CN112577438B - Coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle image - Google Patents
Coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle image Download PDFInfo
- Publication number
- CN112577438B CN112577438B CN202011277126.5A CN202011277126A CN112577438B CN 112577438 B CN112577438 B CN 112577438B CN 202011277126 A CN202011277126 A CN 202011277126A CN 112577438 B CN112577438 B CN 112577438B
- Authority
- CN
- China
- Prior art keywords
- point
- aerial vehicle
- unmanned aerial
- image
- horizontal movement
- 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.)
- Active
Links
- 239000003245 coal Substances 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000012544 monitoring process Methods 0.000 title claims abstract description 25
- 238000005065 mining Methods 0.000 claims abstract description 90
- 230000008569 process Effects 0.000 claims description 17
- 230000000873 masking effect Effects 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 7
- 239000011435 rock Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000003702 image correction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003595 spectral 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
-
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a coal mine area three-dimensional deformation monitoring method by utilizing an unmanned aerial vehicle image, which comprises the following steps: estimating a coal mining ground surface influence area; registering two time phase unmanned aerial vehicle images I1 and I2; after registration, extracting homonymous points from the unmanned aerial vehicle images corresponding to the coal mining surface affected area, and rejecting mismatching point pairs; determining horizontal movement of the earth surface at two time intervals according to the extracted homonymy points, and performing spatial interpolation to obtain continuous horizontal movement in an image space; calculating two time phase earth surface subsidence values according to the extracted horizontal movement and by combining the relation between subsidence and horizontal movement in the coal mining subsidence model; and projecting the horizontal movement to the direction parallel to and perpendicular to the mining trend direction, and fusing the horizontal movement and the subsidence value to obtain a three-dimensional deformation value of the earth surface. The method realizes the three-dimensional deformation monitoring of the coal mine area by using the multi-temporal unmanned aerial vehicle image, solves the problem of dependence of the image-based mine area deformation monitoring on the SAR image, and has good monitoring effect on the three-dimensional deformation of the coal mine area.
Description
Technical Field
The invention relates to a coal mine area three-dimensional deformation monitoring method by utilizing an unmanned aerial vehicle image, and belongs to the field of coal mine deformation monitoring.
Background
After the local ore body is mined, a cavity is formed in the rock body, the original stress balance state around the cavity is damaged, the stress is redistributed until the new balance is achieved, the process is a very complex physical and mechanical change process and is also a process of generating movement and damage of the rock stratum, and the process or the phenomenon is called rock stratum movement. When the goaf area is enlarged to a certain extent, the rock strata moves and develops to the earth surface, so that the earth surface moves and deforms, and the process and the phenomenon are called earth surface movement. From the process of surface movement, the movement state of a surface point can be described by a vertical movement component and a horizontal movement component, the vertical movement component is generally called as sinking, and the horizontal movement component is divided into horizontal movement along a section direction and horizontal movement along a vertical section direction according to a relation relative to a certain section.
Mine deformation traditionally adopts the technical route of arranging observation stations and observing by adopting a level gauge. With the development of satellite imaging technology, a great deal of research is carried out on extracting deformation of a mining area by utilizing Synthetic Aperture Radar (SAR) images, and although the SAR image has the advantage of wide monitoring range, the acquisition cost of high-resolution SAR data is high, and the information observed by low-resolution SAR data has limitation. Since the 20 th century and the 30 th era, aerial photogrammetry has been developed for a long time, along with the maturity of the unmanned aerial vehicle technology and the reduction of the manufacturing cost, the unmanned aerial vehicle technology, the remote sensing technology, the information technology and the sensor technology are highly integrated, and the technology of acquiring ground images by using the unmanned aerial vehicle for measurement is rapidly developed, so that the aerial photogrammetry has been widely applied to the fields of topographic map measurement, urban three-dimensional modeling, emergency rescue, agricultural production and the like. But because the spectral information on earth's surface can only be obtained to the unmanned aerial vehicle image, the degree of difficulty that utilizes the unmanned aerial vehicle image to carry out the monitoring of earth's surface deformation is very big.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the coal mine area three-dimensional deformation monitoring method using the unmanned aerial vehicle image solves the problem of dependence of image-based mine area deformation monitoring on the SAR image, and has the advantages of low cost, high precision, wide monitoring range, convenience in implementation and the like.
