011161 development Methods 0.000 description 5
- 230000003595 spectral effect Effects 0.000 description 4
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000001788 irregular Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 244000025254 Cannabis sativa Species 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000005802 health problem Effects 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 230000000246 remedial effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64C—AEROPLANES; HELICOPTERS
- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D47/00—Equipment not otherwise provided for
- B64D47/08—Arrangements of cameras
-
- 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
- G01C11/02—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
- G01C11/025—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures by scanning the object
-
- 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
- G01C11/36—Videogrammetry, i.e. electronic processing of video signals from a single source or from different sources to give parallax or range information
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
-
- 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
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Multimedia (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Signal Processing (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a method for monitoring surface deformation based on LiDAR point cloud and oblique aerial image, which comprises the following steps: setting an urban ground surface monitoring zone, and dividing the urban ground surface monitoring zone to obtain a plurality of monitoring grids; acquiring LiDAR point cloud data of the monitoring grid; acquiring an oblique aerial image of the monitoring grid; constructing a monitoring grid earth surface model based on the LiDAR point cloud data and the oblique aerial image; splicing all the monitoring grid earth surface models to obtain an urban earth surface monitoring model; and comparing adjacent urban ground surface monitoring zone ground surface models within preset time, judging whether ground surface deformation occurs, if so, generating a ground surface deformation report and giving an early warning, and if not, continuing monitoring. The urban ground surface deformation monitoring system can comprehensively, accurately and visually monitor and early warn urban ground surface deformation, and further reduce various losses.
Description
Technical Field
The invention relates to the field of surface deformation monitoring, in particular to a surface deformation monitoring method based on LiDAR point cloud and oblique aerial image.
Background
With the diversification and deep and rapid development of economy, the regional economy presents strong development power, a world-level city group is built, and numerous opportunities are provided for the development of the regional economy. The structural health problems of urban infrastructure are directly related to the sustainable development of urban environment and social economy. With the accelerated construction progress of the regional economic zone, the problems of urban ground settlement and infrastructure deformation in the regional economic zone become more serious and become a new urban disease, which mainly comprises the following steps: deformation and collapse of buildings, urban land subsidence, sea reclamation area subsidence, railway and highway subsidence, deformation of the surface of the earth along the pipeline facilities, deformation of the surface of the earth in dam and reservoir areas, surface subsidence caused by underground water mining, oil extraction and mining, landslide and displacement of artificial slopes. The direct economic loss of geological disasters caused by ground settlement reaches billions of yuan every year, and the indirect loss caused by interrupting traffic and destroying production and living facilities is difficult to estimate.
When the urban ground surface deformation is monitored, the urban ground surface three-dimensional or two-dimensional model is obtained directly and repeatedly by means of aerial shooting of the city and by means of LiDAR data or vertical aerial shooting, and the difference between the models before and after comparison is used for finding whether the ground surface is deformed or not, but buildings in the city are large in quantity, dense and different in shape, no matter the LiDAR data or the vertical aerial shooting generate deviation when the urban ground surface model is established, and the accuracy of a judgment result is reduced.
Therefore, a comprehensive earth surface deformation monitoring method is urgently needed, which monitors urban earth surface deformation and gives out early warning, and remedial measures are taken as soon as possible to reduce social, economic and other losses.
Disclosure of Invention
The invention aims to provide a method for monitoring surface deformation based on LiDAR point cloud and inclined aerial image, which aims to solve the problems in the prior art, combines the LiDAR point cloud and the inclined aerial image and improves the effectiveness of monitoring the surface deformation.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a LiDAR point cloud and inclined aerial image-based ground surface deformation monitoring method, which comprises the following steps of:
setting an urban ground surface monitoring zone, and dividing the urban ground surface monitoring zone to obtain a plurality of monitoring grids;
acquiring LiDAR point cloud data of the monitoring grid;
acquiring an oblique aerial image of the monitoring grid;
constructing a monitoring grid earth surface model based on the LiDAR point cloud data and the oblique aerial image;
splicing all the monitoring grid earth surface models to obtain an urban earth surface monitoring model;
and comparing adjacent urban ground surface monitoring zone ground surface models within preset time, judging whether ground surface deformation occurs, if so, generating a ground surface deformation report and giving an early warning, and if not, continuing monitoring.
