Geospatialization method for evidence-proof photo of unmanned aerial vehicle

Abstract

The invention discloses a geographic spatialization method for an unmanned aerial vehicle proof photo, and the method comprises the steps: 1) obtaining a boundary line of a to-be-processed region, and recording the outermost boundary points of the boundary line in four directions; 2) constructing a rectangular area to be shot based on an outermost boundary point A = {AW, AE, AS, AN}; 3) dividing the rectangular area to be shot into # imgabs0 # sub-areas to be shot; 4) planning the flight path of the unmanned aerial vehicle based on the coordinate matrix D of the to-be-shot sub-region and the current position coordinates (Fx, Fy) of the unmanned aerial vehicle; 5) preprocessing the geographic image shot by the unmanned aerial vehicle; 6) splicing the preprocessed geographic images based on the overlapping shooting area delta S of two adjacent to-be-shot sub-regions and the adjacent relation of different to-be-shot sub-regions; and 7) importing the geographic image into a GIS system, and presenting the spatial information of the to-be-processed area through a map. The problem of short endurance time of the unmanned aerial vehicle is considered, the path of the unmanned aerial vehicle is planned according to the size of the to-be-shot area, and the shooting efficiency of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The invention relates to the field of image processing, and in particular to a geospatialization method for drone evidence photos. Background Art

[0002] As an effective carrier for recording geographic spatial information, maps have become a powerful tool for people to understand the world with their advantages such as intuitiveness, integrity and simplicity. They have also played an important role in people's understanding of the real world and in obtaining and analyzing applied geographic information.

[0003] Geographic Information System (GIS) is an extension and development of computer mapping technology. It not only provides powerful technical support for map making, but also enables the management of massive amounts of multi-source spatiotemporal geographic data. Compared with video surveillance systems, GIS mainly presents spatiotemporal information in the form of maps, which has the advantages of overview, location, and measurability.

[0004] Existing geospatial expression methods mainly focus on the materials of a single image and pay less attention to the fusion of multi-image spatial information. Summary of the invention

[0005] The purpose of the present invention is to provide a method for geospatialization of drone evidence photos, comprising the following steps:

[0006] 1) Obtain the boundary line of the area to be processed and record the outermost boundary points of the boundary line in four directions, denoted as A = {A W ,A E ,A S ,A N};

[0007] 2) Based on the outermost boundary point A = {A W ,A E ,A S ,A N}, construct the rectangular area to be photographed, and determine the area of ​​the rectangular area to be photographed The outermost boundary point A W , A E The linear distance in the horizontal direction; The outermost boundary point A S , A N The straight-line distance in the vertical direction;

[0008] 3) Determine the single shooting area S0 of the drone at the height h, and judge whether the single shooting area S0 ≥ S is established. If so, take the entire rectangular area to be shot as the sub-area to be shot, and jump to step 5); if not, go to step 4);

[0009] 4) Based on the single-shot area S0, the rectangular area to be shot is divided into sub-regions to be photographed, construct a set of sub-regions to be photographed G = {G1, G2, ..., G C}; G c represents the cth sub-area to be photographed; c = 1, 2, ..., C; represents rounding up; ΔS is the overlapping shooting area of ​​two adjacent sub-areas to be shot;

[0010] Record the coordinates of the four boundary points and the center point of the sub-area to be photographed, construct the coordinate matrix D of the sub-area to be photographed, and jump to step 6);

[0011] The coordinate matrix D of the sub-region to be photographed is as follows:

[0012]

[0013] Where D c,0 ,D c,1 ,D c,2 ,D c,3 ,D c,4 They represent the center point and four boundary points of the cth sub-area to be photographed respectively; (D c,0x ,D c,0y ) represents the coordinates of the center point of the cth sub-area to be photographed; (D c,1x ,D c,1y ),(D c,2x ,D c,2y ),(D c,3x ,D c,3y ),(D c,4x ,D c,4y ) represents the coordinates of the boundary point of the cth sub-area to be photographed; c = 1, 2, ..., C;

[0014] 5) Based on the coordinates of the center point of the sub-area to be photographed (D x ,D y ) and the current position coordinates of the drone (F x ,F y ), plan the flight path of the drone, make the drone rise at the current position until the distance from the ground reaches h, then move to the center point of the sub-area to be photographed at a speed v, take the geographic image of the sub-area to be photographed, transmit it to the processing end, end the shooting task, and jump to step 7);

[0015] 6) Based on the coordinate matrix D of the sub-area to be photographed and the current position coordinates of the drone (F x ,F y ), planning the flight path of the drone and taking geographic images of the sub-area to be photographed, the steps include:

[0016] 6.1) Calculate the longest single flight distance of the drone P max is the maximum available power of the drone; ΔP is the reserved power; P0 is the power consumption per unit time of the drone flight; v is the flight speed of the drone;

[0017] 6.2) Based on the coordinates of the boundary points of the sub-area to be photographed and the coordinates of the current position of the drone (F x ,F y ), determine the straight-line distance g0 between the current UAV and the center point of the nearest sub-area to be photographed;

[0018] 6.3) Randomly select a center point of a sub-region to be photographed as the starting point and the end point, and traverse all the center points of the sub-regions to be photographed with no duplication and shortest path as constraints to construct a candidate initial path with a length denoted as s1;

[0019] 6.4) Determine whether s1<g-2g0 is established. If so, jump to step 6.5); if not, jump to step 6.6);

[0020] 6.5) Taking the current position of the drone as the starting point and the end point, and with no duplication and shortest path as constraints, traverse the center points of all sub-areas to be photographed to construct the drone path;

[0021] Based on the current position of the drone (F x ,F y ) as the starting point, control the drone to rise at the current position until the distance from the ground reaches h, then fly along the drone path at a speed v, traverse all the center points of the sub-areas to be photographed, and capture the geographic image of the sub-area to be photographed at each center point, and enter step 7);

[0022] During the flight, each time the drone reaches the center point of a sub-area to be photographed, it takes a geographic image of the sub-area to be photographed, records the serial number of the sub-area to be photographed corresponding to the geographic image, and transmits it to the processing end;

[0023] 6.6) Construct the j-th flight path planning model of the UAV; the initial value of j is 1;

[0024] The objective function of the UAV j-th flight path planning model is as follows:

[0025] max[(n-1)b+ma,nb+(m-1)a] (2)

