A terrain extraction method, device and equipment based on oblique photography and a medium

Abstract

The present application provides a kind of topographic extraction method, device, equipment and medium based on oblique photography, it is related to geological survey technical field, including generating orthophoto and digital surface model;Determine the crown area and crown center height of each representative tree;Allometric growth formula is established based on the crown area and crown center height corresponding to multiple experimental trees;A plurality of group parameters are sequentially verified based on the crown area and crown height corresponding to multiple verification trees;Determine target allometric growth formula based on target group parameter;Obtain the digital elevation model of terrain.The present application determines the representative tree located at the boundary of forest based on the data obtained by existing unmanned aerial vehicle oblique photography, and determines the required parameters by the crown area and crown center height of the representative tree, establishes allometric growth formula, and the established allometric growth formula can better reflect the growth conditions of all trees in the current forest, so that the height of the tree can be accurately inverted, and a more accurate terrain model is obtained.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and more specifically, to a method, apparatus, equipment, and medium for terrain extraction based on oblique photography. Background Technology

[0002] Currently, the conventional method for extracting the real terrain under eucalyptus forests using drone technology is: constructing the real terrain under eucalyptus forests based on oblique photogrammetry. This method uses a multi-lens camera mounted on a drone to collect the outline and texture information of ground features from different angles to generate a digital surface model. Then, the height of trees in the area is manually counted to deduce the real terrain under the eucalyptus forest. However, this method is labor-intensive and inefficient. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, device, and medium for terrain extraction based on oblique photogrammetry, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] In a first aspect, this application provides a terrain extraction method based on oblique photogrammetry, comprising:

[0005] Oblique photographic data of the forest is acquired, and orthophotos and digital surface models are generated based on the oblique photographic data;

[0006] Based on the orthophoto, all representative trees are identified, and the canopy area and canopy center height of each representative tree are determined. The representative tree is the tree located at the forest boundary and whose canopy center is closest to the boundary.

[0007] All representative trees are divided into multiple experimental trees and multiple validation trees. Based on the canopy area and canopy center height of the multiple experimental trees, an allometric growth formula is established, and multiple sets of parameters are calculated accordingly. Each set of parameters includes growth parameters and corresponding correction coefficients.

[0008] Based on the canopy area and canopy height corresponding to multiple verification trees, multiple groups of parameters are verified sequentially, and the target group of parameters is determined from them;

[0009] The target allometric growth formula is determined based on the target group parameters, and the crown center height of all trees in the orthophoto is calculated based on the target allometric growth formula.

[0010] A digital elevation model of the terrain is obtained based on the elevation values ​​in the digital surface model and the calculated crown center height of all trees.

[0011] Secondly, this application also provides a terrain extraction device based on oblique photography, comprising:

[0012] The acquisition unit is used to acquire oblique photographic data of the forest and generate orthophotos and digital surface models based on the oblique photographic data.

[0013] The first determining unit is used to determine all representative trees based on the orthophoto, and to determine the canopy area and canopy center height of each representative tree, wherein the representative tree is the tree located at the forest boundary and whose canopy center is closest to the boundary;

[0014] The first partitioning unit is used to divide all representative trees into multiple experimental trees and multiple verification trees. Based on the canopy area and canopy center height of the multiple experimental trees, an allometric growth formula is established, and multiple sets of parameters are calculated accordingly. Each set of parameters includes growth parameters and corresponding correction coefficients.

[0015] The verification unit is used to sequentially verify multiple groups of parameters based on the canopy area and canopy height corresponding to multiple verification trees, and determine the target group parameters from them;

[0016] The second determining unit is used to determine the target allometric growth formula based on the target group parameters, and to calculate the crown center height of all trees in the orthophoto based on the target allometric growth formula.

[0017] The first calculation unit is used to obtain a digital elevation model of the terrain based on the elevation values ​​in the digital surface model and the calculated crown center height of all trees.

[0018] Thirdly, this application also provides a terrain extraction device based on oblique photography, comprising:

[0019] Memory, used to store computer programs;

[0020] A processor is used to implement the steps of the terrain extraction method based on oblique photogrammetry when executing the computer program.

[0021] Fourthly, this application also provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the terrain extraction method based on oblique photography described above.