The invention adopts the following technical scheme for solving the technical problems:
a coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle images comprises the following steps:
step 1, acquiring unmanned aerial vehicle images I1 and I2 covering the same coal mining area at two different time phases, wherein the unmanned aerial vehicle images I1 and I2 are orthoimages, and the spatial resolutions of the unmanned aerial vehicle images I1 and I2 are the same;
step 2, estimating the influence range of underground coal mining on the earth surface by combining underground mining data and a coal mining subsidence model, namely a coal mining earth surface influence area;
step 3, registering the unmanned aerial vehicle images I1 and I2 at two different time phases to obtain registered images I1 and I2', and masking the image area of the coal mining ground surface influence area corresponding to each image during registration;
step 4, extracting homonymous point pairs of the images I1 and I2 'on the image areas corresponding to the coal mining surface influence areas by utilizing a plurality of homonymous point extraction methods for the registered images I1 and I2';
step 5, according to the homonymous point pairs of the images I1 and I2', alternate horizontal movement of the earth surface is calculated when the unmanned aerial vehicle images I1 and I2 are obtained in a different mode, the homonymous point pairs which are mismatched are eliminated by combining underground mining data, spatial interpolation is carried out on the earth surface horizontal movement obtained by calculation of the remaining homonymous point pairs, and continuous horizontal movement in an image space is obtained;
step 6, calculating the surface subsidence values between the unmanned aerial vehicle images I1 and I2 in two different time phases according to the continuous horizontal movement in the image space and the relation between subsidence and horizontal movement in the coal mining subsidence model;
and 7, projecting the horizontal movement of the earth surface to the direction parallel to the mining trend direction, projecting the horizontal movement of the earth surface to the direction vertical to the mining trend direction, and fusing the subsidence value of the earth surface and the horizontal movement of the earth surface after the projection to obtain the three-dimensional deformation value of the earth surface.
As a preferred scheme of the invention, in the unmanned aerial vehicle images I1 and I2 in the step 1, the flight direction of the unmanned aerial vehicle is parallel to the coal mining trend direction in the acquisition process; or the flight direction of the unmanned aerial vehicle is perpendicular to the coal mining trend direction in the acquisition process.
As a preferred aspect of the present invention, the coal mining surface area of step 2 includes a surface area corresponding to the goaf of the coal mine and a region that is externally extended from the surface area, a distance between a boundary of the externally extended region and a boundary of the surface area is r, r is H/tan β, r is a main influence radius, H is a mining depth, and tan β is a main influence tangent.
As a preferred embodiment of the present invention, the specific process of step 3 is as follows:
masking an image area corresponding to a coal mining surface influence area on an unmanned aerial vehicle image I1, masking an image area corresponding to the coal mining surface influence area on an unmanned aerial vehicle image I2, extracting homonymy point pairs in non-masking processing areas of the unmanned aerial vehicle images I1 and I2, calculating correction parameters, registering the unmanned aerial vehicle images I1 and I2 by using the correction parameters, using the same correction parameters for the masking processing areas and the non-masking processing areas, correcting the unmanned aerial vehicle image I1 to an image coordinate system without changing the unmanned aerial vehicle image I2 in the registration process, and obtaining a corrected unmanned aerial vehicle image I2', wherein the origin of the image coordinate system is the upper left corner of the unmanned aerial vehicle image I1, the direction from the origin to the right is the X-axis direction of the image coordinate system, and the direction from the origin to the bottom is the Y-axis direction of the image coordinate system.
As a preferable scheme of the present invention, the method for extracting multiple homologous points in step 4 includes: the method comprises the following steps of KAZE feature points, scale-invariant feature transformation points, maximum stable extremum region points, Harris points, weighted alpha shape feature points and straight line matching feature points.
As a preferred embodiment of the present invention, in step 5, according to the homonymous point pairs of the images I1 and I2', horizontal movement of the earth surface at two different times of acquisition of the unmanned aerial vehicle images I1 and I2 is calculated, and the homonymous point pairs which are mismatched in combination with the underground mining data are removed, specifically:
for the extracted homonymous point pairs p (I, j) and p '(I', j '), the point p (I, j) is located on the drone image I1, the point p' (I ', j') is located on the drone image I2 ', (I, j) is the coordinate of the point p (I, j) in the image coordinate system, and (I', j ') is the coordinate of the point p' (I ', j') in the image coordinate system, according to the formula UX(i, j) ═ j' -j and UY(i, j) ═ i' -i two non-simultaneously spaced horizontal earth surface movements are calculated, UX(i, j) is the horizontal movement of the homonymous point pair p (i, j) and p ' (i ', j ') in the X-axis direction, UY(i, j) is the original of the image coordinate system by the horizontal movement of the homonymous point pair p (i, j) and p ' (i ', j ') in the Y-axis directionThe point is the upper left corner of the unmanned aerial vehicle image I1, the direction of the origin point to the right is the X-axis direction of the image coordinate system, and the direction of the origin point to the lower is the Y-axis direction of the image coordinate system;
by means of UX(i,j)≤UmaxAnd UY(i,j)≤UmaxRejecting mismatching pairs of homonymous points, U, requiring conditions to be met simultaneouslymaxIs the maximum horizontal movement, Umax＝bwmaxB is a horizontal shift coefficient, wmaxIs the estimated maximum dip value of the current signal,
m is the mining thickness, q is the subsidence coefficient related to lithology, alpha is the coal seam dip angle, alpha is less than or equal to 15 degrees, k is the coefficient, and the value range of k is [2,3 ]]，n1Is a coefficient of tendency to adopt fully, n2Is a coefficient of full mining degree of the trend, n is more than or equal to 01≤1，0≤n2≤1，n1＝D1/D01，n2＝D2/D02，D1Is the length of the gob along the dip, D2Is the length of the goaf along the strike, D01Is the critical length of the goaf tendency when the earth's surface reaches full mining, D02The critical length of the goaf trend when the earth surface reaches full mining, b, q, m, alpha and D1、D2、D01And D02Is known as underground mining data.
