CN111473776A - Landslide crack monitoring method based on single-image close-range photogrammetry - Google Patents
Landslide crack monitoring method based on single-image close-range photogrammetry Download PDFInfo
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Abstract
The invention belongs to the field of geological disaster monitoring, and discloses a landslide crack monitoring method based on single-image close-range photogrammetry, which comprises the following steps of: s1, acquiring images of the monitoring area in real time through a camera; s2, comparing the real-time image of the monitored image with the initial image, respectively obtaining the feature points of the real-time image and the initial image by using an SIFI algorithm, and matching the feature points to obtain the homonymous feature points of the real-time image and the initial image; s3, establishing a two-dimensional field displacement vector model based on the homonymous feature points of the initial image and the real-time image and detecting an active area based on the gray level image so as to obtain a landslide crack area; and S5, calculating the width of the landslide crack according to the coordinates in the ground photogrammetry coordinate system corresponding to the homonymous feature points in the landslide area in the initial image and the real-time image. The invention has the advantages of high precision, no contact, wide monitoring range, visual monitoring result and the like.
Description
Technical Field
The invention belongs to the field of geological disaster monitoring, and particularly relates to a landslide crack monitoring method based on close-range photogrammetry.
Background
The land area of China is vast, the geographic environment is complex, and geological disasters such as landslides occur frequently, so that the method is one of countries affected by the most serious geological disasters in the world. Landslide disasters often cause villages to be buried, road and river channels are blocked, the living environment of people is seriously affected, and the life and property safety of people is greatly threatened. Therefore, it is very important to enhance the deformation monitoring of the slope. The slope deformation monitoring can provide a technical basis for preventing and controlling landslide and possible sliding and creeping deformation, and predict and forecast the development trend of displacement and deformation of the slope in the future. However, the technology used in the current monitoring scheme is not mature enough, and part of the monitoring equipment and instruments are high in cost and cannot be widely applied. The research on monitoring technology and method must be continuously carried out, so that the monitoring cost is reduced and the application range of the monitoring technology is improved. Slope deformation monitoring techniques are of various types. Available monitoring methods are various, and commonly used methods include a conventional geodetic measurement method, a GPS detection method, a three-dimensional laser scanning method, an instrument observation method, a radar observation method, a close-range photogrammetry method, and the like. However, except for the GPS and InSAR methods, data acquisition is mainly completed manually, the observation period is long, and the data acquisition is greatly affected by external conditions such as climate and environment. The monitoring and inspection work needs to be carried out on the landslide body or on the periphery of the landslide, and once the landslide slides, the personal safety of monitoring and inspection personnel cannot be guaranteed. The monitoring data can not be processed in time, the monitoring result has delay, and early warning can not be carried out in time. Except InSAR, the monitoring method can not realize real-time acquisition of monitoring data based on surfaces, and brings difficulty to landslide prediction. Chinese patent 110514113a discloses a "landslide crack monitoring method based on monocular vision camera" using close-range photogrammetry method, which realizes non-contact monitoring of landslide cracks. However, this method has the following problems: 1) the monitoring object is an existing landslide crack, and the possible cracks cannot be monitored; 2) the method is characterized in that a round mark steel chisel needs to be arranged at the crack, the change of the crack is monitored by monitoring the displacement change of the round mark, the change condition of the whole crack cannot be directly monitored, and once the round mark is damaged, the monitoring cannot be carried out; 3) the distribution density of the circular marks influences the accuracy of monitoring results, extensive monitoring based on surfaces cannot be realized, and the phenomenon of monitoring missing may exist.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a landslide crack monitoring method based on single-image close-range photogrammetry, so as to solve the defects of image monitoring in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that: a landslide crack monitoring method based on single-image close-range photogrammetry comprises the following steps:
s1, acquiring images of the monitoring area in real time through a camera;
s2, comparing the real-time image of the monitored image with the initial image, respectively obtaining the feature points of the real-time image and the initial image by using an SIFI algorithm, and matching the feature points to obtain the homonymous feature points of the real-time image and the initial image;
s3, subtracting the coordinates of image points of the homonymous feature points of the initial image and the real-time image to obtain a displacement vector of the homonymous feature points, if the displacement vector is larger than a displacement threshold value, judging that the point is displaced, then constructing a Delaunay triangulation network by using the detected displacement point, and defining a landslide body area A1; subtracting the gray values of corresponding pixels of the initial image and the real-time image, constructing a Delaunay triangulation network by using the pixels of which the gray difference values are greater than the gray threshold value, and delineating a landslide range area A2;
s4, coupling a landslide body area A1 with a landslide range area A2 to obtain a landslide crack area A3, wherein A3 is A2-A1;
s5, obtaining a slope surface equation of a landslide body before and after a landslide by using three-dimensional space coordinates of a plurality of ground feature points arranged on the slope surface and a least square plane fitting method; then calculating to obtain space coordinates corresponding to image point coordinates of all the characteristic points with the same name on the initial image and the real-time image by a linear transformation method; and calculating to obtain the width of the landslide crack according to the space coordinates corresponding to the image point coordinates of the homonymous feature points in the landslide body region in the initial image and the real-time image.