[0022] The beneficial effects of this invention are as follows:

[0023] This invention identifies representative trees at the forest boundary based on data obtained from existing UAV oblique photography, and determines the required parameters by using the canopy area and canopy center height of the representative trees. It then establishes an allometric growth formula, which can better reflect the growth status of all trees in the current forest, thereby accurately retrieving the tree height and obtaining a more accurate terrain model.

[0024] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the terrain extraction method based on oblique photography as described in an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of the Gaussian filtering process described in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the convolution factor described in the embodiments of the present invention;

[0029] Figure 4 This is a schematic diagram of the gradient direction as described in the embodiments of the present invention;

[0030] Figure 5 This is a schematic diagram of non-maximum suppression as described in an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the terrain extraction device based on oblique photography as described in an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the terrain extraction device based on oblique photography as described in an embodiment of the present invention.

[0033] Marked in the image:

[0034] 100. Acquisition Unit; 200. First Determination Unit; 300. First Division Unit; 400. Verification Unit; 500. Second Determination Unit; 600. First Calculation Unit; 210. First Processing Unit; 220. Second Division Unit; 230. First Assignment Unit; 240. Conversion Unit; 250. Third Determination Unit; 260. Fourth Determination Unit; 270. Fifth Determination Unit; 211. Second Processing Unit; 212. Sixth Calculation Unit; 213. Suppression Processing Unit; 214. Third Assignment Unit; 215. Tracking Unit; 2 16. Seventh Determination Unit; 310. First Substitution Unit; 320. First Arrangement Unit; 330. Third Division Unit; 340. First Assignment Unit; 410. Fourth Division Unit; 420. Second Substitution Unit; 430. Second Calculation Unit; 440. Third Calculation Unit; 450. Second Arrangement Unit; 460. Second Assignment Unit; 470. Fourth Calculation Unit; 480. Comparison Unit; 490. Sixth Determination Unit; 610. Second Assignment Unit; 620. Interpolation Unit; 630. Fifth Calculation Unit; 640. Generation Unit;

[0035] 800. Terrain extraction device based on oblique photography; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0037] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0038] Example 1:

[0039] This embodiment provides a terrain extraction method based on oblique photogrammetry.

[0040] See Figure 1 The figure shows that the method includes steps S100, S200, S300, S400, S500 and S600.

[0041] Step S100. Obtain oblique photogrammetry data of the forest, and generate orthophotos and digital surface models based on the oblique photogrammetry data;

[0042] Specifically, oblique photogrammetry data is usually obtained from drone aerial photography. After oblique photogrammetry data is modeled using oblique photogrammetry modeling tools, orthophoto DOM and digital surface model (DSM) can be obtained.

[0043] Step S200. Based on the orthophoto, determine all representative trees and determine the canopy area and canopy center height of each representative tree. The representative tree is the tree located at the forest boundary and whose canopy center is closest to the boundary.

[0044] Specifically, step S200 includes:

[0045] Step S210. Process the orthophoto based on the edge detector to determine the forest area and forest boundary in the orthophoto;

[0046] Specifically, step S210 includes:

[0047] Step S211. Perform Gaussian filtering on the orthophoto to obtain the first processed image;

[0048] Specifically, Gaussian filtering is applied to the orthophoto to smooth non-edge areas with weak texture, thereby removing noise and obtaining more accurate edges. Figure 2 As shown, a weighted average value is calculated around pixel S using a 5×5 Gaussian filter to obtain the final filtering result T. The size of the filter used for different orthophotos is not different, and no special restrictions are made here.

[0049] Step S212. Calculate the gradient of the first processed image and obtain gradient values ​​in different directions;

[0050] Specifically, using the Sobel convolution factor S x and S y To calculate the first processed image, the convolution factor is as follows: Figure 3 As shown, the horizontal and vertical gradients of each pixel are calculated, thereby determining the gradient magnitude and direction of each pixel. The formula for calculating the gradient magnitude is:

[0051]

[0052] Where G represents the gradient magnitude of a pixel; dx d represents the horizontal gradient of a pixel; y Represents the vertical gradient of a pixel;

[0053] The formula for calculating the gradient direction is:

[0054]

[0055] Where θ represents the gradient direction of the pixel; d x d represents the horizontal gradient of a pixel; y Represents the vertical gradient of a pixel;

[0056] To simplify the gradient direction calculation, the gradient direction of each pixel is discretized into eight directions (up, down, left, right, upper right, upper left, lower left, lower right) based on the directions of the eight pixels surrounding it. The nearest value is then selected from the calculated results. Figure 4 The diagram shown illustrates the gradient direction.