As a preferable scheme of the present invention, the method for calculating the surface subsidence value in step 6 comprises:
obtaining sinking values w (i, j), w (i, j +1), w (i +1, j +1), w (i-1, j-1) and w (i, j-1) by a least square method, wherein the formula is as follows:
wherein Δ D is the spatial resolution, U, of the UAV images I1 and I2X(I, j) is the horizontal movement of point p (I, j) on the X-axis on I1, UY(I, j) is the horizontal movement of point p (I, j) on the Y-axis on I1, UY(I, j +1) is the horizontal movement of point p (I, j +1) on the Y-axis on I1, UX(I +1, j) is the horizontal movement of point p (I +1, j) on the X-axis on I1, UX(I-1, j-1) is the horizontal movement of point p (I-1, j-1) on the X-axis on I1, UY(I-1, j-1) is the horizontal movement of point p (I-1, j-1) on the Y-axis on I1, UY(I-1, j) is the horizontal movement of point p (I-1, j) on the Y-axis on I1, UX(I, j-1) is the horizontal shift on the X-axis of point p (I, j-1) on I1, w (I, j) is the sinking value of point p (I, j) on I1, w (I, j +1) is the sinking value of point p (I, j +1) on I1, w (I +1, j) is the sinking value of point p (I +1, j) on I1, w (I +1, j +1) is the sinking value of point p (I +1, j +1) on I1, w (I-1, j) is the sinking value of point p (I-1, j) on I1, w (I-1, j-1) is the sinking value of point p (I-1, j-1) on I1, w (I, j-1) is the sinking value of point p (I, j-1) on I1, b is the main shift coefficient, r is the horizontal shift coefficientAffecting the radius.
As a preferred embodiment of the present invention, the three-dimensional deformation value of the earth's surface in step 7 is:
TDC＝[Uh(i,j),Uv(i,j),w(i,j)]
where TDC is the three-dimensional deformation value of point p (I, j), w (I, j) is the sinking value of point p (I, j) on I1, Uh(i, j) is the horizontal movement of point p (i, j) in the direction of the mining strike, Uv(i, j) is the horizontal movement of point p (i, j) in the direction perpendicular to the direction of the mining strike, Uh(i,j)＝UX(i,j)cosθ+UY(i,j)sinθ，Uv(i,j)＝UX(i,j)sinθ+UY(i, j) cos θ, θ is the angle between the X axis of the image coordinate system and the mining heading direction, UX(I, j) is the horizontal movement of point p (I, j) on the X-axis on I1, UY(I, j) is the horizontal movement of the point p (I, j) on the Y axis on I1, the origin of the image coordinate system is the upper left corner of the drone image I1, the direction of the origin to the right is the X axis direction of the image coordinate system, and the direction of the origin to the bottom is the Y axis direction of the image coordinate system.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
according to the invention, the multi-temporal unmanned aerial vehicle image is used for extracting the horizontal movement of the ground surface caused by underground coal mining in the coal mining area, and then the subsidence value of the ground surface is calculated by taking the coal mining subsidence model as a support, so that the three-dimensional deformation monitoring of the coal mining area is realized, the problem of dependence of the image-based mining area deformation monitoring on the SAR image is solved, the application field of the unmanned aerial vehicle image is expanded, and the method has the advantages of clear theory, low cost, high precision, wide monitoring range and convenience in realization, and provides a new way for the three-dimensional deformation monitoring of the coal mining area caused by underground coal mining.