[0026] In the formula, n and m are the number of sub-areas to be photographed that the drone passes through in the horizontal and vertical directions; the distance between the center points of adjacent sub-areas to be photographed in the horizontal direction is b = |D c+1,0,x -D c,0,x |; The distance between the center points of adjacent sub-areas to be photographed in the vertical direction is a=|Dc+1,0,y -D c,0,y |;

[0027] The constraints of the first flight path planning model of the UAV include distance constraints, adjacent point constraints, and end point constraints;

[0028] The distance constraints are as follows:

[0029] (n-1)b+ma≤g-g0-g1 (3)

[0030] nb+(m-1)a≤g-g0-g1 (4)

[0031] Where g1 is the distance between the center point of the last sub-area to be photographed reached by the UAV during its j-th flight and the starting point of the UAV; the starting point of the UAV during its first flight is (F x ,F y );

[0032] The adjacent point constraint means that the i+1th sub-area to be photographed that the drone passes through is adjacent to the ith sub-area to be photographed that the drone passes through;

[0033] The end point constraint means that the last sub-area to be photographed that the drone passes through is adjacent to the first sub-area to be photographed;

[0034] 6.7) Solve the j-th flight path planning model of the UAV, construct a non-repetitive first flight path of the UAV, record all the sub-areas to be photographed that the UAV's j-th flight path passes through, and add a new sub-area to be photographed in the set of sub-areas to be photographed G = {G1, G2, ..., G C}, delete the sub-areas to be photographed where geographic images have been taken, and update the set of sub-areas to be photographed;

[0035] 6.8) Determine whether the updated set of sub-areas to be photographed is empty. If so, proceed to step 7). Otherwise, select the center point of the sub-area to be photographed that the drone has not passed through and is closest to the starting point of the jth flight path of the drone as the starting point of the j+1th flight, set j=j+1, charge the drone, and return to step 6.6);

[0036] 7) Preprocessing the geographic images taken by the drone; after preprocessing, if the number of geographic images taken by the drone is 1, proceed to step 9), otherwise, proceed to step 8);

[0037] 8) splicing the preprocessed geographic images based on the overlapping shooting area ΔS of two adjacent sub-areas to be photographed and the adjacent relationship between different sub-areas to be photographed;

[0038] 9) Import geographic images into the GIS system and present the spatial information of the area to be processed through maps.

[0039] Furthermore, the single maximum shooting area S0 of the drone at the height h is determined by the parameters of the camera carried by the drone.

[0040] Furthermore, the single maximum shooting area S0 of the drone at the height h is determined by the following method:

[0041] a1) Control the UAV to rise in the calibration area until the height above the calibration area reaches h, and take a rectangular image of the calibration area;

[0042] a2) Marking the drone shooting boundary line in the calibration area according to the rectangular image boundary of the calibration area;

[0043] a3) The drone repeatedly shoots the image of the calibration area, and determines whether the marked drone shooting boundary line is the rectangular image boundary of the calibration area. If so, proceed to step a5), otherwise, proceed to step a4);

[0044] a4) Adjust the drone shooting boundary line and return to step a3);

[0045] a5) Measure the size of the drone's shooting boundary area to calculate the maximum single shooting area S0 = S 0x ×S 0y ; S 0x , S 0y Take the length and width of the boundary area for the drone.

[0046] Furthermore, the overlapping shooting area ΔS of two adjacent sub-regions to be shot is determined as follows:

[0047] b1) Calculation command The candidate overlapping shooting area is an integer, denoted as ΔS0={ΔS 01 ,ΔS 02 ,...,ΔS 0s};

[0048] b2) Determine the minimum value of the candidate overlapping shooting area Is it true? If so, let the overlapping shooting area ΔS = minΔS0, otherwise, let

[0049] Further, when When is an integer, the sub-areas to be photographed are arranged in a matrix in the rectangular area to be photographed, which is recorded as the first matrix. The size of the first matrix is is the length and width of each sub-area to be photographed;

[0050] when When it is not an integer, the previous The sub-areas to be photographed are arranged in a matrix, which is recorded as the second matrix. The size of the second matrix is the remaining The sub-areas to be photographed are arranged in a matrix, which is recorded as the third matrix. The size of the second matrix is

[0051] Further, the steps of preprocessing the geographic images taken by the drone include:

[0052] c1) Move the neighborhood window Ω pixel by pixel in the geographic image k , calculate the entropy F(x,y) of each pixel and construct the entropy image F;

[0053] The entropy of a pixel point F(x,y) is as follows:

[0054]

[0055] In the formula, p l is the gray level l in the neighborhood window Ω k The probability of occurrence in; L is the maximum gray level; l is the gray level;

[0056] Among them, the gray level l is in the neighborhood window Ω k The probability of occurrence p l As shown below:

[0057]

[0058] Where n l Represents the number of pixels with gray level j; M k ×R k is the neighborhood window Ω k size;

[0059] c2) Based on the entropy image, a denoising model is established, namely:

[0060]

[0061] Where, I represents the geographic image taken by the drone; div and ▽ are the divergence operator and gradient operator; B is the denoised geographic image; Q is the Gaussian kernel function; F is the entropy image; g(▽B,F) and g(▽I,F) are diffusion coefficient functions;

[0062] Among them, the diffusion coefficient functions g(▽B,F) and g(▽I,F) at the pixel point (x,y) are as follows:

[0063]

[0064]

[0065]

[0066]

[0067] Where |▽B(x,y)| and |▽I(x,y)| are the gradient norms of the denoised geographic image and the geographic image taken by the drone at the pixel point (x,y); K is the preset edge intensity threshold; f(F(x,y)) is the entropy function; F max 、F min They represent the maximum and minimum values ​​of entropy respectively; T1 is the maximum value of the gradient modulus |▽B(x,y)|; T2 is the maximum value of the gradient modulus |▽I(x,y)|;

[0068] c3) De-noising the geographic image using a denoising model.