Optionally, setting an urban ground surface monitoring zone, dividing the urban ground surface monitoring zone, and obtaining a plurality of monitoring grids includes:
acquiring a latest urban aerial landform image, and identifying ground attachment information of the urban aerial landform image;
and dividing the urban ground surface monitoring zone according to the ground attachment information and the performance of the data acquisition device to obtain a plurality of monitoring grids.
Optionally, the ground attachment information includes an attachment type and an attachment shape, wherein the attachment type includes a building, a flat ground, a mountain land, and a water area.
Optionally, acquiring the oblique aerial image of the monitoring grid comprises:
setting a shooting route, a shooting angle and a shooting object point;
and acquiring the inclined aerial image of the monitoring grid based on the shooting route, the shooting angle and the shooting object point, wherein the inclined aerial image comprises strip-shaped inclined aerial photographic data and annular inclined aerial photographic data.
Optionally, constructing a monitoring grid surface model based on the LiDAR point cloud data and the oblique aerial image comprises:
preprocessing the LiDAR point cloud data to obtain target LiDAR point cloud data, and constructing an urban earth surface digital elevation model by using the target LiDAR point cloud data;
preprocessing the oblique aerial image, and constructing an urban earth surface digital three-dimensional model by utilizing the preprocessed oblique aerial image;
and registering the urban earth surface digital elevation model and the urban earth surface digital three-dimensional model to construct a monitoring grid earth surface model.
Optionally, the LiDAR point cloud data preprocessing comprises:
removing noise values and abnormal values in the LiDAR point cloud data to obtain effective LiDAR point cloud data;
and filtering the effective LiDAR point cloud data to finish the pretreatment of the LiDAR point cloud data.
Optionally, the preprocessing the oblique aerial image comprises:
and removing a noise image in the oblique aerial image, and carrying out image dodging processing and spatial triphase difference processing on the reserved oblique aerial image.
Optionally, registering the urban surface digital elevation model and the urban surface digital three-dimensional model, and constructing a monitoring grid surface model includes:
extracting markers in the digital three-dimensional model of the urban ground surface, selecting characteristic points of a plurality of markers, obtaining pixel coordinates of the characteristic points of the markers, obtaining LiDAR point cloud coordinates of the characteristic points, fusing the pixel coordinates and the LiDAR point cloud coordinates by using a gray-scale weighting method to obtain marking information of all the characteristic points, and constructing the monitoring grid ground surface model by using all the marking information.
Optionally, the comparing the adjacent urban surface monitoring zone surface models within a preset time includes:
selecting the same monitoring object in the urban ground surface monitoring zone ground surface model at the adjacent time, randomly selecting the monitoring mark points of the monitoring object, respectively obtaining the mark information of the monitoring mark points in the urban ground surface monitoring zone ground surface model, and comparing the change of the mark information.
The invention discloses the following technical effects:
according to the method for monitoring the earth surface deformation of the LiDAR point cloud and the oblique aerial image, the elevation data of the LiDAR point cloud and the spectral characteristics of the oblique aerial image are fused, whether the ground in an urban group is deformed or not and the deformation degree are comprehensively monitored, the one-sidedness and the hysteresis of single-method monitoring are overcome, and the accuracy and the effectiveness of monitoring the earth surface deformation of the urban group are remarkably improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 shows an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
The invention provides a method for monitoring surface deformation based on LiDAR point cloud and oblique aerial image, as shown in figure 1.
The LiDAR point cloud data are laser signals transmitted to the ground through an airborne laser radar, then the laser signals reflected by the ground are collected, and accurate spatial information of the points is obtained through joint calculation and deviation correction.
The oblique aerial image obtains the oblique image of the same ground feature different angles through the shooting of multi-angle to acquire the side texture of the ground feature that traditional aerial photogrammetry can not acquire, utilize the oblique aerial image can accurately obtain the appearance characteristic of multi-angle ground attachment.
And setting an urban ground surface monitoring zone, dividing the urban ground surface monitoring zone, and obtaining a plurality of monitoring grids.