Drawings
Fig. 1 is a flow chart of an implementation of the method for monitoring three-dimensional deformation of a coal mine area by using an unmanned aerial vehicle image according to the present invention.
FIG. 2 is a schematic illustration of the extent of a coal face area of influence.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, the invention provides a coal mine area three-dimensional deformation monitoring method using unmanned aerial vehicle images, comprising the following steps:
a, acquiring unmanned aerial vehicle images I1 and unmanned aerial vehicle images I2 which cover the same mining area at different time phases;
the flight direction of the unmanned aerial vehicle is basically parallel or vertical to the mining trend direction, the acquired unmanned aerial vehicle images I1 and I2 are orthoimages, the unmanned aerial vehicle images I1 and I2 are acquired in an oblique photography mode, otherwise, the oblique photography images are converted into orthoimages by using a complex image processing method, the acquired unmanned aerial vehicle images I1 and I2 have the same spatial resolution, and the coal seam inclination angle alpha of a coal mine area is less than or equal to 15 degrees;
b, estimating the influence range (coal mining surface influence area) of underground coal mining on the surface by combining the underground mining data and the coal mining subsidence model;
underground mining data: the method comprises the steps of (1) estimating the influence range of underground coal mining on the earth surface (a coal mining earth surface influence area, shown in figure 2) by utilizing a coal mining subsidence model based on a probability integration method according to national earth coordinate system coordinates of an underground goaf boundary, a mining depth H and a main influence angle tan beta in the historical development process of the mine area, wherein the coal mining earth surface influence area consists of an area with the goaf boundary as an axis and a radius of r and an area above the goaf, wherein r is a main influence radius, and the main influence angle tan beta is obtained according to a formula of r;
c, registering the two time phase unmanned aerial vehicle images I1 and I2;
masking an unmanned aerial vehicle image area corresponding to a coal mining surface influence area before registration to avoid the influence of horizontal movement and sinking of the surface on two-time-phase unmanned aerial vehicle image registration caused by coal mining influence, wherein an unmanned aerial vehicle image I1 is unchanged in the registration process, and only an unmanned aerial vehicle image I2 is corrected to an image coordinate system to obtain a corrected image I2', wherein the origin of the image coordinate system is positioned at the upper left corner of I1, the direction from the origin to the right is the X-axis direction, the direction from the origin to the bottom is the Y-axis direction, specifically, the same-name point is not extracted from a mask area in the registration process, the same-name point is extracted only from a non-mask area, and image correction parameters are calculated, and the same correction parameters are used for the mask area and the non-mask area;
d I1 and I2 are registered, then, for the unmanned aerial vehicle images corresponding to the coal mining surface influence area, the homonymous points of the images I1 and I2 'are jointly extracted by adopting a KAZE Feature point, a Scale-Invariant Feature Transform (SIFT) point, a Maximum Stable Extremum Region (MSER) point, a Harris point, a Weighted alpha-shape Feature (W alpha SH) point and a Feature point equivalent name point identification method of straight line matching, and the homonymous point pairs p (I, j) and p' (I ', j') are extracted according to a formula UX(i, j) ═ j' -j and UY(I, j) ═ I ' -I calculation of horizontal ground movement between two time phase images, where p (I, j) is located on drone image I1, p ' (I ', j ') is located on drone image I2 ', UX(i, j) is the horizontal movement of point p (i, j) in the X-axis direction, UY(i, j) is the horizontal movement of the point p (i, j) in the Y-axis direction, (i, j) is the coordinates of the point p (i, j) in the image coordinate system, and (i ', j ') is the coordinates of the point p ' (i ', j ') in the image coordinate system, and then using the formula UX(i,j)≤UmaxAnd UY(i,j)≤UmaxRejecting mismatched point pairs with a maximum horizontal shift Umax＝bwmaxWherein b is a horizontal migration coefficient, q is a subsidence coefficient related to lithology, alpha is less than or equal to 15 degrees and is a coal seam inclination angle, m is mining thickness, and the estimated maximum subsidence value