Further, before the step S1, the method further includes the following steps:
determining the focal length, the area array size and the monitoring area of the camera, calculating the shooting distance D from the camera to a landslide body, wherein D is (f × H)/H, f is the focal length of the camera, H is the shooting height range, H is the height of a CCD (charge coupled device) element of the camera, measuring the height and the width of the monitoring area, determining the monitoring area, further determining the spatial position where the camera is arranged, fixing the camera (2) through a forced centering pile (1), and enabling the forced centering pile (1) to be used for obtaining the spatial coordinate information of the shooting center of the camera.
Further, in step S5, the specific method for calculating the spatial coordinates corresponding to the image point coordinates of the feature points with the same name in the initial image and the real-time image includes:
selecting and calibrating 6 ground object points on the slope, obtaining image point coordinates of the 6 ground object points in the initial image and the real-time image and three-dimensional space coordinates of corresponding space positions before and after the landslide, and calculating to obtain a direct linear transformation coefficient l between an image point coordinate system before and after the landslide and a three-dimensional space coordinate system in a ground photogrammetry coordinate systemi' (i ═ 1,2, …,11) and l "i(i ═ 1,2, …,11), the calculation formula is:
wherein, (X ' n, Y ' n) represents the image point coordinates of the feature point, (X ' n, Y ' n, Z ' n) represents the three-dimensional space coordinates of the feature point at the space position, and n is 1,2,3,4,5,6, and represents the feature point number;
then, according to the direct linear transformation basic relation, the slope equation is established in parallel, and all homonymous characteristic points are calculated and obtained in the initial graphCoordinates (x) of image points1，y1) Corresponding spatial coordinate (X)D1、YD1、ZD1) And coordinates (x) of image points in real-time images2，y2) Corresponding spatial coordinate (X)D2、YD2、ZD2) The calculation formula is as follows:
wherein, a1,b1,c1Coefficient of slope equation, D, representing the body of the landslide front1Constant of the slope equation, a, representing the body of the landslide preceding the landslide2,b2,c2Coefficient of slope equation, D, representing the body of the landslide after landslide2And the slope equation constant of the landslide body after landslide is expressed.
In step S5, the equation of the slope of the landslide mass is:
aX+bY+cZ+D＝0；
in the formula, a, b and c are slope equation coefficients of a sliding mass, and D is a slope equation constant.
Further, the landslide crack monitoring method based on single-image close-range photogrammetry further comprises the steps of uniformly selecting a plurality of ground feature points on the monitoring slope surface, measuring three-dimensional space coordinates of the ground feature points before and after landslide, and measuring the three-dimensional space coordinates through a total station.
Further, the calculation formula of the width of the landslide crack is as follows:
where d represents the landslide crack width.
Further, the displacement threshold value is zero, and the gray level threshold value is zero.
Compared with the prior art, the invention has the following beneficial effects: the invention provides a landslide crack monitoring method based on single-image close-range photogrammetry, which is characterized in that a camera is erected in a monitoring area, the obtained monitoring image is used for carrying out landslide crack monitoring on the monitoring area, a real-time image of the monitoring image is compared with an initial image, characteristic points of the real-time image and the initial image are respectively obtained by using an SIFI algorithm, and characteristic point matching is carried out to obtain homonymous characteristic points of the real-time image and the initial image; establishing a two-dimensional field displacement vector model and detecting an active area based on a gray image based on the homonymous feature points of the initial image and the real-time image, and further obtaining a landslide crack area; and finally, calculating to obtain the width of the landslide crack according to the coordinates in the ground photogrammetry coordinate system corresponding to the homonymous feature points in the landslide region in the initial image and the real-time image. Displacement monitoring and crack area delineation are realized. The invention has simple monitoring structure, can realize non-contact monitoring by using close-range photogrammetry technology based on the monitoring range of the surface, can accurately define a sliding area and has strong visibility.