Since the monitoring object in this embodiment is a large-scale urban area, the area is large, and the ground attachments are different in type and cross, which causes great difficulty in monitoring. And because adopt unmanned aerial vehicle to carry out information acquisition in the air in this embodiment, because the restriction of the electrified volume of unmanned aerial vehicle, can't carry out the long-time information acquisition activity of large tracts of land, consequently, in this embodiment, use city earth's surface attachment type as the basis to combine the unmanned aerial vehicle performance of taking photo by plane to divide city crowd monitoring area, form a plurality of city monitoring grids, use monitoring grid as the object, carry out city earth's surface information acquisition in proper order or many unmanned aerial vehicles simultaneously. Since the city is a gathering place of human activities, in order to meet the needs of human life and work, the city group comprises various ground attachments such as buildings, grasslands, mountains, rivers, lakes and the like, and when information is acquired, different ground attachments have different influence degrees on the information acquisition device, so that when the grid division is carried out, the grid division rule is met, and meanwhile, the ground attachments of the same kind in the region are taken as a reference. If grass is present in a plurality of building groups, the grass is divided into the building groups as targets.
And collecting the point cloud data of the LiDAR on the urban ground surface in the monitoring grid range.
After city monitoring meshing is accomplished, carry on laser radar to unmanned aerial vehicle on, according to the kind of ground attachment in this monitoring grid, set up unmanned aerial vehicle's flight route and the flying height of different regions, set up laser radar's working parameter, start unmanned aerial vehicle and laser radar and gather the original city earth's surface LiDAR point cloud data in the detection grid according to the flight planning.
And acquiring the urban ground surface inclined aerial image of the monitoring grid.
Carry on high definition camera simultaneously on unmanned aerial vehicle for gather original city earth's surface slope aerial image. Because high definition camera and laser radar carry on to an unmanned aerial vehicle, consequently only need set up the shooting angle and the shooting object point of high definition camera, the shooting object point is the target point of pointer to the monitoring object surface, like building outer surface number, position. The technical scheme of strip-shaped oblique aerial photography and annular oblique aerial photography is adopted in the shooting process, the high-definition camera lens angle is controlled by using an unmanned aerial vehicle flight control system during strip-shaped oblique aerial photography, aerial image data are respectively acquired in 5 directions of vertical downward direction, forward view 45 degrees, backward view 45 degrees, left view 45 degrees, right view 45 degrees and the like, and strip-shaped oblique photography measurement data of a monitoring area are formed. In the unmanned aerial vehicle annular image data acquisition, an unmanned aerial vehicle is used for carrying out surrounding shooting on buildings with complex structures in a detection grid, and building images are uniformly acquired from the periphery of a target.
And constructing a grid earth surface model by utilizing the collected LiDAR point cloud data of the original city earth surface and the collected inclined aerial image of the original city earth surface.
To ensure the accuracy of the model, the collected information is first preprocessed. And screening the noise value and the abnormal value of the original city ground surface LiDAR point cloud data, and removing the screened noise value and the abnormal value to obtain effective LiDAR point cloud data. The noise values and abnormal values include weak echo signals, abnormal laser noise points caused by multiple reflections or refractions. Filtering the effective urban surface LiDAR point cloud data by using an irregular triangulation network (TIN) -based encryption filtering algorithm to obtain final LiDAR point cloud data. Specifically, all airborne LiDAR point cloud data is generated into a coarse scale grid, the lowest point in the range is read out in each grid range, and a sparse coarse scale TIN is generated through a plurality of lower seed points. And calculating the distance d between each new three-dimensional point and the TIN, connecting the three points of the triangular net with the additional angles a, B and y of the triangular surface, judging whether the three points meet the range of a given threshold value, if so, regenerating the triangular net of the information, and otherwise, removing the point.
And performing data interpolation encryption processing on the final LiDAR point cloud data by using a natural adjacent point interpolation method, and after performing regular grid networking processing on the final LiDAR ground point cloud data, converting the point cloud data into a regularly distributed matrix form from the original irregular distribution. And constructing an urban earth surface digital elevation model by using LiDAR ground point cloud data in a matrix form.