Wherein k is coefficient and the value range is [2,3 ]]Generally, k is 2, 0. ltoreq. n1A coefficient of tendency to fully adopt is not less than 1, n is not less than 02A coefficient of full mining degree in the trend of not more than 1, n1According to the formula n1＝D1/D01Calculation of n2According to the formula n2＝D2/D02Calculation, D1Is the length of the gob along the dip, D2Is the length of the goaf along the strike, D01Is to achieve full mining of the earth surfaceCritical length of time gob inclination, D02The critical length of the goaf trend when the earth surface reaches full mining, b, q, m, alpha and D1、D2、D01And D02Known underground mining data;
e, determining horizontal movement of the earth surface between two time phases according to the extracted homonymous points, performing spatial interpolation on the extracted horizontal movement to obtain continuous horizontal movement in an image space, and extracting a formula of the horizontal movement of the earth surface between two time phases of a pair of homonymous points p (i, j) and p ' (i ', j ') as follows: u shapeX(i, j) ═ j' -j and UYI ' -I, where p (I, j) is located on drone image I1, p ' (I ', j ') is located on drone image I2 ', UX(i, j) is the horizontal movement of the homologous points p (i, j) and p ' (i ', j ') in the X-axis direction, UY(i, j) is a horizontal movement of the homonymous points p (i, j) and p '(i', j ') in the Y-axis direction, (i, j) is a coordinate of the point p (i, j) in the picture coordinate system, and (i', j ') is a coordinate of the point p' (i ', j') in the picture coordinate system;
f, calculating two time phase earth surface subsidence values according to the extracted horizontal movement and by combining the relationship between subsidence and horizontal movement in the coal mining subsidence model, wherein the method for calculating the two time phase earth surface subsidence values comprises the following steps: by using the following equation sets (1) - (8), sinking values w (i, j), w (i, j +1), w (i +1, j +1), w (i-1, j-1) and w (i, j-1) can be obtained by solving 8 equations, and when 7 unknowns are solved by using 8 equations, the reliability of solving the sinking values is improved by using a least square method:
where Δ D is the spatial resolution, U, of the drone imagery I1 and I2X(I, j) is the horizontal movement of point p (I, j) on the X-axis on I1, UY(I, j) is the horizontal movement of point p (I, j) on the Y-axis on I1, UY(I, j +1) is the horizontal movement of point p (I, j +1) on the Y-axis on I1, UX(I +1, j) is the horizontal movement of point p (I +1, j) on the X-axis on I1, UX(I-1, j-1) is the horizontal movement of point p (I-1, j-1) on the X-axis on I1, UY(I-1, j-1) is the horizontal movement of point p (I-1, j-1) on the Y-axis on I1, UY(I-1, j) is the horizontal movement of point p (I-1, j) on the Y-axis on I1, UX(I, j-1) is the horizontal shift of point p (I, j-1) on I1 on the X-axis, w (I, j) is the dip of point p (I, j) on I1, w (I, j +1) is the dip of point p (I, j +1) on I1, w (I +1, j) is the dip of point p (I +1, j) on I1, w (I +1, j +1) is the dip of point p (I +1, j +1) on I1, w (I-1, j) is the dip of point p (I-1, j) on I1, w (I-1, j-1) is the dip of point p (I-1, j-1) on I1, w (I, j-1) is the dip of point p (I, j-1) on I1, b is the horizontal shift coefficient, r is the major influence coefficient, r is the radius H/tan H, tan H is the depth of the mining depth, tan β is the major influence tangent;
g projecting the horizontal movement to a direction parallel and perpendicular to the direction of the mining strikeAnd then, fusing the horizontal movement and sinking values to obtain a three-dimensional deformation value of the earth surface, wherein the formula of horizontal movement reprojection is as follows: u shapeh(i,j)＝UX(i,j)cosθ+UY(i,j)sinθ，Uv(i,j)＝UX(i,j)sinθ+UY(i, j) cos θ, by combining the horizontal movement and the sinking values of the same point p (i, j) into a new three-dimensional deformation vector TDC [ [ U ]h(i,j),Uv(i,j),w(i,j)]Wherein theta is the angle between the X axis of the image coordinate system and the mining trend direction, Uh(i, j) is the horizontal movement of point p (i, j) in the direction of the mining strike, Uv(i, j) is the horizontal movement of point p (i, j) in the direction perpendicular to the direction of the mining strike, UX(I, j) is the horizontal movement of point p (I, j) on the X-axis on I1, UY(I, j) is the horizontal movement of the point p (I, j) on the Y axis on I1, the origin of the image coordinate system is the upper left corner of the drone image I1, the direction of the origin to the right is the X axis direction of the image coordinate system, and the direction of the origin to the bottom is the Y axis direction of the image coordinate system.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (5)
1. A coal mine area three-dimensional deformation monitoring method by using unmanned aerial vehicle images is characterized by comprising the following steps:
step 1, acquiring unmanned aerial vehicle images I1 and I2 covering the same coal mining area at two different time phases, wherein the unmanned aerial vehicle images I1 and I2 are orthoimages, and the spatial resolutions of the unmanned aerial vehicle images I1 and I2 are the same;