[0069] Further, the step of stitching the preprocessed geographic images includes:

[0070] d1) Based on the camera parameters, the conversion relationship between the geographic image size and the sub-area to be photographed is constructed, that is:

[0071]

[0072]

[0073] ΔS'=αβΔS(14)

[0074] In the formula, α and β are proportional coefficients; is the length and width of each sub-region to be photographed; e1 and e2 are the length and width of the geographic image; ΔS' is the overlapping area of ​​the geographic images of adjacent sub-regions to be photographed;

[0075] d2) Based on the adjacent relationship of the sub-areas to be photographed, determine the overlapping position and overlapping size between two adjacent geographic images; when the center points of the adjacent sub-areas to be photographed are located in the same horizontal direction, the overlapping size is When the center points of adjacent sub-areas to be photographed are located in the same vertical direction, the overlap size is

[0076] d3) splicing the geographic images based on the overlapping position and overlapping size between two adjacent geographic images;

[0077] d4) randomly selecting u geographic images and verifying the overlapping positions of these geographic images. If the verification passes, the stitching is terminated; if the verification fails, the process proceeds to step d5);

[0078] The steps to verify the overlapping positions of these geographic images include:

[0079] d4.1) converting the selected u geographic images into grayscale images;

[0080] d4.2) Record the grayscale value of the pixels in the overlapping area;

[0081] d4.3) Calculate the difference in grayscale values ​​between overlapping areas of adjacent grayscale images. If the difference is less than the threshold, the verification is successful. Otherwise, the verification fails.

[0082] d5) Convert all geographic images into grayscale images;

[0083] d6) stitching geographic images based on the difference in grayscale values.

[0084] Furthermore, if the geographic image is taken by the drone during one flight, the threshold is z1; if the geographic image is taken by the drone during multiple flights, the threshold is z2=2z1.

[0085] Further, the drone camera plane was parallel to the ground when capturing the image.

[0086] The technical effect of the present invention is undoubted, and the beneficial effects of the present invention are as follows:

[0087] 1) Considering the short flight time of drones, the drone path is planned according to the size of the area to be photographed to improve the efficiency of drone photography.

[0088] 2) Based on image entropy, the geographic images taken by drones are denoised to improve the accuracy of geographic images.

[0089] 3) A two-step method is used to stitch geographic images, which not only improves the stitching efficiency but also ensures the stitching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 The figure is a flow chart of the method. DETAILED DESCRIPTION

[0091] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0092] Embodiment 1:

[0093] A method for geospatialization of drone evidence photos, comprising the following steps:

[0094] 1) Obtain the boundary line of the area to be processed and record the outermost boundary points of the boundary line in four directions, denoted as A = {A W ,A E ,A S ,A N};

[0095] 2) Based on the outermost boundary point A = {A W ,A E ,A S ,A N}, construct the rectangular area to be photographed, and determine the area of ​​the rectangular area to be photographed The outermost boundary point A W , A E The linear distance in the horizontal direction; The outermost boundary point A S , A N The straight-line distance in the vertical direction;

[0096] 3) Determine the single shooting area S0 of the drone at the height h, and judge whether the single shooting area S0 ≥ S is established. If so, take the entire rectangular area to be shot as the sub-area to be shot, and jump to step 5); if not, go to step 4);

[0097] 4) Based on the single-shot area S0, the rectangular area to be shot is divided into sub-regions to be photographed, construct a set of sub-regions to be photographed G = {G1, G2, ..., G C}; G c represents the cth sub-area to be photographed; c = 1, 2, ..., C; represents rounding up; ΔS is the overlapping shooting area of ​​two adjacent sub-areas to be shot;

[0098] Record the coordinates of the four boundary points and the center point of the sub-area to be photographed, construct the coordinate matrix D of the sub-area to be photographed, and jump to step 6);

[0099] The coordinate matrix D of the sub-region to be photographed is as follows:

[0100]

[0101] Where D c,0 ,D c,1 ,D c,2 ,D c,3 ,D c,4 They represent the center point and four boundary points of the cth sub-area to be photographed respectively; (D c,0x ,D c,0y ) represents the coordinates of the center point of the cth sub-area to be photographed; (D c,1x ,D c,1y ),(D c,2x ,D c,2y ),(D c,3x ,D c,3y ),(D c,4x ,D c,4y) represents the coordinates of the boundary point of the cth sub-area to be photographed; c = 1, 2, ..., C;

[0102] 5) Based on the coordinates of the center point of the sub-area to be photographed (D x ,D y ) and the current position coordinates of the drone (F x ,F y ), plan the flight path of the drone, make the drone rise at the current position until the distance from the ground reaches h, then move to the center point of the sub-area to be photographed at a speed v, take the geographic image of the sub-area to be photographed, transmit it to the processing end, end the shooting task, and jump to step 7);

[0103] 6) Based on the coordinate matrix D of the sub-area to be photographed and the current position coordinates of the drone (F x ,F y ), planning the flight path of the drone and taking geographic images of the sub-area to be photographed, the steps include:

[0104] 6.1) Calculate the longest single flight distance of the drone P max is the maximum available power of the drone; ΔP is the reserved power; P0 is the power consumption per unit time of the drone flight; v is the flight speed of the drone;

[0105] 6.2) Based on the coordinates of the boundary points of the sub-area to be photographed and the coordinates of the current position of the drone (F x ,F y ), determine the straight-line distance g0 between the current UAV and the center point of the nearest sub-area to be photographed;

[0106] 6.3) Randomly select a center point of a sub-region to be photographed as the starting point and the end point, and traverse all the center points of the sub-regions to be photographed with no duplication and shortest path as constraints to construct a candidate initial path with a length denoted as s1;

[0107] 6.4) Determine whether s1<g-2g0 is established. If so, jump to step 6.5); if not, jump to step 6.6);

[0108] 6.5) Taking the current position of the drone as the starting point and the end point, and with no duplication and shortest path as constraints, traverse the center points of all sub-areas to be photographed to construct the drone path;

[0109] Based on the current position of the drone (F x ,F y ) as the starting point, control the drone to rise at the current position until the distance from the ground reaches h, then fly along the drone path at a speed v, traverse all the center points of the sub-areas to be photographed, and capture the geographic image of the sub-area to be photographed at each center point, and enter step 7);

[0110] During the flight, each time the drone reaches the center point of a sub-area to be photographed, it takes a geographic image of the sub-area to be photographed, records the serial number of the sub-area to be photographed corresponding to the geographic image, and transmits it to the processing end;

[0111] 6.6) Construct the j-th flight path planning model of the UAV; the initial value of j is 1;

[0112] The objective function of the UAV j-th flight path planning model is as follows:

[0113] max[(n-1)b+ma,nb+(m-1)a](2)

[0114] In the formula, n and m are the number of sub-areas to be photographed that the drone passes through in the horizontal and vertical directions; the distance between the center points of adjacent sub-areas to be photographed in the horizontal direction is b = |D c+1,0,x -D c,0,x |; The distance between the center points of adjacent sub-areas to be photographed in the vertical direction is a=|D c+1,0,y -D c,0,y |;