[0057] Step S213. Perform non-maximum suppression based on gradient values ​​in different directions to obtain the second processed image;

[0058] Specifically, after obtaining the gradient magnitude and direction of each pixel, the system iterates through the pixels in the image, removing all non-edge points. In practice, each pixel is iterated one by one, determining whether the current pixel is the maximum value with the same gradient direction among its surrounding pixels. Based on the determination result, a decision is made on whether to suppress the point. Specifically, for each pixel, it is determined whether the pixel is a local maximum in the positive / negative gradient direction. If it is, the point is retained; otherwise, it is suppressed and its value is reset to zero. Figure 5 As shown, points A, B, and C have the same direction (the gradient direction is perpendicular to the edge). We determine whether these three points are their respective local maxima. If they are, we retain the point; otherwise, we suppress the point and set it to zero. After the above processing, for several edge points in the same direction, basically only one is retained, thereby eliminating the stray response caused by edge detection and achieving the purpose of edge refinement.

[0059] Step S214. Reassign values ​​to the second processed image based on the preset maximum and minimum thresholds to obtain the third processed image;

[0060] Specifically, two thresholds are set: a high threshold (maxVal) and a low threshold (minVal). These two thresholds are used to remove false edges caused by noise. The threshold values ​​are determined based on the specific image conditions. Each pixel P(x,y) in the image is reassigned based on the following formula:

[0061]

[0062] If P(x,y)=2, then mark the current edge pixel as a strong edge point and keep it; if P(x,y)=1, then mark the current edge pixel as a virtual edge point and keep it; if P(x,y)=0, then suppress the current edge pixel and discard it.

[0063] Step S215. Track the boundaries in the third-processed image based on hysteresis techniques to obtain continuous boundary curves;

[0064] Specifically, the edge of the forest is a continuous curve. A hysteresis technique is used to remove noise pixels that do not participate in the formation of the curve, thus completing the entire edge. Starting from any strong edge point, the entire edge is tracked in the image. A virtual edge point is only accepted when it connects to a strong edge point. Once it participates in the completion of the entire edge, the virtual edge point is redefined as a strong edge point; otherwise, it is discarded. This process is repeated until all edges in the entire image have been tracked.

[0065] Step S216. Determine the forest area and forest boundary based on the boundary curve.

[0066] Specifically, a 1.0m buffer zone is created on both sides of the extracted edge. The area within the buffer zone with a significantly higher elevation than the other side is the forest area, and the edge is the forest boundary.

[0067] Step S220. Divide the orthophoto corresponding to the forest area based on the Otsu's method to obtain the canopy area and non-canopy area;

[0068] Specifically, within forested areas, tree canopies are typically brighter than shaded gaps and overlapping canopies; that is, tree canopies appear brighter in orthophotos acquired by drones. The Otsu's method (maximum between-class variance) is used to determine this by setting a threshold P. θ The orthophoto is segmented into binary images to detect brighter canopy regions. The orthophoto brightness range of the brighter canopy regions is statistically analyzed in local areas of the image. c ∈[P cl P ch The pixel brightness range in the darker gaps in the forest and overlapping areas of the tree canopy is P. g ∈[P gl P gh To determine, P θ Located at P c and P g Between these, the area corresponding to the high brightness range is divided into the canopy area, and the area corresponding to the low brightness range is divided into the non-canopy area.

[0069] Step S230. Assign the pixels of the image corresponding to the canopy region to the first set threshold, and assign the pixels of the image corresponding to the non-canopy region to the second set threshold to obtain a pixel map;

[0070] Specifically, the pixel P in both the canopy and non-canopy regions is re-assigned based on the following formula, where... Obtain the pixel image after assignment.

[0071] Step S240. Convert the pixel image into a vector plane to obtain multiple independent vector planes;

[0072] Specifically, the assigned pixel image is converted into a vector surface, and small patches are removed to eliminate some defects caused by incorrect segmentation.

[0073] Step S250. Based on multiple independent vector planes, determine multiple tree canopies and obtain the canopy area of ​​each tree canopy;

[0074] Step S260. Based on the digital surface model, determine the point with the largest elevation from multiple independent vector surfaces, and use it as the center of the tree canopy;

[0075] Step S270. Based on the forest boundary and the center of the canopy, identify all representative trees.