step 2, estimating the influence range of underground coal mining on the earth surface by combining underground mining data and a coal mining subsidence model, namely a coal mining earth surface influence area;
step 3, registering the unmanned aerial vehicle images I1 and I2 at two different time phases to obtain registered images I1 and I2', and masking the image area of the coal mining ground surface influence area corresponding to each image during registration; the specific process is as follows:
masking an image area corresponding to a coal mining surface influence area on an unmanned aerial vehicle image I1, masking an image area corresponding to the coal mining surface influence area on an unmanned aerial vehicle image I2, extracting dotted pairs in unmasked processing areas of the unmanned aerial vehicle images I1 and I2, calculating correction parameters, registering the unmanned aerial vehicle images I1 and I2 by using the correction parameters, using the same correction parameters for the masked processing areas and the unmasked processing areas, correcting the unmanned aerial vehicle image I1 to an image coordinate system without changing the unmanned aerial vehicle image I2 in the registration process, and obtaining a corrected unmanned aerial vehicle image I2', wherein the origin of the image coordinate system is the upper left corner of the unmanned aerial vehicle image I1, the direction from the origin to the right is the X-axis direction of the image coordinate system, and the direction from the origin to the bottom is the Y-axis direction of the image coordinate system;
step 4, extracting homonymous point pairs of the images I1 and I2 'on the image areas corresponding to the coal mining surface influence areas by utilizing a plurality of homonymous point extraction methods for the registered images I1 and I2';
step 5, according to the homonymous point pairs of the images I1 and I2', alternate horizontal movement of the earth surface is calculated when the unmanned aerial vehicle images I1 and I2 are obtained in a different mode, the homonymous point pairs which are mismatched are eliminated by combining underground mining data, spatial interpolation is carried out on the earth surface horizontal movement obtained by calculation of the remaining homonymous point pairs, and continuous horizontal movement in an image space is obtained;
according to the homonymous point pairs of the images I1 and I2', the horizontal movement of the earth surface at the phases of the unmanned aerial vehicle images I1 and I2 in two different acquisition processes is calculated, and the homonymous point pairs which are mismatched are removed by combining underground mining data, and the method specifically comprises the following steps:
for the extracted homonymous point pairs p (I, j) and p '(I', j '), the point p (I, j) is located on the drone image I1, the point p' (I ', j') is located on the drone image I2 ', (I, j) is the coordinate of the point p (I, j) in the image coordinate system, and (I', j ') is the coordinate of the point p' (I ', j') in the image coordinate system, according to the formula UX(i, j) ═ j' -j and UY(i, j) ═ i' -i two non-simultaneously spaced horizontal earth surface movements are calculated, UX(i, j) is the horizontal movement of the homonymous point pair p (i, j) and p ' (i ', j ') in the X-axis direction, UY(i, j) are the same nameHorizontally moving the point pairs p (I, j) and p ' (I ', j ') in the Y-axis direction, wherein the origin of the image coordinate system is the upper left corner of the unmanned aerial vehicle image I1, the rightward direction of the origin is the X-axis direction of the image coordinate system, and the downward direction of the origin is the Y-axis direction of the image coordinate system;
by means of UX(i,j)≤UmaxAnd UY(i,j)≤UmaxRejecting mismatching pairs of homonymous points, U, requiring conditions to be met simultaneouslymaxIs the maximum horizontal movement, Umax＝bwmaxB is a horizontal shift coefficient, wmaxIs the estimated maximum dip value of the current signal,
m is the mining thickness, q is the subsidence coefficient related to lithology, alpha is the coal seam dip angle, alpha is less than or equal to 15 degrees, k is the coefficient, and the value range of k is [2,3 ]]，n1Is a coefficient of tendency to adopt fully, n2Is a coefficient of full mining degree of the trend, n is more than or equal to 01≤1，0≤n2≤1，n1＝D1/D01，n2＝D2/D02，D1Is the length of the gob along the dip, D2Is the length of the goaf along the strike, D01Is the critical length of the goaf tendency when the earth's surface reaches full mining, D02The critical length of the goaf trend when the earth surface reaches full mining, b, q, m, alpha and D1、D2、D01And D02Known underground mining data;
step 6, calculating the surface subsidence values between the unmanned aerial vehicle images I1 and I2 in two different time phases according to the continuous horizontal movement in the image space and the relation between subsidence and horizontal movement in the coal mining subsidence model;