[0115] The constraints of the first flight path planning model of the UAV include distance constraints, adjacent point constraints, and end point constraints;

[0116] The distance constraints are as follows:

[0117] (n-1)b+ma≤g-g0-g1(3)

[0118] nb+(m-1)a≤g-g0-g1(4)

[0119] Where g1 is the distance between the center point of the last sub-area to be photographed reached by the UAV during its j-th flight and the starting point of the UAV; the starting point of the UAV during its first flight is (F x ,F y );

[0120] The adjacent point constraint means that the i+1th sub-area to be photographed that the drone passes through is adjacent to the ith sub-area to be photographed that the drone passes through;

[0121] The end point constraint means that the last sub-area to be photographed that the drone passes through is adjacent to the first sub-area to be photographed;

[0122] 6.7) Solve the j-th flight path planning model of the UAV, construct a non-repetitive first flight path of the UAV, record all the sub-areas to be photographed that the UAV's j-th flight path passes through, and add a new sub-area to be photographed in the set of sub-areas to be photographed G = {G1, G2, ..., G C}, delete the sub-areas to be photographed where geographic images have been taken, and update the set of sub-areas to be photographed;

[0123] 6.8) Determine whether the updated set of sub-areas to be photographed is empty. If so, proceed to step 7). Otherwise, select the center point of the sub-area to be photographed that the drone has not passed through and is closest to the starting point of the jth flight path of the drone as the starting point of the j+1th flight, set j=j+1, charge the drone, and return to step 6.6);

[0124] 7) Preprocessing the geographic images taken by the drone; after preprocessing, if the number of geographic images taken by the drone is 1, proceed to step 9), otherwise, proceed to step 8);

[0125] 8) splicing the preprocessed geographic images based on the overlapping shooting area ΔS of two adjacent sub-areas to be photographed and the adjacent relationship between different sub-areas to be photographed;

[0126] 9) Import geographic images into the GIS system and present the spatial information of the area to be processed through maps.

[0127] The maximum single shooting area S0 of the drone at the height h is determined by the parameters of the camera carried by the drone.

[0128] The maximum single-shot area S0 of the drone at height h is determined by:

[0129] a1) Control the UAV to rise in the calibration area until the height above the calibration area reaches h, and take a rectangular image of the calibration area;

[0130] a2) Marking the drone shooting boundary line in the calibration area according to the rectangular image boundary of the calibration area;

[0131] a3) The drone repeatedly shoots the image of the calibration area, and determines whether the marked drone shooting boundary line is the rectangular image boundary of the calibration area. If so, proceed to step a5), otherwise, proceed to step a4);

[0132] a4) Adjust the drone shooting boundary line and return to step a3);

[0133] a5) Measure the size of the drone's shooting boundary area to calculate the maximum single shooting area S0 = S 0x ×S 0y ; S 0x , S 0y Take the length and width of the boundary area for the drone.

[0134] The overlapping shooting area ΔS of two adjacent sub-areas to be shot is determined as follows:

[0135] b1) Calculation command The candidate overlapping shooting area is an integer, denoted as ΔS0={ΔS 01 ,ΔS02 ,...,ΔS 0s};

[0136] b2) Determine the minimum value of the candidate overlapping shooting area Is it true? If so, let the overlapping shooting area ΔS = minΔS0, otherwise, let

[0137] when When is an integer, the sub-areas to be photographed are arranged in a matrix in the rectangular area to be photographed, which is recorded as the first matrix. The size of the first matrix is is the length and width of each sub-area to be photographed;

[0138] when When it is not an integer, the previous The sub-areas to be photographed are arranged in a matrix, which is recorded as the second matrix. The size of the second matrix is the remaining The sub-areas to be photographed are arranged in a matrix, which is recorded as the third matrix. The size of the second matrix is

[0139] The steps for preprocessing geographic images taken by drones include:

[0140] c1) Move the neighborhood window Ω pixel by pixel in the geographic image k , calculate the entropy F(x,y) of each pixel and construct the entropy image F;

[0141] The entropy of a pixel point F(x,y) is as follows:

[0142]

[0143] In the formula, p l is the gray level l in the neighborhood window Ω k The probability of occurrence in; L is the maximum gray level; l is the gray level;

[0144] Among them, the gray level l is in the neighborhood window Ω k The probability of occurrence p l As shown below:

[0145]

[0146] Where n l Represents the number of pixels with gray level j; M k ×R k is the neighborhood window Ω k size;

[0147] c2) Based on the entropy image, a denoising model is established, namely:

[0148]

[0149] Where, I represents the geographic image taken by the drone; div and ▽ are the divergence operator and gradient operator; B is the denoised geographic image; Q is the Gaussian kernel function; F is the entropy image; g(▽B,F) and g(▽I,F) are diffusion coefficient functions;

[0150] Among them, the diffusion coefficient functions g(▽B,F) and g(▽I,F) at the pixel point (x,y) are as follows:

[0151]

[0152] Where |▽B(x,y)| and |▽I(x,y)| are the gradient norms of the denoised geographic image and the geographic image taken by the drone at the pixel point (x,y); K is the preset edge intensity threshold; f(F(x,y)) is the entropy function; F max 、F min They represent the maximum and minimum values ​​of entropy respectively; T1 is the maximum value of the gradient modulus |▽B(x,y)|; T2 is the maximum value of the gradient modulus |▽I(x,y)|;

[0153] c3) De-noising the geographic image using a denoising model.

[0154] The steps for stitching preprocessed geographic images include:

[0155] d1) Based on the camera parameters, the conversion relationship between the geographic image size and the sub-area to be photographed is constructed, that is:

[0156]

[0157] ΔS'=αβΔS(14)

[0158] In the formula, α and β are proportional coefficients; is the length and width of each sub-region to be photographed; e1 and e2 are the length and width of the geographic image; ΔS' is the overlapping area of ​​the geographic images of adjacent sub-regions to be photographed;

[0159] d2) Based on the adjacent relationship of the sub-areas to be photographed, determine the overlapping position and overlapping size between two adjacent geographic images; when the center points of the adjacent sub-areas to be photographed are located in the same horizontal direction, the overlapping size is When the center points of adjacent sub-areas to be photographed are located in the same vertical direction, the overlap size is

[0160] d3) splicing the geographic images based on the overlapping position and overlapping size between two adjacent geographic images;

[0161] d4) randomly selecting u geographic images and verifying the overlapping positions of these geographic images. If the verification passes, the stitching is terminated; if the verification fails, the process proceeds to step d5);

[0162] The steps to verify the overlapping positions of these geographic images include:

[0163] d4.1) converting the selected u geographic images into grayscale images;

[0164] d4.2) Record the grayscale value of the pixels in the overlapping area;

[0165] d4.3) Calculate the difference in grayscale values ​​between overlapping areas of adjacent grayscale images. If the difference is less than the threshold, the verification is successful. Otherwise, the verification fails.