[0076] Specifically, based on the forest boundary, trees located on the boundary are identified. Among them, the tree located on the boundary and whose crown center is closest to the boundary is defined as the representative tree. The representative tree is located on the boundary and is not obstructed by other trees. The crown center height of the representative tree can be obtained based on the oblique photogrammetry model and the digital surface model.

[0077] Step S300. Divide all representative trees into multiple experimental trees and multiple validation trees. Based on the canopy area and canopy center height of the multiple experimental trees, establish an allometric growth formula and calculate multiple sets of parameters. Each set of parameters includes growth parameters and corresponding correction coefficients.

[0078] Specifically, establishing the allometric growth formula for the current forest requires establishing and verifying it based on representative trees in the current forest, so as to obtain an allometric growth formula that can accurately reflect the growth status of all trees in the current forest. Based on the location and number of representative trees, they are evenly divided into X experimental trees and Y verification trees, of which 80% of the total number of representative trees are experimental trees and 20% of the total number of representative trees are verification trees.

[0079] Specifically, step S300 includes:

[0080] Step S310. Substitute the canopy area and canopy center height of each experimental tree into the allometric growth formula in turn to calculate multiple sets of parameters. Each set of parameters includes a first growth parameter and a corresponding first correction coefficient.

[0081] Specifically, the allometric growth formula is:

[0082] Coeff a = (Ha) / ln(CA)

[0083] Where CA is the canopy area; H is the height of the canopy center; Coeff a Here are the growth parameters; 'a' is the correction factor.

[0084] By substituting the canopy area and canopy center height of each experimental tree into the above formula, the corresponding first growth parameter and first correction coefficient can be calculated, thereby obtaining multiple sets of parameters.

[0085] Step S320. Arrange the first growth parameter in the multiple sets of parameters in ascending order to obtain the first sequence;

[0086] Step S330. Divide the first sequence into uniform parts and use the multiple boundary values ​​of the division as multiple second growth parameters;

[0087] Specifically, the calculated first growth parameters are sorted from smallest to largest and divided into 20 equal parts, resulting in 21 critical values. These 21 critical values ​​are then redefined as second growth parameters.

[0088] Step S340. Use the correction coefficients corresponding to the multiple second growth parameters as multiple second correction coefficients.

[0089] Specifically, the first correction coefficient corresponding to the determined second growth parameter is renamed the second correction coefficient.

[0090] Step S400. Based on the canopy area and canopy height of multiple verification trees, verify multiple group parameters in sequence, and determine the target group parameters from them;

[0091] Specifically, by verifying the tree, we can determine which set of parameters among the combinations identified above can accurately reflect the growth status of all trees in the tree area.

[0092] Specifically, step S400 includes:

[0093] Step S410. Divide the multiple second growth parameters and multiple second correction coefficients into multiple groups of parameters based on their correspondence;

[0094] Step S420. Substitute multiple sets of parameters into the allometric growth formula in sequence to obtain multiple initial formulas;

[0095] Specifically, an initial formula is used to calculate the crown center height of the verification tree. The initial formula is as follows:

[0096]

[0097] in, Coeff_a represents the crown center height of the nth experimental tree calculated from the mth set of parameters, where m = 1, 2, ..., 21; m CA is the second growth parameter in the m-th parameter group; n Let α be the canopy area of ​​the nth experimental tree; m This is the second correction coefficient in the m-th group of parameters.

[0098] Step S430. Based on the initial formula and the canopy area of ​​multiple verification trees, calculate the calculated canopy center height of multiple verification trees;

[0099] Step S440. Calculate the difference between the calculated crown center height of multiple verification trees and the crown center height of the corresponding position in the digital surface model to obtain multiple first differences;

[0100] Step S450. Arrange the multiple first differences in ascending order to obtain the second sequence;

[0101] Step S460. Obtain the first difference value corresponding to the middle position of the second sequence, and use it as the second difference value;

[0102] Step S470. Calculate based on multiple initial formulas to obtain multiple second differences;

[0103] Step S480. Compare multiple second differences and determine the minimum value as the target difference;

[0104] Step S490. Determine the corresponding initial formula based on the target difference, which serves as the target allometric growth formula.