the calculation method of the surface subsidence value comprises the following steps:
obtaining sinking values w (i, j), w (i, j +1), w (i +1, j +1), w (i-1, j-1) and w (i, j-1) by a least square method, wherein the formula is as follows:
wherein Δ D is the spatial resolution, U, of the UAV images I1 and I2X(I, j) is the horizontal movement of point p (I, j) on the X-axis on I1, UY(I, j) is the horizontal movement of point p (I, j) on the Y-axis on I1, UY(I, j +1) is the horizontal movement of point p (I, j +1) on the Y-axis on I1, UX(I +1, j) is the horizontal movement of point p (I +1, j) on the X-axis on I1, UX(I-1, j-1) is the horizontal movement of point p (I-1, j-1) on the X-axis on I1, UY(I-1, j-1) is the horizontal movement of point p (I-1, j-1) on the Y-axis on I1, UY(I-1, j) is the Y-axis of point p (I-1, j) on I1Horizontal movement, UX(I, j-1) is the horizontal shift of point p (I, j-1) on I1 on the X axis, w (I, j) is the dip of point p (I, j) on I1, w (I, j +1) is the dip of point p (I, j +1) on I1, w (I +1, j) is the dip of point p (I +1, j) on I1, w (I +1, j +1) is the dip of point p (I +1, j +1) on I1, w (I-1, j) is the dip of point p (I-1, j) on I1, w (I-1, j-1) is the dip of point p (I-1, j-1) on I1, w (I, j-1) is the dip of point p (I-1, j-1) on I1, b is the major radius of the shift;
and 7, projecting the horizontal movement of the earth surface to the direction parallel to the mining trend direction, projecting the horizontal movement of the earth surface to the direction vertical to the mining trend direction, and fusing the subsidence value of the earth surface and the horizontal movement of the earth surface after the projection to obtain the three-dimensional deformation value of the earth surface.
2. The method for monitoring the three-dimensional deformation of the coal mine area by using the unmanned aerial vehicle image as claimed in claim 1, wherein the unmanned aerial vehicle images I1 and I2 in the step 1 are obtained in such a way that the flight direction of the unmanned aerial vehicle is parallel to the coal mine mining trend direction; or the flight direction of the unmanned aerial vehicle is perpendicular to the coal mining trend direction in the acquisition process.
3. The method for monitoring the three-dimensional deformation of the coal mine area by using the unmanned aerial vehicle image according to claim 1, wherein the coal mining surface influence area in the step 2 comprises a surface area corresponding to a coal mine goaf and an area externally expanded from the surface area, a distance between a boundary of the externally expanded area and a boundary of the surface area is r, r is H/tan β, r is a main influence radius, H is a mining depth, and tan β is a main influence tangent.
4. The method for monitoring the three-dimensional deformation of the coal mine area by using the unmanned aerial vehicle image as claimed in claim 1, wherein the method for extracting the plurality of homonymy points in step 4 comprises: the method comprises the following steps of KAZE feature points, scale-invariant feature transformation points, maximum stable extremum region points, Harris points, weighted alpha shape feature points and straight line matching feature points.
5. The method for monitoring the three-dimensional deformation of the coal mine area by using the unmanned aerial vehicle image as claimed in claim 1, wherein the three-dimensional deformation value of the earth surface in the step 7 is as follows:
TDC＝[Uh(i,j),Uv(i,j),w(i,j)]
where TDC is the three-dimensional deformation value of point p (I, j), w (I, j) is the sinking value of point p (I, j) on I1, Uh(i, j) is the horizontal movement of point p (i, j) in the direction of the mining strike, Uv(i, j) is the horizontal movement of point p (i, j) in the direction perpendicular to the direction of the mining strike, Uh(i,j)＝UX(i,j)cosθ+UY(i,j)sinθ，Uv(i,j)＝UX(i,j)sinθ+UY(i, j) cos θ, θ is the angle between the X axis of the image coordinate system and the mining heading direction, UX(I, j) is the horizontal movement of point p (I, j) on the X-axis on I1, UY(I, j) is the horizontal movement of the point p (I, j) on the Y axis on I1, the origin of the image coordinate system is the upper left corner of the drone image I1, the direction of the origin to the right is the X axis direction of the image coordinate system, and the direction of the origin to the bottom is the Y axis direction of the image coordinate system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011277126.5A CN112577438B (en) | 2020-11-16 | 2020-11-16 | Coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011277126.5A CN112577438B (en) | 2020-11-16 | 2020-11-16 | Coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112577438A CN112577438A (en) | 2021-03-30 |
| CN112577438B true CN112577438B (en) | 2021-12-28 |
Family
ID=75122634
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011277126.5A Active CN112577438B (en) | 2020-11-16 | 2020-11-16 | Coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112577438B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113091599B (en) * | 2021-04-06 | 2021-12-03 | 中国矿业大学 | Surface three-dimensional deformation extraction method fusing unmanned aerial vehicle DOM and satellite-borne SAR images |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617649B (en) * | 2013-11-05 | 2016-05-11 | 北京江宜科技有限公司 | A kind of river model topographic survey method based on Camera Self-Calibration technology |
| CN105783878A (en) * | 2016-03-11 | 2016-07-20 | 三峡大学 | Small unmanned aerial vehicle remote sensing-based slope deformation detection and calculation method |
| CN106767380B (en) * | 2017-01-19 | 2019-04-19 | 中南大学 | A kind of big magnitude three-dimensional deformation estimation method of mining area surface based on two scape SAR intensity images |
| CN106969751B (en) * | 2017-03-13 | 2017-11-14 | 西安科技大学 | A kind of method of the coal mining subsidence amount monitoring calculation based on unmanned aerial vehicle remote sensing |
| CN109211132A (en) * | 2017-07-07 | 2019-01-15 | 北京林业大学 | A kind of photogrammetric method for obtaining tall and big object deformation information of unmanned plane high-precision |
| CN109143257A (en) * | 2018-07-11 | 2019-01-04 | 中国地质调查局西安地质调查中心 | Unmanned aerial vehicle onboard radar mining land change monitors system and method |
| CN110132237A (en) * | 2019-05-05 | 2019-08-16 | 四川省地质工程勘察院 | A kind of method of urban ground deformation disaster EARLY RECOGNITION |
| CN110136128B (en) * | 2019-05-20 | 2021-03-02 | 中国矿业大学 | SAR image change detection method based on Rao detection |
| CN110378199B (en) * | 2019-06-03 | 2021-08-06 | 北京北科安地科技发展有限公司 | Rock-soil body displacement monitoring method based on multi-period images of unmanned aerial vehicle |
| CN110750866B (en) * | 2019-09-17 | 2022-04-26 | 中国矿业大学 | Method for rapidly obtaining mining subsidence prediction parameters by using unmanned aerial vehicle technology |
| CN111551932A (en) * | 2020-03-16 | 2020-08-18 | 中国矿业大学 | Method for accurately acquiring mining influence boundary and determining building damage level |
-
2020
- 2020-11-16 CN CN202011277126.5A patent/CN112577438B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112577438A (en) | 2021-03-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111598823B (en) | Multisource mobile measurement point cloud data space-ground integration method and storage medium | |
| Dall’Asta et al. | Unmanned Aerial Systems and DSM matching for rock glacier monitoring | |
| WO2022213673A1 (en) | Method for extracting three-dimensional surface deformation by combining unmanned aerial vehicle doms and satellite-borne sar images | |
| CN104484668B (en) | A kind of contour of building line drawing method of the how overlapping remote sensing image of unmanned plane | |
| Rau et al. | True orthophoto generation of built-up areas using multi-view images | |
| CN107451593B (en) | High-precision GPS positioning method based on image feature points | |
| CN112987065B (en) | Multi-sensor-integrated handheld SLAM device and control method thereof | |
| CN105300362A (en) | Photogrammetry method used for RTK receivers | |
| Di et al. | Coastal mapping and change detection using high-resolution IKONOS satellite imagery | |
| CN113624231B (en) | Inertial vision integrated navigation positioning method based on heterogeneous image matching and aircraft | |
| CN112489094A (en) | Multi-line airborne laser radar point cloud data registration method | |
| Kim et al. | Investigating applicability of unmanned aerial vehicle to the tidal flat zone | |
| CN116910876A (en) | Unmanned aerial vehicle-based remote sensing-assisted landslide emergency treatment engineering investigation design method | |
| CN112577438B (en) | Coal mine area three-dimensional deformation monitoring method utilizing unmanned aerial vehicle image | |
| CN115457022A (en) | Three-dimensional deformation detection method based on real-scene three-dimensional model front-view image | |
| CN109358315B (en) | Auxiliary target indirect positioning method and system | |
| Oh et al. | Automatic georeferencing of aerial images using stereo high-resolution satellite images | |
| Tian et al. | Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery | |
| KR102013625B1 (en) | Method and apparatus for constructing high precision 3D GIS data using lattice type GCPs | |
| Zhang et al. | Generation of digital surface model from high resolution satellite imagery | |
| Hwangbo et al. | Integration of orbital and ground image networks for the automation of rover localization | |
| Zhou et al. | Occlusion detection for urban aerial true orthoimage generation | |
| CN114463523A (en) | Point cloud navigation band adjustment method based on minimum Hausdorff distance surface feature constraint | |
| CN114004870A (en) | Unmanned aerial vehicle SAR image geocoding method using optical orthographic image | |
| Barazzetti | Sliver removal in object-based change detection from VHR satellite images |
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 |