[0166] d5) Convert all geographic images into grayscale images;

[0167] d6) stitching geographic images based on the difference in grayscale values.

[0168] If the geographic image is taken by the drone during one flight, the threshold is z1; if the geographic image is taken by the drone during multiple flights, the threshold is z2=2z1.

[0169] The drone camera plane was parallel to the ground when capturing the images.

[0170] Embodiment 2:

[0171] A method for geospatialization of drone evidence photos, comprising the following steps:

[0172] 1) Obtain the boundary line of the area to be processed and record the outermost boundary points of the boundary line in four directions, denoted as A = {A W ,A E ,A S ,A N};

[0173] 2) Based on the outermost boundary point A = {A W ,A E ,A S ,A N}, construct the rectangular area to be photographed, and determine the area of ​​the rectangular area to be photographed The outermost boundary point A W , A E The linear distance in the horizontal direction; The outermost boundary point A S , A N The straight-line distance in the vertical direction;

[0174] 3) Determine the single shooting area S0 of the drone at the height h, and judge whether the single shooting area S0 ≥ S is established. If so, take the entire rectangular area to be shot as the sub-area to be shot, and jump to step 5); if not, go to step 4);

[0175] 4) Based on the single-shot area S0, the rectangular area to be shot is divided into sub-regions to be photographed, construct a set of sub-regions to be photographed G = {G1, G2, ..., G C}; G c represents the cth sub-area to be photographed; c = 1, 2, ..., C; represents rounding up; ΔS is the overlapping shooting area of ​​two adjacent sub-areas to be shot;

[0176] Record the coordinates of the four boundary points and the center point of the sub-area to be photographed, construct the coordinate matrix D of the sub-area to be photographed, and jump to step 6);

[0177] The coordinate matrix D of the sub-region to be photographed is as follows:

[0178]

[0179] Where D c,0 ,D c,1 ,D c,2 ,D c,3 ,D c,4 They represent the center point and four boundary points of the cth sub-area to be photographed respectively; (D c,0x ,D c,0y ) represents the coordinates of the center point of the cth sub-area to be photographed; (D c,1x ,D c,1y ),(D c,2x ,D c,2y ),(D c,3x ,D c,3y ),(D c,4x ,D c,4y ) represents the coordinates of the boundary point of the cth sub-area to be photographed; c = 1, 2, ..., C;

[0180] 5) Based on the coordinates of the center point of the sub-area to be photographed (D x ,D y ) and the current position coordinates of the drone (F x ,F y ), plan the flight path of the drone, make the drone rise at the current position until the distance from the ground reaches h, then move to the center point of the sub-area to be photographed at a speed v, take the geographic image of the sub-area to be photographed, transmit it to the processing end, end the shooting task, and jump to step 7);

[0181] 6) Based on the coordinate matrix D of the sub-area to be photographed and the current position coordinates of the drone (F x ,F y ), planning the flight path of the drone and taking geographic images of the sub-area to be photographed, the steps include:

[0182] 6.1) Calculate the longest single flight distance of the drone P max is the maximum available power of the drone; ΔP is the reserved power; P0 is the power consumption per unit time of the drone flight; v is the flight speed of the drone;

[0183] 6.2) Based on the coordinates of the boundary points of the sub-area to be photographed and the coordinates of the current position of the drone (F x ,F y ), determine the straight-line distance g0 between the current UAV and the center point of the nearest sub-area to be photographed;

[0184] 6.3) Randomly select a center point of a sub-region to be photographed as the starting point and the end point, and traverse all the center points of the sub-regions to be photographed with no duplication and shortest path as constraints to construct a candidate initial path with a length denoted as s1;

[0185] 6.4) Determine whether s1<g-2g0 is established. If so, jump to step 6.5); if not, jump to step 6.6);

[0186] 6.5) Taking the current position of the drone as the starting point and the end point, and with no duplication and shortest path as constraints, traverse the center points of all sub-areas to be photographed to construct the drone path;

[0187] Based on the current position of the drone (F x ,F y ) as the starting point, control the drone to rise at the current position until the distance from the ground reaches h, then fly along the drone path at a speed v, traverse all the center points of the sub-areas to be photographed, and capture the geographic image of the sub-area to be photographed at each center point, and enter step 7);

[0188] During the flight, each time the drone reaches the center point of a sub-area to be photographed, it takes a geographic image of the sub-area to be photographed, records the serial number of the sub-area to be photographed corresponding to the geographic image, and transmits it to the processing end;

[0189] 6.6) Construct the j-th flight path planning model of the UAV; the initial value of j is 1;

[0190] The objective function of the UAV j-th flight path planning model is as follows:

[0191] max[(n-1)b+ma,nb+(m-1)a](2)

[0192] In the formula, n and m are the number of sub-areas to be photographed that the drone passes through in the horizontal and vertical directions; the distance between the center points of adjacent sub-areas to be photographed in the horizontal direction is b = |D c+1,0,x -D c,0,x |; The distance between the center points of adjacent sub-areas to be photographed in the vertical direction is a=|D c+1,0,y -D c,0,y |;

[0193] The constraints of the first flight path planning model of the UAV include distance constraints, adjacent point constraints, and end point constraints;

[0194] The distance constraints are as follows:

[0195] (n-1)b+ma≤g-g0-g1(3)

[0196] nb+(m-1)a≤g-g0-g1(4)

[0197] Where g1 is the distance between the center point of the last sub-area to be photographed reached by the UAV during its j-th flight and the starting point of the UAV; the starting point of the UAV during its first flight is (F x ,F y );

[0198] The adjacent point constraint means that the i+1th sub-area to be photographed that the drone passes through is adjacent to the ith sub-area to be photographed that the drone passes through;

[0199] The end point constraint means that the last sub-area to be photographed that the drone passes through is adjacent to the first sub-area to be photographed;

[0200] 6.7) Solve the j-th flight path planning model of the UAV, construct a non-repetitive first flight path of the UAV, record all the sub-areas to be photographed that the UAV's j-th flight path passes through, and add a new sub-area to be photographed in the set of sub-areas to be photographed G = {G1, G2, ..., G C}, delete the sub-areas to be photographed where geographic images have been taken, and update the set of sub-areas to be photographed;

[0201] 6.8) Determine whether the updated set of sub-areas to be photographed is empty. If so, proceed to step 7). Otherwise, select the center point of the sub-area to be photographed that the drone has not passed through and is closest to the starting point of the jth flight path of the drone as the starting point of the j+1th flight, set j=j+1, charge the drone, and return to step 6.6);

[0202] 7) Preprocessing the geographic images taken by the drone; after preprocessing, if the number of geographic images taken by the drone is 1, proceed to step 9), otherwise, proceed to step 8);

[0203] 8) splicing the preprocessed geographic images based on the overlapping shooting area ΔS of two adjacent sub-areas to be photographed and the adjacent relationship between different sub-areas to be photographed;

[0204] 9) Import geographic images into the GIS system and present the spatial information of the area to be processed through maps.

[0205] Embodiment 3:

[0206] A method for geospatialization of drone evidence photos, the technical content of which is the same as that of Example 2. Furthermore, the single maximum shooting area S0 of the drone at a height h is determined by the parameters of the camera carried by the drone.

[0207] Embodiment 4:

[0208] A method for geospatialization of drone evidence photos, the technical content of which is the same as any one of Embodiments 2-3, and further, the single maximum shooting area S0 of the drone at a height h is determined by the following method:

[0209] a1) Control the UAV to rise in the calibration area until the height above the calibration area reaches h, and take a rectangular image of the calibration area;

[0210] a2) Marking the drone shooting boundary line in the calibration area according to the rectangular image boundary of the calibration area;

[0211] a3) The drone repeatedly shoots the image of the calibration area, and determines whether the marked drone shooting boundary line is the rectangular image boundary of the calibration area. If so, proceed to step a5), otherwise, proceed to step a4);

[0212] a4) Adjust the drone shooting boundary line and return to step a3);

[0213] a5) Measure the size of the drone's shooting boundary area to calculate the maximum single shooting area S0 = S 0x ×S 0y ; S 0x , S 0y Take the length and width of the boundary area for the drone.

[0214] Embodiment 5:

[0215] A method for geospatialization of drone evidence photos, the technical content of which is the same as any one of Embodiments 2-4, and further, the overlapping shooting area ΔS of two adjacent sub-areas to be shot is determined as follows:

[0216] b1) Calculation command The candidate overlapping shooting area is an integer, denoted as ΔS0={ΔS 01 ,ΔS 02 ,...,ΔS 0s};

[0217] b2) Determine the minimum value of the candidate overlapping shooting area Is it true? If so, let the overlapping shooting area ΔS = minΔS0, otherwise, let

[0218] Embodiment 6:

[0219] A method for geospatialization of drone evidence photos, the technical content is the same as any one of embodiments 2-5, further, when When is an integer, the sub-areas to be photographed are arranged in a matrix in the rectangular area to be photographed, which is recorded as the first matrix. The size of the first matrix is is the length and width of each sub-area to be photographed;

[0220]

[0221] when When it is not an integer, the previous The sub-areas to be photographed are arranged in a matrix, which is recorded as the second matrix. The size of the second matrix is the remaining The sub-areas to be photographed are arranged in a matrix, which is recorded as the third matrix. The size of the second matrix is

[0222] Embodiment 7:

[0223] A method for geospatialization of drone evidence photos, the technical content of which is the same as any one of Embodiments 2-6, and further, the step of preprocessing the geographic image taken by the drone includes:

[0224] c1) Move the neighborhood window Ω pixel by pixel in the geographic image k , calculate the entropy F(x,y) of each pixel and construct the entropy image F;

[0225] The entropy of a pixel point F(x,y) is as follows:

[0226]

[0227] In the formula, p l is the gray level l in the neighborhood window Ω k The probability of occurrence in; L is the maximum gray level; l is the gray level;

[0228] Among them, the gray level l is in the neighborhood window Ω k The probability of occurrence p l As shown below:

[0229]

[0230] Where n lRepresents the number of pixels with gray level j; M k ×R k is the neighborhood window Ω k size;

[0231] c2) Based on the entropy image, a denoising model is established, namely:

[0232]

[0233] Where, I represents the geographic image taken by the drone; div and ▽ are the divergence operator and gradient operator; B is the denoised geographic image; Q is the Gaussian kernel function; F is the entropy image; g(▽B,F) and g(▽I,F) are diffusion coefficient functions;

[0234] Among them, the diffusion coefficient functions g(▽B,F) and g(▽I,F) at the pixel point (x,y) are as follows:

[0235]

[0236] Where |▽B(x,y)| and |▽I(x,y)| are the gradient norms of the denoised geographic image and the geographic image taken by the drone at the pixel point (x,y); K is the preset edge intensity threshold; f(F(x,y)) is the entropy function; F max 、F min They represent the maximum and minimum values ​​of entropy respectively; T1 is the maximum value of the gradient modulus |▽B(x,y)|; T2 is the maximum value of the gradient modulus |▽I(x,y)|;

[0237] c3) De-noising the geographic image using a denoising model.

[0238] Embodiment 8:

[0239] A method for geospatialization of drone evidence photos, the technical content of which is the same as any one of Embodiments 2-7, and further, the step of splicing the pre-processed geographic images comprises:

[0240] d1) Based on the camera parameters, the conversion relationship between the geographic image size and the sub-area to be photographed is constructed, that is:

[0241]

[0242] ΔS'=αβΔS(14)

[0243] In the formula, α and β are proportional coefficients; is the length and width of each sub-region to be photographed; e1 and e2 are the length and width of the geographic image; ΔS' is the overlapping area of ​​the geographic images of adjacent sub-regions to be photographed;

[0244] d2) Based on the adjacent relationship of the sub-areas to be photographed, determine the overlapping position and overlapping size between two adjacent geographic images; when the center points of the adjacent sub-areas to be photographed are located in the same horizontal direction, the overlapping size is When the center points of adjacent sub-areas to be photographed are located in the same vertical direction, the overlap size is

[0245] d3) splicing the geographic images based on the overlapping position and overlapping size between two adjacent geographic images;

[0246] d4) randomly selecting u geographic images and verifying the overlapping positions of these geographic images. If the verification passes, the stitching is terminated; if the verification fails, the process proceeds to step d5);

[0247] The steps to verify the overlapping positions of these geographic images include:

[0248] d4.1) converting the selected u geographic images into grayscale images;

[0249] d4.2) Record the grayscale value of the pixels in the overlapping area;

[0250] d4.3) Calculate the difference in grayscale values ​​between overlapping areas of adjacent grayscale images. If the difference is less than the threshold, the verification is successful. Otherwise, the verification fails.