[0105] Specifically, the difference between the calculated crown center height and the crown center height obtained from the oblique image is statistically analyzed, and the median value is calculated. The formula for calculating the median value is as follows:

[0106]

[0107] in, The median of the height difference calculated for the m-th group of parameters; The height difference of the nth verification tree calculated for the mth set of parameters, where m = 1, 2, ..., 21; n = 1, 2, ..., Y;

[0108] Pick The second growth parameter and the second correction coefficient corresponding to the minimum value are used as the optimal parameters of the allometric growth formula, thereby establishing the target allometric growth equation.

[0109] Step S500. Determine the target allometric growth formula based on the target group parameters, and calculate the crown center height of all trees in the orthophoto based on the target allometric growth formula;

[0110] Specifically, the height of the crown center corresponding to all determined crowns is calculated using the established target allometric growth equation.

[0111] Step S600. Based on the elevation values ​​in the digital surface model and the calculated crown center height of all trees, obtain the digital elevation model of the terrain.

[0112] Specifically, step S600 includes:

[0113] Step S610. Assign values ​​to the pixels of the corresponding image of the canopy region based on the calculated canopy center height of all trees to obtain a digital height map;

[0114] Specifically, within the forest area, the corresponding pixels are assigned values ​​based on the determined canopy location and the canopy center height calculated based on the target allometric growth equation. The pixel value is the canopy center height, while pixels in non-forest areas are assigned a value of 0, thus obtaining the corresponding digital height map.

[0115] Step S620. Perform terrain interpolation on the digital height map based on the Kriging interpolation method to obtain the interpolated digital height map. The interpolated digital height map has the same resolution as the digital surface model.

[0116] Step S630. Calculate the difference between the digital surface model and the interpolated digital height map to obtain the terrain digital height map;

[0117] Step S640. Generate a digital elevation model of the terrain based on the corresponding numerical elevation map.

[0118] Specifically, by calculating the difference between the digital surface model and the interpolated digital height map, a true topographic map with tree height removed can be obtained, thus generating a true digital elevation model of the terrain.

[0119] Example 2:

[0120] like Figure 6 As shown, this embodiment provides a terrain extraction device based on oblique photography, the device comprising:

[0121] Acquisition unit 100 is used to acquire oblique photogrammetry data of the forest and generate orthophotos and digital surface models based on the oblique photogrammetry data;

[0122] The first determining unit 200 is used to determine all representative trees based on orthophotos, and to determine the canopy area and canopy center height of each representative tree. The representative tree is the tree located at the forest boundary and whose canopy center is closest to the boundary.

[0123] The first division unit 300 is used to divide all representative trees into multiple experimental trees and multiple validation trees. Based on the canopy area and canopy center height of the multiple experimental trees, an allometric growth formula is established, and multiple sets of parameters are calculated accordingly. Each set of parameters includes growth parameters and corresponding correction coefficients.

[0124] The verification unit 400 is used to sequentially verify multiple group parameters based on the canopy area and canopy height of multiple verification trees, and determine the target group parameters from them;

[0125] The second determining unit 500 is used to determine the target allometric growth formula based on the target group parameters, and to calculate the crown center height of all trees in the orthophoto based on the target allometric growth formula.

[0126] The first calculation unit 600 is used to obtain a digital elevation model of the terrain based on the elevation values ​​in the digital surface model and the calculated crown center height of all trees.

[0127] In one specific embodiment disclosed in this application, the first determining unit 200 includes:

[0128] The first processing unit 210 is used to process the orthophoto based on the edge detector to determine the forest area and forest boundary in the orthophoto.

[0129] The second partitioning unit 220 is used to partition the orthophoto corresponding to the forest area based on the maximum inter-class variance method to obtain the canopy area and the non-canopy area.

[0130] The first assignment unit 230 is used to assign the pixels of the image corresponding to the canopy region to a first set threshold, and assign the pixels of the image corresponding to the non-canopy region to a second set threshold, so as to obtain a pixel image.

[0131] The conversion unit 240 is used to convert the pixel image into a vector surface to obtain multiple independent vector surfaces;

[0132] The third determining unit 250 is used to determine multiple tree canopies based on multiple independent vector planes and obtain the canopy area of ​​each tree canopy;

[0133] The fourth determining unit 260 is used to determine the point with the largest elevation from multiple independent vector surfaces based on the digital surface model, and use it as the center of the tree canopy;

[0134] The fifth determining unit 270 is used to determine all representative trees based on the forest boundary and the center of the canopy.