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Fig. 1 is a schematic view of a landslide crack monitoring method based on single-image close-range photogrammetry according to an embodiment of the present invention;
FIG. 2 is a detailed flow chart of landslide monitoring according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a two-dimensional field displacement vector model based on image feature points according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a two-dimensional field displacement vector model delineation result based on image feature points according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an active region detection model based on grayscale images according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a result of a delineation of an active region detection model based on a gray-scale image according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a landslide crack delineation result based on monoscopic close-range photogrammetry in an embodiment of the present invention;
fig. 8 is a flowchart of the SIFT feature matching algorithm.
In the figure, 1 is a forced centering pile, 2 cameras, 3 is a slope surface, A1 is a landslide body area, A2 is a landslide range area, and A3 is a landslide crack area.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all 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.
The embodiment of the invention provides a landslide crack monitoring method based on single-image close-range photogrammetry. Obtaining homonymous feature points of the images before and after slippage by using an SIFT (Scale-invariant feature transform) algorithm; and calculating the coordinates of the homonymous points in the ground photogrammetry coordinate system by using the obtained image plane coordinates of the homonymous feature points, calculating the displacement vectors which are correctly matched with the homonymous points, obtaining the space displacement vectors, and constructing a displacement vector field.
Specifically, as shown in fig. 1 and 2, the embodiment of the present invention specifically includes the following steps:
determining a focal length, an area array size and a monitoring area of a camera, calculating a shooting distance from the camera to a monitored body slope surface, wherein D is (f × H)/H, wherein f is a digital camera focal length, H is a shooting height range, and H is a digital camera CCD element height, measuring the height and the width of the monitoring area, determining the monitoring area, further determining a spatial position where the camera is arranged, fixing a video camera (4) through a forced centering pile (3), wherein the forced centering pile (3) is used for obtaining spatial coordinate information of a camera shooting center, and meanwhile, uniformly selecting at least 6 ground object points on the monitored body slope surface and measuring a three-dimensional spatial coordinate by using a total station.
Selecting a fixed-focus lens of the camera, and setting the depth of field, the aperture, the shutter speed, the light sensitivity ISO and a manual shooting mode of the camera;
thirdly, utilizing the acquired image to monitor the landslide crack of the monitoring area, including crack area delineation and displacement monitoring, as shown in fig. 3, the method specifically comprises the following steps:
s1, acquiring images of the monitoring area in real time through a camera;
s2, comparing the real-time image of the monitored image with the initial image, respectively obtaining the feature points of the real-time image and the initial image by using an SIFI algorithm, and matching the feature points to obtain the homonymous feature points of the real-time image and the initial image.
As shown in fig. 8, it is a flowchart of a SIFT feature matching algorithm, which is a mature algorithm in the image field, and therefore is not described herein again. For the field, if the slope is displaced, the slope is directly represented in the monitoring image in the form of local image displacement. The ground surface stone block moving mode is linear moving or rotary moving. In order to ensure that the same-name points can be matched in the monitored image in the landslide process, the matching algorithm is required to have strong matching capability under the conditions of image rotation and scale change. The SIFT feature points are local features and keep invariance to rotation, scaling and illumination change of the image. And the side slope is an inclined plane, in the process of landslide, the top soil block slides downwards, the image feature points monitored in the monitoring image are deformed into affine deformation, and the SIFT feature points also keep certain stability for affine transformation.
S3, as shown in FIG. 3, subtracting coordinates of image points of the same-name feature points of the initial image and the real-time image to obtain a displacement vector of the same-name feature points, if the displacement vector is larger than a displacement threshold value, judging that the point has displacement, then constructing a Delaunay triangulation network by using the detected displacement point, and defining a landslide body area A1; the landslide cracks only exist in the image after landslide, the corresponding homonymous feature points cannot be found in the image before landslide, therefore, the cracks cannot be defined, the two-dimensional field displacement vector model defining range based on the image feature points is only a landslide body area, and the defining result is shown in fig. 4; the boundary of the triangular net is the boundary of the landslide body area. Specifically, the displacement threshold may be zero, or may be selected according to specific situations.