And preprocessing the original urban ground surface inclined aerial image, including removing a noise image, wherein the noise image comprises a blurred image, an overexposed image and the like. The light transmission route in the atmosphere is changed from a regular straight line to an irregular curve due to the refraction of the atmosphere to the light, so that the generated image can be displaced and deformed. Especially, in the case of an oblique image with a large shooting angle, image displacement, chromatic aberration and deformation are more obvious, and the atmospheric medium can absorb the energy of light, so that the light propagation speed is slowed down to cause the brightness to be weakened. In oblique photography, the contrast, tone, chromatic aberration and definition of an image are directly affected by refraction, scattering and absorption of light by the atmosphere, so that in most cases, a certain degree of tone difference exists between an oblique image and a bottom-view image. Based on Smart3DCapture software, matching the acquired feature points with the homonymous points by adopting multiple visual angles, and then reversely calculating the space position of each picture and the attitude angle of the picture, thereby determining the relation between the pictures and carrying out space-triplet encryption. And constructing a digital three-dimensional model of the urban earth surface by using the oblique aerial image after the aerial image encryption.
Extracting markers in the digital three-dimensional model of the urban earth surface, selecting characteristic points of a plurality of markers, selecting all the points highlighting the shapes of the markers according to the appearance shape characteristics of the markers, such as building protrusions, outer contours, building depressions and the like, acquiring pixel coordinates of the characteristic points, acquiring LiDAR point cloud coordinates of the characteristic points, and fusing the pixel coordinates and the LiDAR point cloud coordinates by utilizing a gray weighting method. The process of weighted average fusion of gray values of pixels of A, B two source images can be expressed as:
J(m，n)＝I 1 P(m，n)+I 2 Q(m，n) (1)
in the formula, m and n respectively represent the row number and the column number of the image, I 1 ，I 2 As weighting factors for the image A, B, typically I 1 +I 2 If P (m, n), Q (m, n), and J (m, n) are gray values of an image, P is airborne LiDAR point cloud data and Q is oblique aerial image data for the fusion, I is spectral information 1 ＝0，I 2 1,; for spatial information, I 1 ＝1，I 2 0. Because the density of the point cloud data is far less than the pixel density, the situation that the values of a plurality of pixels are assigned to the same point does not occur. That is, through the registration of the point cloud data and the image data, one point cloud data corresponds to one pixel data item, the mark information of all the characteristic points is obtained, and a monitoring grid earth surface model is constructed by using all the mark information. Monitoring in a grid earth modelThe marker information in each object is a 7-dimensional matrix, and the attributes of airborne LiDAR point cloud data include spatial three-dimensional coordinates x, y, z and light intensity values i and spectral attributes (R, G, B) of oblique aerial image data, so that in the embodiment, the spectral characteristics of the image and the position attributes of the LiDAR point cloud data are fused, and an intuitive urban surface model is obtained while the elevation coordinate value of each observation point in the surface attachment can be obtained.
And splicing all the monitoring grid earth surface models to obtain the urban earth surface monitoring model.
And arranging the obtained monitoring grid earth surface models according to the positions of the monitoring grid earth surface models in the urban earth surface monitoring zone, and forming the urban earth surface monitoring model by all the monitoring grid earth surface models.
According to the geological environment of the area where the city is located and the city development process, setting the time interval for collecting the city surface information, regularly collecting the city surface information according to the time interval, and constructing a city surface monitoring model. Selecting two urban surface monitoring models with the nearest time for comparison, specifically, randomly selecting a plurality of different types of surface attachments and surface attachments of major concern, randomly selecting a specified number of observation points on the selected surface attachments, when urban ground settlement and infrastructure deformation problems occur, changing the elevation information of the attachments, comparing the elevation information of the observation points in the front and rear urban surface monitoring models, judging whether the elevation information of the positions of the attachments changes, if so, generating a surface deformation report which indicates the change amount and the change speed, giving out early warning according to the change amount and the change speed, and if not, continuing monitoring.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the following descriptions are only illustrative and not restrictive, and that the scope of the present invention is not limited to the above embodiments: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the scope of the disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A ground surface deformation monitoring method based on LiDAR point cloud and inclined aerial image is characterized in that: the method comprises the following steps:
setting an urban ground surface monitoring zone, and dividing the urban ground surface monitoring zone to obtain a plurality of monitoring grids;
acquiring LiDAR point cloud data of the monitoring grid;
acquiring an oblique aerial image of the monitoring grid;
constructing a monitoring grid earth surface model based on the LiDAR point cloud data and the oblique aerial image;
splicing all the monitoring grid earth surface models to obtain an urban earth surface monitoring model;
and comparing the adjacent urban ground surface monitoring zone ground surface models within preset time, judging whether ground surface deformation occurs, if so, generating a ground surface deformation report and giving an early warning, and if not, continuing monitoring.
2. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 1, wherein: setting a city surface monitoring zone, dividing the city surface monitoring zone, and obtaining a plurality of monitoring grids comprises:
acquiring a latest urban aerial landform image, and identifying ground attachment information of the urban aerial landform image;
and dividing the urban ground surface monitoring zone according to the ground attachment information and the performance of the data acquisition device to obtain a plurality of monitoring grids.
3. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 2, wherein: the ground attachment information includes an attachment type and an attachment shape, wherein the attachment type includes a building, a flat ground, a mountain land, and a water area.
4. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 1, wherein: acquiring the oblique aerial image of the monitoring grid comprises:
setting a shooting route, a shooting angle and a shooting object point;
and acquiring the inclined aerial image of the monitoring grid based on the shooting route, the shooting angle and the shooting object point, wherein the inclined aerial image comprises strip-shaped inclined aerial photographic data and annular inclined aerial photographic data.
5. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 1, wherein: constructing a monitoring grid earth surface model based on the LiDAR point cloud data and the oblique aerial image comprises:
preprocessing the LiDAR point cloud data to obtain target LiDAR point cloud data, and constructing an urban earth surface digital elevation model by using the target LiDAR point cloud data;
preprocessing the oblique aerial image, and constructing an urban ground surface digital three-dimensional model by utilizing the preprocessed oblique aerial image;
and registering the urban earth surface digital elevation model and the urban earth surface digital three-dimensional model to construct a monitoring grid earth surface model.
6. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 5, wherein: the LiDAR point cloud data preprocessing comprises:
removing noise values and abnormal values in the LiDAR point cloud data to obtain effective LiDAR point cloud data;
and filtering the effective LiDAR point cloud data to finish the pretreatment of the LiDAR point cloud data.
7. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 5, wherein: the preprocessing the oblique aerial image comprises the following steps:
and removing a noise image in the oblique aerial image, and carrying out image dodging processing and spatial triphase difference processing on the reserved oblique aerial image.
8. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 5, wherein: registering the urban surface digital elevation model and the urban surface digital three-dimensional model, and constructing a monitoring grid surface model comprises the following steps:
extracting markers in the digital three-dimensional model of the urban ground surface, selecting characteristic points of a plurality of markers, obtaining pixel coordinates of the characteristic points of the markers, obtaining LiDAR point cloud coordinates of the characteristic points, fusing the pixel coordinates and the LiDAR point cloud coordinates by using a gray-scale weighting method to obtain marking information of all the characteristic points, and constructing the monitoring grid ground surface model by using all the marking information.