[0251] d5) Convert all geographic images into grayscale images;

[0252] d6) stitching geographic images based on the difference in grayscale values.

[0253] Embodiment 9:

[0254] A method for geospatialization of drone evidence photos, the technical content of which is the same as any one of Examples 2-8. Further, if the geographic image is taken by the drone during a single flight, the threshold is z1; if the geographic image is taken by the drone during multiple flights, the threshold is z2=2z1.

[0255] Embodiment 10:

[0256] A method for geospatialization of drone evidence photos, the technical content of which is the same as any one of Examples 2-9, and further, when taking images, the drone camera plane is parallel to the ground.

Claims

1. A method for geospatialization of drone evidence photos, characterized in that: The following steps are involved: 1) Obtain the boundary line of the area to be processed and record the outermost boundary points of the boundary line in four directions, denoted as A = {A W ,A E ,A S ,A N }; 2) Based on the outermost boundary point A = {A W ,A E ,A S ,A N }, construct the rectangular area to be photographed, and determine the area of ​​the rectangular area to be photographed The outermost boundary point A W , A E The linear distance in the horizontal direction; The outermost boundary point A S , A N The straight-line distance in the vertical direction; 3) Determine the single shooting area S0 of the drone at the height h, and judge whether the single shooting area S0 ≥ S is established. If so, take the entire rectangular area to be shot as the sub-area to be shot, and jump to step 5); if not, go to step 4); 4) Based on the single-shot area S0, the rectangular area to be photographed is divided into sub-regions to be photographed, construct a set of sub-regions to be photographed G = {G1, G2, ..., G C }; G c represents the cth sub-area to be photographed; c = 1, 2, ..., C; represents rounding up; ΔS is the overlapping shooting area of ​​two adjacent sub-areas to be shot; Record the coordinates of the four boundary points and the center point of the sub-area to be photographed, construct the coordinate matrix D of the sub-area to be photographed, and jump to step 6); The coordinate matrix D of the sub-region to be photographed is as follows: Where D c,0 ,D c,1 ,D c,2 ,D c,3 ,D c,4 They represent the center point and four boundary points of the cth sub-area to be photographed respectively; (D c,0x ,D c,0y ) represents the coordinates of the center point of the cth sub-area to be photographed; (D c,1x ,D c,1y ),(D c,2x ,D c,2y ),(D c,3x ,D c,3y ),(D c,4x ,D c,4y ) represents the coordinates of the boundary point of the cth sub-area to be photographed; c = 1, 2, ..., C; 5) Based on the coordinates of the center point of the sub-area to be photographed (D x ,D y ) and the current position coordinates of the drone (F x ,F y ), plan the flight path of the drone, make the drone rise at the current position until the distance from the ground reaches h, then move to the center point of the sub-area to be photographed at a speed v, take the geographic image of the sub-area to be photographed, transmit it to the processing end, end the shooting task, and jump to step 7); 6) Based on the coordinate matrix D of the sub-area to be photographed and the current position coordinates of the drone (F x ,F y ), planning the flight path of the drone and taking geographic images of the sub-area to be photographed, the steps include: 6.1) Calculate the longest single flight distance of the drone P max is the maximum available power of the drone; ΔP is the reserved power; P0 is the power consumption per unit time of the drone flight; v is the flight speed of the drone; 6.2) Based on the coordinates of the boundary points of the sub-area to be photographed and the coordinates of the current position of the drone (F x ,F y ), determine the straight-line distance g0 between the current UAV and the center point of the nearest sub-area to be photographed; 6.3) Randomly select a center point of a sub-region to be photographed as the starting point and the end point, and traverse all the center points of the sub-regions to be photographed with no duplication and shortest path as constraints to construct a candidate initial path with a length denoted as s1; 6.4) Determine whether s1<g-2g0 is established. If so, jump to step 6.5); if not, jump to step 6.6); 6.5) Taking the current position of the drone as the starting point and the end point, and with no duplication and shortest path as constraints, traverse the center points of all sub-areas to be photographed to construct the drone path; Based on the current position of the drone (F x ,F y ) as the starting point, control the drone to rise at the current position until the distance from the ground reaches h, then fly along the drone path at a speed v, traverse all the center points of the sub-areas to be photographed, and capture the geographic image of the sub-area to be photographed at each center point, and enter step 7); During the flight, each time the drone reaches the center point of a sub-area to be photographed, it takes a geographic image of the sub-area to be photographed, records the serial number of the sub-area to be photographed corresponding to the geographic image, and transmits it to the processing end; 6.6) Construct the j-th flight path planning model of the UAV; the initial value of j is 1; The objective function of the UAV j-th flight path planning model is as follows: max[(n-1)b+ma,nb+(m-1)a] (2) In the formula, n and m are the number of sub-areas to be photographed that the drone passes through in the horizontal and vertical directions; the distance between the center points of adjacent sub-areas to be photographed in the horizontal direction is b = |D c+1,0,x -D c,0,x |; The distance between the center points of adjacent sub-areas to be photographed in the vertical direction is a=|D c+1,0,y -D c,0,y |; The constraints of the first flight path planning model of the UAV include distance constraints, adjacent point constraints, and end point constraints; The distance constraints are as follows: (n-1)b+ma≤g-g0-g1(3) nb+(m-1)a≤g-g0-g1(4) Where g1 is the distance between the center point of the last sub-area to be photographed reached by the UAV during its j-th flight and the starting point of the UAV; the starting point of the UAV during its first flight is (F x ,F y ); The adjacent point constraint means that the i+1th sub-area to be photographed that the drone passes through is adjacent to the ith sub-area to be photographed that the drone passes through; The end point constraint means that the last sub-area to be photographed that the drone passes through is adjacent to the first sub-area to be photographed; 6.7) Solve the j-th flight path planning model of the UAV, construct a non-repetitive first flight path of the UAV, record all the sub-areas to be photographed that the UAV's j-th flight path passes through, and add a new sub-area to be photographed in the set of sub-areas to be photographed G = {G1, G2, ..., G C }, delete the sub-areas to be photographed where geographic images have been taken, and update the set of sub-areas to be photographed; 6.8) Determine whether the updated set of sub-areas to be photographed is empty. If so, proceed to step 7). Otherwise, select the center point of the sub-area to be photographed that the drone has not passed through and is closest to the starting point of the jth flight path of the drone as the starting point of the j+1th flight, set j=j+1, charge the drone, and return to step 6.6); 7) Preprocessing the geographic images taken by the drone; after preprocessing, if the number of geographic images taken by the drone is 1, proceed to step 9), otherwise, proceed to step 8); 8) splicing the preprocessed geographic images based on the overlapping shooting area ΔS of two adjacent sub-areas to be photographed and the adjacent relationship between different sub-areas to be photographed; 9) Import geographic images into the GIS system and present the spatial information of the area to be processed through maps.

2. The method for geospatialization of drone evidence photos according to claim 1, characterized in that: The maximum single shooting area S0 of the drone at the height h is determined by the parameters of the camera carried by the drone.

3. The method for geospatialization of drone evidence photos according to claim 2, characterized in that: The maximum single-shot area S0 of the drone at height h is determined by: 1) Control the drone to rise in the calibration area until the height above the calibration area reaches h, and take a rectangular image of the calibration area; 2) Mark the drone shooting boundary line in the calibration area according to the rectangular image boundary of the calibration area; 3) The drone repeatedly shoots the image of the calibration area, and determines whether the marked drone shooting boundary line is the rectangular image boundary of the calibration area. If so, proceed to step 5, otherwise, proceed to step 4); 4) Adjust the drone shooting boundary line and return to step 3); 5) Measure the size of the drone's shooting boundary area to calculate the maximum single shooting area S0 = S 0x ×S 0y ; S 0x , S 0y Take the length and width of the boundary area for the drone.

4. The method for geospatialization of drone evidence photos according to claim 1, characterized in that: The overlapping shooting area ΔS of two adjacent sub-areas to be shot is determined as follows: 1) Calculation command The candidate overlapping shooting area is an integer, denoted as ΔS0={ΔS 01 ,ΔS 02 ,...,ΔS 0s }; 2) Determine the minimum value of the candidate overlapping shooting area Is it true? If so, let the overlapping shooting area ΔS = minΔS0, otherwise, let 5. The method for geospatialization of drone evidence photos according to claim 4, characterized in that: when When is an integer, the sub-areas to be photographed are arranged in a matrix in the rectangular area to be photographed, which is recorded as the first matrix. The size of the first matrix is is the length and width of each sub-area to be photographed; when When it is not an integer, the previous The sub-areas to be photographed are arranged in a matrix, which is recorded as the second matrix. The size of the second matrix is the remaining The sub-areas to be photographed are arranged in a matrix, which is recorded as the third matrix. The size of the second matrix is 6. The method for geospatialization of drone evidence photos according to claim 1, characterized in that: The steps for preprocessing geographic images taken by drones include: 1) Move the neighborhood window Ω pixel by pixel in the geographic image k , calculate the entropy F(x,y) of each pixel and construct the entropy image F; The entropy of a pixel point F(x,y) is as follows: In the formula, p l is the gray level l in the neighborhood window Ω k The probability of occurrence in; L is the maximum gray level; l is the gray level; Among them, the gray level l is in the neighborhood window Ω k The probability of occurrence p l As shown below: Where n l Represents the number of pixels with gray level j; M k ×R k is the neighborhood window Ω k size; 2) Based on the entropy image, a denoising model is established, namely: Where I represents the geographic image taken by the drone; div and are the divergence operator and the gradient operator; B is the denoised geographic image; Q is the Gaussian kernel function; F is the entropy image; is the diffusion coefficient function; Among them, the diffusion coefficient function at the pixel point (x, y) is They are as follows: In the formula, is the gradient modulus of the denoised geographic image and the geographic image taken by the drone at the pixel point (x, y); K is the preset edge intensity threshold; f(F(x, y) is the entropy function; F max 、F min They represent the maximum and minimum values ​​of entropy respectively; T1 is the gradient modulus The maximum value of T2 is the gradient mode. The maximum value of 3) Use the denoising model to denoise the geographic image.

7. The method for geospatialization of drone evidence photos according to claim 1, characterized in that: The steps for stitching preprocessed geographic images include: 1) Based on the camera parameters, the conversion relationship between the geographic image size and the sub-area to be photographed is constructed, that is: ΔS'=αβΔS(14) In the formula, α and β are proportional coefficients; is the length and width of each sub-region to be photographed; e1 and e2 are the length and width of the geographic image; ΔS' is the overlapping area of ​​the geographic images of adjacent sub-regions to be photographed; 2) Based on the adjacent relationship of the sub-areas to be photographed, determine the overlapping position and overlapping size between two adjacent geographic images; when the center points of adjacent sub-areas to be photographed are located in the same horizontal direction, the overlapping size is When the center points of adjacent sub-areas to be photographed are located in the same vertical direction, the overlap size is 3) stitching geographic images based on the overlapping position and overlapping size between two adjacent geographic images; 4) Randomly select u geographic images and verify the overlapping positions of these geographic images. If the verification passes, the stitching ends. If the verification fails, go to step 5); 5) Convert all geographic images into grayscale images; 6) Based on the difference in grayscale values, the geographic images are stitched together.

8. The method for geospatialization of drone evidence photos according to claim 7, characterized in that: The steps to verify the overlapping positions of these geographic images include: 4.1) Convert the selected u geographic images into grayscale images; 4.2) Record the grayscale value of the pixels in the overlapping area; 4.3) Calculate the difference in grayscale values ​​between overlapping areas of adjacent grayscale images. If it is less than the threshold, the verification passes; otherwise, the verification fails.

9. The method for geospatialization of drone evidence photos according to claim 7, characterized in that: If the geographic image is taken by the drone during one flight, the threshold is z1; if the geographic image is taken by the drone during multiple flights, the threshold is z2=2z1.

10. The method for geospatialization of drone evidence photos according to claim 1, characterized in that: The drone camera plane was parallel to the ground when the images were captured.

Images