Meanwhile, in the embodiment, the algorithm idea of the active region detection model based on the gray-scale image is as shown in fig. 5, the gray-scale values of the corresponding pixels of the initial image and the real-time image are subtracted, and the gray-scale difference is 0 in the region where no landslide occurs; when a landslide area occurs, an approximate probability event that the gray difference value is larger than zero occurs, and the model is designed based on the event. And further constructing a Delaunay triangulation network by using points (high-value points) with the gray difference value larger than zero, and detecting all changes of corresponding positions of the two images by comparing the difference of the gray values of the corresponding pixel positions of the two images based on an active region detection model of the gray images. When a landslide occurs, the landslide body integrally moves, corresponding positions in the two images are changed, pixel gray values of the corresponding positions are different, and a landslide range can be defined. At this time, the crack generated by the landslide is present only in the image captured after the landslide, and the corresponding position pixel gradation value is different from that of the image before the landslide, so that the model can be circled in the range of the landslide crack, and the circled result is as shown in fig. 6. The boundary of the triangular net is the boundary of the landslide body area and the landslide crack area. Considering some measurement errors, a gray threshold value can be set, a Delaunay triangulation network is constructed by using the pixel points with the gray difference value larger than the gray threshold value, and a landslide range area a2 is defined.
S4, coupling a landslide body area A1 with a landslide range area A2 to obtain a landslide crack area A3, wherein A3 is A2-A1;
in the embodiment of the invention, a two-dimensional field displacement vector model based on image feature points and an active region detection model based on a gray image are coupled, the delineation result is shown in fig. 7, the delineation results of fig. 4 and fig. 6 are superposed, an area A1 in the figure is a landslide crack area, and an area A2 is a landslide body area.
S5, obtaining a slope surface equation of a slope body before and after occurrence of a landslide by using three-dimensional space coordinates of a plurality of ground feature points arranged on the slope surface and a least square plane fitting method; then calculating to obtain space coordinates corresponding to image point coordinates of all the characteristic points with the same name on the initial image and the real-time image by a linear transformation method; and calculating to obtain the width of the landslide crack according to the coordinates in the ground photogrammetry coordinate system corresponding to the homonymous characteristic points in the landslide region in the initial image and the real-time image.
Specifically, the sliding mass slope equation is:
aX+bY+cZ+D＝0； (1)
in the formula, a, b and c are slope equation coefficients of a landslide body, D is a slope equation constant, and (X, Y and Z) represent three-dimensional space coordinates. Therefore, the three-dimensional space coordinates of 6 ground object points of the slope surface of the monitoring body before the landslide obtained in the step one are utilized to obtain the slope surface equation of the landslide body before the landslide by using the least square plane fitting method, and the slope surface equation coefficient a of the landslide body before the landslide is obtained1,b1,c1And constant of slope equation D1Similarly, the three-dimensional space coordinates of 6 ground object points of the slope surface of the monitoring body corresponding to the real-time image are measured (namely the three-dimensional space coordinates of the ground object points after the landslide occurs), the slope surface equation of the landslide body after the landslide can be obtained by using a least square plane fitting method, and the slope surface equation coefficient a of the landslide body after the landslide is obtained2,b2,c2And constant of slope equation D2The value of (c).
In addition, the method further comprises the steps of uniformly selecting a plurality of ground feature points on the monitoring slope surface, and measuring three-dimensional space coordinates of the ground feature points before and after the landslide, wherein the three-dimensional space coordinates are measured through a total station. That is to say, when a landslide is not started, a plurality of ground feature points need to be arranged on the slope surface, three-dimensional space coordinates of the ground feature points need to be measured through a total station, and after a landslide occurs subsequently, the three-dimensional space coordinates of the ground feature points need to be measured again, so that a slope surface equation of the landslide body after the landslide is obtained through a least square plane fitting method.
Secondly, a direct linear transformation method is used for obtaining the space coordinates which are correctly matched with the same-name points. Direct linear transformation is an algorithm that directly establishes the transformation relationship between image points and object points. The non-measuring camera cannot provide an inner orientation element value and an outer orientation element initial value, and the direct linear transformation algorithm does not need inner and outer orientation elements, so that the algorithm is widely applied to engineering application by using the non-measuring camera. The direct linear transformation has the basic relation:
in the formula Ii(i ═ 1,2, …,11) are direct linear transform coefficients, X, Y are image point coordinates, and X, Y, Z are three-dimensional space coordinates of the corresponding object point. Therefore, from the three-dimensional space coordinates (X ' n, Y ' n, Z ' n) of the feature points on the slope of 6 monitored objects and their coordinates (X ' n, Y ' n) in the image plane coordinate system, (n ═ 1,2,3,4,5,6, which represents the feature point number), we can obtain the following equation (2):
(3) the formula is a direct linear transformation coefficient calculation equation calculated by the image plane coordinates of the ground object points and the three-dimensional space coordinates in the ground photogrammetry coordinate system. The three-dimensional space coordinates of the land object points of the 6 monitoring body slopes before sliding and the coordinates in the image plane coordinate system in the initial image are substituted into the formula (3), and then the direct linear transformation coefficient l between the image plane coordinate system of the sliding body before sliding and the three-dimensional space coordinate system in the ground photogrammetry coordinate system can be calculatedi(i is 1,2, …,11), similarly, the coordinates of the land object points on the slope of 6 monitored bodies after sliding and the coordinates of the image plane coordinate system in the real-time image are substituted into the formula (3), and the direct linear transformation coefficient l between the image plane coordinate system of the sliding body after sliding and the three-dimensional space coordinate system in the ground photogrammetry coordinate system can be calculated "i(i＝1,2,…,11)。
Then, according to the direct linear transformation basic relation and the parallel slope and rising surface equation, the image point coordinates (x) of all the homonymous characteristic points in the initial image can be calculated1，y1) Corresponding spatial coordinate (X)D1、YD1、ZD1) Andcoordinates (x) of image points in real-time images2，y2) Corresponding spatial coordinate (X)D2、YD2、ZD2) The calculation formula is as follows:
and finally, solving the distance d of the homonymous feature points before and after slippage according to a distance formula, wherein the distance formula is as follows:
wherein d is the width of the landslide crack. The purpose of monitoring the landslide crack can be achieved by observing the distance change of each matched homonymous point. Therefore, after the steps are carried out, the invention can realize the monitoring of the landslide crack of the planar area.
The invention discloses a landslide crack monitoring method based on single-image close-range photogrammetry, which is characterized in that a camera is erected in a monitoring area, the obtained monitoring image is used for carrying out landslide crack monitoring on the monitoring area, a real-time image of the monitoring image is compared with an initial image, feature points of the real-time image and the initial image are respectively obtained by using an SIFI algorithm, and the feature points are matched to obtain the homonymous feature points of the real-time image and the initial image; establishing a two-dimensional field displacement vector model and detecting an active area based on a gray image based on the homonymous feature points of the initial image and the real-time image, and further obtaining a landslide crack area; and finally, calculating to obtain the width of the landslide crack according to the coordinates in the ground photogrammetry coordinate system corresponding to the homonymous feature points in the landslide region in the initial image and the real-time image. Displacement monitoring and crack area delineation are realized. Compared with other monitoring methods, the method has the advantages of high precision, no contact, wide monitoring range, visual monitoring result and the like.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A landslide crack monitoring method based on single-image close-range photogrammetry is characterized by comprising the following steps:
s1, acquiring images of the monitoring area in real time through a camera;
s2, comparing the real-time image of the monitored image with the initial image, respectively obtaining the feature points of the real-time image and the initial image by using an SIFI algorithm, and matching the feature points to obtain the homonymous feature points of the real-time image and the initial image;
s3, subtracting the coordinates of image points of the homonymous feature points of the initial image and the real-time image to obtain a displacement vector of the homonymous feature points, if the displacement vector is larger than a displacement threshold value, judging that the point is displaced, then constructing a Delaunay triangulation network by using the detected displacement point, and defining a landslide body area A1; subtracting the gray values of corresponding pixels of the initial image and the real-time image, constructing a Delaunay triangulation network by using the pixels of which the gray difference values are greater than the gray threshold value, and delineating a landslide range area A2;
s4, coupling a landslide body area A1 with a landslide range area A2 to obtain a landslide crack area A3, wherein A3 is A2-A1;
s5, obtaining a slope surface equation of a landslide body before and after a landslide by using three-dimensional space coordinates of a plurality of ground feature points arranged on the slope surface and a least square plane fitting method; then calculating to obtain space coordinates corresponding to image point coordinates of all the characteristic points with the same name on the initial image and the real-time image by a linear transformation method; and calculating to obtain the width of the landslide crack according to the space coordinates corresponding to the image point coordinates of the homonymous feature points in the landslide body region in the initial image and the real-time image.