9. The LiDAR point cloud and oblique aerial image-based surface deformation monitoring method of claim 8, wherein: comparing the adjacent urban ground surface monitoring zone ground surface models within a preset time comprises the following steps:
selecting the same monitoring object in the urban ground surface monitoring zone ground surface model at the adjacent time, randomly selecting the monitoring mark points of the monitoring object, respectively obtaining the mark information of the monitoring mark points in the urban ground surface monitoring zone ground surface model, and comparing the change of the mark information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210590180.8A CN115014224B (en) | 2022-05-27 | 2022-05-27 | Ground surface deformation monitoring method based on LiDAR point cloud and inclined aerial image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210590180.8A CN115014224B (en) | 2022-05-27 | 2022-05-27 | Ground surface deformation monitoring method based on LiDAR point cloud and inclined aerial image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115014224A true CN115014224A (en) | 2022-09-06 |
| CN115014224B CN115014224B (en) | 2023-05-02 |
Family
ID=83070051
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210590180.8A Active CN115014224B (en) | 2022-05-27 | 2022-05-27 | Ground surface deformation monitoring method based on LiDAR point cloud and inclined aerial image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115014224B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115619922A (en) * | 2022-10-11 | 2023-01-17 | 江苏中源城乡规划设计有限公司 | Industrial enterprise land survey method based on three-dimensional live-action modeling technology |
| CN116261121A (en) * | 2023-05-05 | 2023-06-13 | 山东省地质矿产勘查开发局八〇一水文地质工程地质大队(山东省地矿工程勘察院) | Unmanned aerial vehicle geological mapping data transmission method and system |
| CN117346742A (en) * | 2023-10-09 | 2024-01-05 | 广东省核工业地质局测绘院 | Hydropower station mapping system based on airborne laser radar and oblique photogrammetry |
| CN117572455A (en) * | 2023-11-24 | 2024-02-20 | 齐鲁空天信息研究院 | Mountain reservoir topographic map mapping method based on data fusion |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020025984A1 (en) * | 2018-08-01 | 2020-02-06 | Pantazis Alexandros | Method of use of a lidar device and operatively associated lidar data processing unit for providing real-time monitoring of meteorological parameters |
| CN111415409A (en) * | 2020-04-15 | 2020-07-14 | 北京煜邦电力技术股份有限公司 | Modeling method, system, equipment and storage medium based on oblique photography |
| CN111458720A (en) * | 2020-03-10 | 2020-07-28 | 中铁第一勘察设计院集团有限公司 | Airborne laser radar data-based oblique photography modeling method for complex mountainous area |
| CN111928824A (en) * | 2020-08-07 | 2020-11-13 | 贵州正业工程技术投资有限公司 | Engineering investigation method combining laser radar and oblique photography |
| CN112270251A (en) * | 2020-10-26 | 2021-01-26 | 清华大学 | Self-adaptive multi-sensor data fusion method and system based on mutual information |
| CN113012398A (en) * | 2021-02-20 | 2021-06-22 | 中煤航测遥感集团有限公司 | Geological disaster monitoring and early warning method and device, computer equipment and storage medium |
| AU2021240246A1 (en) * | 2014-01-31 | 2021-10-28 | Pictometry International Corp. | Augmented three dimensional point collection of vertical structures |
| CN113611082A (en) * | 2021-07-12 | 2021-11-05 | 北京铁科特种工程技术有限公司 | Unmanned aerial vehicle railway slope monitoring and early warning system and method |
| CN113628341A (en) * | 2021-08-31 | 2021-11-09 | 时空云科技有限公司 | Automatic single modeling method based on oblique photography data and LIDAR point cloud fusion |
-
2022
- 2022-05-27 CN CN202210590180.8A patent/CN115014224B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2021240246A1 (en) * | 2014-01-31 | 2021-10-28 | Pictometry International Corp. | Augmented three dimensional point collection of vertical structures |
| WO2020025984A1 (en) * | 2018-08-01 | 2020-02-06 | Pantazis Alexandros | Method of use of a lidar device and operatively associated lidar data processing unit for providing real-time monitoring of meteorological parameters |
| CN111458720A (en) * | 2020-03-10 | 2020-07-28 | 中铁第一勘察设计院集团有限公司 | Airborne laser radar data-based oblique photography modeling method for complex mountainous area |
| CN111415409A (en) * | 2020-04-15 | 2020-07-14 | 北京煜邦电力技术股份有限公司 | Modeling method, system, equipment and storage medium based on oblique photography |
| CN111928824A (en) * | 2020-08-07 | 2020-11-13 | 贵州正业工程技术投资有限公司 | Engineering investigation method combining laser radar and oblique photography |
| CN112270251A (en) * | 2020-10-26 | 2021-01-26 | 清华大学 | Self-adaptive multi-sensor data fusion method and system based on mutual information |