[0135] In one specific embodiment disclosed in this application, the first dividing unit 300 includes:

[0136] The first input unit 310 is used to sequentially input the canopy area and canopy center height of each experimental tree into the allometric growth formula to calculate multiple sets of parameters. Each set of parameters includes a first growth parameter and a corresponding first correction coefficient.

[0137] The first arrangement unit 320 is used to arrange the first growth parameters in multiple sets of parameters in ascending order to obtain a first sequence;

[0138] The third division unit 330 is used to uniformly divide the first sequence and use the multiple boundary values ​​of the division as multiple second growth parameters.

[0139] The first unit 340 is used to take the correction coefficients corresponding to the multiple second growth parameters as multiple second correction coefficients.

[0140] In one specific embodiment disclosed in this application, the verification unit 400 includes:

[0141] The fourth division unit 410 is used to divide multiple second growth parameters and multiple second correction coefficients into multiple groups of parameters based on their correspondence.

[0142] The second substitution unit 420 is used to substitute multiple sets of parameters into the allometric growth formula in sequence to obtain multiple initial formulas;

[0143] The second calculation unit 430 is used to calculate the calculated crown center height of multiple verification trees based on the initial formula and the crown area of ​​multiple verification trees;

[0144] The third calculation unit 440 is used to calculate the difference between the crown center height of the calculation tree corresponding to multiple verification trees and the crown center height of the corresponding position in the digital surface model to obtain multiple first differences;

[0145] The second permutation unit 450 is used to arrange multiple first differences from smallest to largest to obtain a second sequence;

[0146] The second unit 460 is used for the first difference corresponding to the middle position of the second sequence, as the second difference;

[0147] The fourth calculation unit 470 is used to perform calculations based on multiple initial formulas to obtain multiple second differences;

[0148] Comparison unit 480 is used to compare multiple second differences and determine the minimum value as the target difference;

[0149] The sixth determining unit 490 is used to determine the corresponding initial formula based on the target difference, which serves as the target allometric growth formula.

[0150] In one specific embodiment disclosed in this application, the first computing unit 600 includes:

[0151] The second assignment unit 610 is used to assign values ​​to the pixels of the image corresponding to the canopy region based on the calculated canopy center height of all trees, so as to obtain a digital height map.

[0152] Interpolation unit 620 is used to perform terrain interpolation on digital height maps based on the Kriging interpolation method to obtain interpolated digital height maps with the same resolution as the digital surface model.

[0153] The fifth calculation sheet, 630, is used to calculate the difference between the digital surface model and the interpolated digital height map to obtain the terrain digital height map.

[0154] The generation unit 640 is used to generate a digital elevation model of the terrain based on the corresponding terrain numerical height map.

[0155] In one specific embodiment disclosed in this application, the first processing unit 210 includes:

[0156] The second processing unit 211 is used to perform Gaussian filtering on the orthophoto to obtain the first processed image;

[0157] The sixth calculation unit 212 is used to calculate the gradient of the first processed image and obtain gradient values ​​in different directions;

[0158] The suppression processing unit 213 is used to perform non-maximum suppression processing based on gradient values ​​in different directions to obtain a second processed image;

[0159] The third assignment unit 214 is used to reassign values ​​to the second processed image based on the preset maximum and minimum thresholds to obtain the third processed image;

[0160] The tracking unit 215 is used to track the boundaries in the third-processed image based on hysteresis techniques to obtain continuous boundary curves;

[0161] The seventh determining unit 216 is used to determine the forest area and forest boundary based on the boundary curve.

[0162] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0163] Example 3:

[0164] Corresponding to the above method embodiments, this embodiment also provides a terrain extraction device based on oblique photography. The terrain extraction device based on oblique photography described below and the terrain extraction method based on oblique photography described above can be referred to in correspondence.

[0165] Figure 7 This is a block diagram illustrating a terrain extraction device 800 based on oblique photography, according to an exemplary embodiment. Figure 7 As shown, the terrain extraction device 800 based on oblique photogrammetry may include a processor 801 and a memory 802. The terrain extraction device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0166] The processor 801 controls the overall operation of the oblique photogrammetry-based terrain extraction device 800 to complete all or part of the steps in the oblique photogrammetry-based terrain extraction method described above. The memory 802 stores various types of data to support the operation of the oblique photogrammetry-based terrain extraction device 800. This data may include, for example, instructions for any application or method operating on the oblique photogrammetry-based terrain extraction device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the oblique photogrammetry-based terrain extraction device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0167] In an exemplary embodiment, the terrain extraction device 800 based on oblique photogrammetry may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described terrain extraction method based on oblique photogrammetry.

[0168] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described oblique photogrammetry-based terrain extraction method. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the oblique photogrammetry-based terrain extraction device 800 to complete the above-described oblique photogrammetry-based terrain extraction method.

[0169] Example 4:

[0170] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the terrain extraction method based on oblique photography described above.

[0171] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the terrain extraction method based on oblique photogrammetry described in the above method embodiments.

[0172] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0173] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0174] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A terrain extraction method based on oblique photogrammetry, characterized in that, include: Oblique photographic data of the forest is acquired, and orthophotos and digital surface models are generated based on the oblique photographic data; Based on the orthophoto, all representative trees are identified, and the canopy area and canopy center height of each representative tree are determined. The representative tree is the tree located at the forest boundary and whose canopy center is closest to the boundary. All representative trees are divided into multiple experimental trees and multiple validation trees. Based on the canopy area and canopy center height of the multiple experimental trees, an allometric growth formula is established, and multiple sets of parameters are calculated accordingly. Each set of parameters includes growth parameters and corresponding correction coefficients. Based on the canopy area and canopy center height corresponding to multiple verification trees, multiple sets of parameters are verified sequentially, and the target set of parameters is determined from them; The target allometric growth formula is determined based on the target group parameters, and the crown center height of all trees in the orthophoto is calculated based on the target allometric growth formula. A digital elevation model of the terrain is obtained based on the elevation values ​​in the digital surface model and the calculated crown center height of all trees.

2. The terrain extraction method based on oblique photogrammetry according to claim 1, characterized in that... Based on the orthophoto, all representative trees were determined, including: The orthophoto is processed using an edge detector to determine the forest area and forest boundary in the orthophoto; The orthophotos corresponding to the forest area are divided based on the Otsu's method to obtain the canopy area and the non-canopy area; The pixels of the image corresponding to the canopy region are assigned a first preset threshold, and the pixels of the image corresponding to the non-canopy region are assigned a second preset threshold to obtain a pixel image. The pixel image is converted into a vector surface to obtain multiple independent vector surfaces; Multiple tree canopies are determined based on multiple independent vector planes, and the canopy area of ​​each tree canopy is obtained; Based on the digital surface model, the point with the largest elevation is determined from multiple independent vector surfaces and used as the center of the tree canopy; Based on the forest boundary and the center of the tree canopy, all representative trees are identified.

3. The terrain extraction method based on oblique photography according to claim 1, characterized in that... Based on the canopy area and canopy center height of multiple experimental trees, an allometric growth formula was established, and multiple sets of parameters were calculated accordingly, including: The canopy area and canopy center height of each experimental tree are successively substituted into the allometric growth formula to calculate multiple sets of parameters. Each set of parameters includes a first growth parameter and a corresponding first correction coefficient. The first growth parameter in the multiple sets of parameters is arranged in ascending order to obtain the first sequence; The first sequence is divided evenly, and the multiple boundary values ​​of the division are used as multiple second growth parameters; The correction coefficients corresponding to multiple second growth parameters are used as multiple second correction coefficients.

4. The terrain extraction method based on oblique photography according to claim 3, characterized in that... Based on the canopy area and canopy center height corresponding to multiple verification trees, multiple sets of parameters are verified sequentially, and the target set of parameters is determined from them, including: Based on the correspondence, multiple second growth parameters and multiple second correction coefficients are divided into multiple groups of parameters; By substituting multiple sets of parameters into the allometric growth formula in sequence, multiple initial formulas are obtained; Based on the initial formula and the canopy area of ​​multiple verification trees, the calculated canopy center height of multiple verification trees is obtained; The difference between the crown center height of the computed tree corresponding to multiple verification trees and the crown center height of the corresponding position in the digital surface model is calculated to obtain multiple first differences; Arrange the multiple first differences in ascending order to obtain the second sequence; Obtain the first difference value corresponding to the middle position of the second sequence, and use it as the second difference value; Multiple second differences are obtained by calculating based on multiple initial formulas; Compare multiple second differences and determine the minimum value as the target difference; The initial formula corresponding to the target difference is determined and used as the target allometric growth formula.

5. A terrain extraction device based on oblique photography, characterized in that, include: The acquisition unit is used to acquire oblique photographic data of the forest and generate orthophotos and digital surface models based on the oblique photographic data. The first determining unit is used to determine all representative trees based on the orthophoto, and to determine the canopy area and canopy center height of each representative tree, wherein the representative tree is the tree located at the forest boundary and whose canopy center is closest to the boundary; The first partitioning unit is used to divide all representative trees into multiple experimental trees and multiple verification trees. Based on the canopy area and canopy center height of the multiple experimental trees, an allometric growth formula is established, and multiple sets of parameters are calculated accordingly. Each set of parameters includes growth parameters and corresponding correction coefficients. The verification unit is used to sequentially verify multiple sets of parameters based on the canopy area and the canopy center height corresponding to multiple verification trees, and determine the target set of parameters from them; The second determining unit is used to determine the target allometric growth formula based on the target group parameters, and to calculate the crown center height of all trees in the orthophoto based on the target allometric growth formula. The first calculation unit is used to obtain a digital elevation model of the terrain based on the elevation values ​​in the digital surface model and the calculated crown center height of all trees.

6. The terrain extraction device based on oblique photography according to claim 5, characterized in that, The first determining unit includes: The first processing unit is used to process the orthophoto based on the edge detector to determine the forest area and forest boundary in the orthophoto; The second partitioning unit is used to partition the orthophoto corresponding to the forest area based on the maximum inter-class variance method to obtain the canopy area and the non-canopy area; The first assignment unit is used to assign the pixels of the image corresponding to the canopy region to a first set threshold, and assign the pixels of the image corresponding to the non-canopy region to a second set threshold, so as to obtain a pixel image. A conversion unit is used to convert the pixel image into a vector surface to obtain multiple independent vector surfaces; The third determining unit is used to determine multiple tree canopies based on multiple independent vector planes and obtain the canopy area of ​​each tree canopy; The fourth determining unit is used to determine the point with the largest elevation from multiple independent vector surfaces based on the digital surface model, and use it as the center of the tree canopy; The fifth determining unit is used to determine all representative trees based on the forest boundary and the center of the tree canopy.

7. The terrain extraction device based on oblique photography according to claim 5, characterized in that, The first partitioning unit includes: The first input unit is used to sequentially input the canopy area and the canopy center height of each experimental tree into the allometric growth formula to calculate multiple sets of parameters. Each set of parameters includes a first growth parameter and a corresponding first correction coefficient. The first arrangement unit is used to arrange the first growth parameter in multiple sets of parameters in ascending order to obtain the first sequence; The third partitioning unit is used to uniformly partition the first sequence and use the multiple boundary values ​​of the partition as multiple second growth parameters. The first is used as a unit to take the correction coefficients corresponding to multiple second growth parameters as multiple second correction coefficients.

8. The terrain extraction device based on oblique photography according to claim 7, characterized in that, The verification unit includes: The fourth partitioning unit is used to divide multiple second growth parameters and multiple second correction coefficients into multiple groups of parameters based on their corresponding relationships; The second substitution unit is used to sequentially substitute multiple sets of parameters into the allometric growth formula to obtain multiple initial formulas. The second calculation unit is used to calculate the calculated crown center height of multiple verification trees based on the initial formula and the crown area of ​​multiple verification trees; The third calculation unit is used to calculate the difference between the crown center height of the calculation tree corresponding to multiple verification trees and the crown center height of the corresponding position in the digital surface model to obtain multiple first differences; The second permutation unit is used to arrange multiple first differences from smallest to largest to obtain a second sequence; The second unit is used as the first difference corresponding to the middle position of the second sequence, and serves as the second difference. The fourth calculation unit is used to perform calculations based on multiple initial formulas to obtain multiple second differences; The comparison unit is used to compare multiple second differences and determine the minimum value as the target difference. The sixth determining unit is used to determine the corresponding calculated initial formula based on the target difference, as the target allometric growth formula.

9. A terrain extraction device based on oblique photography, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the terrain extraction method based on oblique photogrammetry as described in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the terrain extraction method based on oblique photography as described in any one of claims 1 to 4.

Images