2. The landslide crack monitoring method based on monoscopic close-range photogrammetry as claimed in claim 1, wherein before the step S1, the method further comprises the following steps:
determining the focal length, the area array size and the monitoring area of the camera, calculating the shooting distance D from the camera to a landslide body, wherein D is (f × H)/H, f is the focal length of the camera, H is the shooting height range, H is the height of a CCD (charge coupled device) element of the camera, measuring the height and the width of the monitoring area, determining the monitoring area, further determining the spatial position where the camera is arranged, fixing the camera (2) through a forced centering pile (1), and enabling the forced centering pile (1) to be used for obtaining the spatial coordinate information of the shooting center of the camera.
3. The landslide crack monitoring method based on monoscopic close-range photogrammetry as claimed in claim 1, wherein in step S5, the specific method for calculating the spatial coordinates corresponding to the image point coordinates of the homonymous feature points of the initial image and the real-time image comprises:
selecting and calibrating 6 ground object points on the slope, obtaining image point coordinates of the 6 ground object points in the initial image and the real-time image and three-dimensional space coordinates of corresponding space positions before and after the landslide, and calculating to obtain a direct linear transformation coefficient l between an image point coordinate system before and after the landslide and a three-dimensional space coordinate system in a ground photogrammetry coordinate systemi' (i ═ 1,2, …,11) and l "i(i ═ 1,2, …,11), the calculation formula is:
wherein, (X ' n, Y ' n) represents the image point coordinates of the feature point, (X ' n, Y ' n, Z ' n) represents the three-dimensional space coordinates of the feature point at the space position, and n is 1,2,3,4,5,6, and represents the feature point number;
then, according to the direct linear transformation basic relation, the slope equation is established in parallel, and all homonymous characteristic points are calculated and obtained in the initial graphCoordinates (x) of image points in the image1，y1) Corresponding spatial coordinate (X)D1、YD1、ZD1) And coordinates (x) of image points in real-time images2，y2) Corresponding spatial coordinate (X)D2、YD2、ZD2) The calculation formula is as follows:
wherein, a1,b1,c1Coefficient of slope equation, D, representing the body of the landslide front1Constant of the slope equation, a, representing the body of the landslide preceding the landslide2,b2,c2Coefficient of slope equation, D, representing the body of the landslide after landslide2And the slope equation constant of the landslide body after landslide is expressed.
4. The landslide crack monitoring method based on monoscopic close-range photogrammetry according to claim 3, wherein in the step S5, the landslide volume slope surface equation is as follows:
aX+bY+cZ+D＝0；
in the formula, a, b and c are slope equation coefficients of the landslide body, and D is a slope equation constant.
5. The landslide crack monitoring method based on monoscopic close-range photogrammetry as claimed in claim 3, further comprising the steps of uniformly selecting a plurality of geodetic points on the monitored slope surface, and measuring three-dimensional space coordinates of the geodetic points before and after the landslide, wherein the three-dimensional space coordinates are measured by a total station.
6. The landslide crack monitoring method based on single-image close-range photogrammetry as claimed in claim 3, wherein the calculation formula of the width of the landslide crack is as follows:
where d represents the landslide crack width.
7. The landslide crack monitoring method based on single-image close-range photogrammetry according to claim 1, wherein a displacement threshold value is zero, and a gray level threshold value is zero.
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| CN114783264A (en) * | 2022-06-23 | 2022-07-22 | 南通午未连海科技有限公司 | Landslide susceptibility model comparative analysis system and display device based on big data |
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| CN118486135A (en) * | 2024-05-10 | 2024-08-13 | 西安科技大学 | Landslide hazard monitoring and early warning method |
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