| CN113012398A (en) * | 2021-02-20 | 2021-06-22 | 中煤航测遥感集团有限公司 | Geological disaster monitoring and early warning method and device, computer equipment and storage medium |
| CN113611082A (en) * | 2021-07-12 | 2021-11-05 | 北京铁科特种工程技术有限公司 | Unmanned aerial vehicle railway slope monitoring and early warning system and method |
| CN113628341A (en) * | 2021-08-31 | 2021-11-09 | 时空云科技有限公司 | Automatic single modeling method based on oblique photography data and LIDAR point cloud fusion |
Non-Patent Citations (2)
| Title |
|---|
| 向云飞等: "基于Lidar数据和倾斜摄影的城市三维模型构建", 《测绘工程》 * |
| 谢奇宇: "基于机载LiDAR和倾斜摄影测量的城市实景三维建模", 《测绘》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115619922A (en) * | 2022-10-11 | 2023-01-17 | 江苏中源城乡规划设计有限公司 | Industrial enterprise land survey method based on three-dimensional live-action modeling technology |
| CN115619922B (en) * | 2022-10-11 | 2023-11-21 | 江苏中源城乡规划设计有限公司 | Industrial enterprise land investigation method based on three-dimensional live-action modeling technology |
| CN116261121A (en) * | 2023-05-05 | 2023-06-13 | 山东省地质矿产勘查开发局八〇一水文地质工程地质大队(山东省地矿工程勘察院) | Unmanned aerial vehicle geological mapping data transmission method and system |
| CN117346742A (en) * | 2023-10-09 | 2024-01-05 | 广东省核工业地质局测绘院 | Hydropower station mapping system based on airborne laser radar and oblique photogrammetry |
| CN117572455A (en) * | 2023-11-24 | 2024-02-20 | 齐鲁空天信息研究院 | Mountain reservoir topographic map mapping method based on data fusion |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115014224B (en) | 2023-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115014224B (en) | Ground surface deformation monitoring method based on LiDAR point cloud and inclined aerial image | |
| CN113034689B (en) | Laser point cloud-based terrain three-dimensional model, terrain map construction method and system, and storage medium | |
| CN101975951B (en) | Field environment barrier detection method fusing distance and image information | |
| Chen et al. | High-resolution monitoring of beach topography and its change using unmanned aerial vehicle imagery | |
| CN106327573A (en) | Real scene three-dimensional modeling method for urban building | |
| TW200929067A (en) | 3D image detecting, editing and rebuilding system | |
| CN113432549B (en) | Tidal trench three-dimensional parameter extraction method and system based on unmanned aerial vehicle photogrammetry | |
| CN116468869A (en) | Live-action three-dimensional modeling method, equipment and medium based on remote sensing satellite image | |
| CN114295069A (en) | Side slope deformation monitoring method and system for unmanned aerial vehicle carrying three-dimensional laser scanner | |
| Jaboyedoff et al. | Mapping and monitoring of landslides using LiDAR | |
| Andaru et al. | The use of UAV remote sensing for observing lava dome emplacement and areas of potential lahar hazards: An example from the 2017–2019 eruption crisis at Mount Agung in Bali | |
| Pfeifer et al. | LiDAR data filtering and digital terrain model generation | |
| CN111964599A (en) | Highway high slope surface deformation monitoring and analyzing method based on oblique photogrammetry technology | |
| CN116448080B (en) | Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method | |
| Griesbaum et al. | Direct local building inundation depth determination in 3-D point clouds generated from user-generated flood images | |
| Brook et al. | Monitoring active landslides in the Auckland region utilising UAV/structure-from-motion photogrammetry | |
| CN116778097A (en) | Site design method based on unmanned aerial vehicle oblique photography technology and BIM technology | |
| CN116912443A (en) | Mining area point cloud and image fusion modeling method using unmanned aerial vehicle aerial survey technology | |
| Miller | Analysis of Viewshed Accuracy with Variable Resolution LIDAR Digital Surface Models and Photogrammetrically-Derived Digital Elevation Models | |
| CN116030116A (en) | Landfill volume analysis method and device, electronic equipment and storage medium | |
| Seong et al. | UAV Utilization for Efficient Estimation of Earthwork Volume Based on DEM | |
| Liao et al. | Unmanned aerial vehicle surveying and mapping trajectory scheduling and autonomous control for landslide monitoring | |
| Pagán et al. | 3D modelling of dune ecosystems using photogrammetry from remotely piloted air systems surveys | |
| CN114972358B (en) | Artificial intelligence-based urban surveying and mapping laser point cloud offset detection method | |
| Hnila et al. | Quality Assessment of Digital Elevation Models in a Treeless High-Mountainous Landscape: A Case Study from Mount Aragats, Armenia |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |