Image processing and measuring method and system based on unmanned aerial vehicle radar and oblique photography

Abstract

The invention discloses an image processing and measuring method and system based on unmanned aerial vehicle radar and oblique photography, relates to the technical field of oblique photography image measurement, and is used for solving the problem that the accuracy of region division and anomaly detection results is reduced. The method comprises the following steps: calculating a vegetation change gradient in combination with a radar echo amplitude to generate vegetation distribution characteristics; extracting an under-forest ground and generating uniform extrapolation points; arranging a clinometer to obtain an elevation and a coordinate; calculating a local slope and a crown fluctuation amplitude; according to the method, the area vegetation coverage state is evaluated, abnormal areas are screened, extrapolation ground points of the abnormal areas are marked and output, high-precision exploration of the forest area is achieved, terrain complexity and vegetation distribution characteristics are accurately reflected, reliable data support is provided for forest management and terrain analysis, and exploration efficiency and precision are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oblique photography image measurement, and more particularly, to an image processing measurement method and system based on unmanned aerial vehicle radar and oblique photography. BACKGROUND

[0002] The terrain of forest areas is complex, and the vegetation is densely distributed. Traditional surveying and mapping methods rely on manual reconnaissance or single remote sensing images for data collection. However, manual reconnaissance is not only time-consuming and labor-intensive but also limited by terrain, making it difficult to obtain accurate data on the ground under the forest. Single optical remote sensing images are severely obstructed by tree canopies, and cannot directly reflect the elevation information and spatial distribution of the ground under the forest, which can easily lead to a decrease in surveying and mapping accuracy.

[0003] The prior art has the following disadvantages:

[0004] Currently, although the existing measurement methods based on unmanned aerial vehicle aerial photography or laser radar can improve data collection efficiency, oblique photography images only provide surface image gray scale information, cannot effectively distinguish between tree canopies and ground under the forest, lack image gray scale feature cooperative processing, leading to inaccurate ground point extraction, insufficient expression of vegetation distribution characteristics, decreased accuracy of region division and anomaly detection results, and affecting subsequent terrain correction and key surveying and mapping point placement work. Therefore, an image processing measurement method and system based on unmanned aerial vehicle radar and oblique photography are proposed.

[0005] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an image processing measurement method and system based on unmanned aerial vehicle radar and oblique photography, which uses image gray scale value and radar echo amplitude fusion to construct vegetation distribution characteristics and ground under the forest extrapolation point generation technology to solve the problems raised in the above background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme, an image processing measurement method based on unmanned aerial vehicle radar and oblique photography, comprising the following steps:

[0008] Step S1: When surveying a forest area, image gray scale values of each pixel point of the forest area are collected by oblique photography, radar echo amplitude values of the pixel points are obtained by radar detection, and vegetation change gradient is calculated according to the image gray scale values;

[0009] Step S2: generating a vegetation distribution feature of each pixel point by integrating the vegetation change gradient and the radar echo amplitude value, identifying and marking each pixel point according to the vegetation distribution feature to obtain the understory ground of the forest area, and generating a uniform extrapolated ground point based on the understory ground;

[0010] Step S3: detecting the forest land slope and the tree crown fluctuation amplitude of the forest area, calculating the steep slope proportion of the forest area according to the forest land slope, evaluating the topographic survey feature of the forest area in combination with the tree crown fluctuation amplitude, and regionally dividing the forest area according to the topographic survey feature;

[0011] Step S4: integrating the understory ground proportion in the divided region, evaluating the vegetation coverage state of the divided region based on the understory ground proportion, screening the divided region according to the vegetation coverage state, and marking to obtain an abnormal region and outputting the extrapolated ground point of the abnormal region.

[0012] In a preferred embodiment, in step S1, when the forest area is surveyed, the unmanned aerial vehicle carries a tilt camera to image different angles of the forest area to obtain forest images;

[0013] The forest images are processed by gray scale to obtain gray scale images, and each pixel point in the gray scale image corresponds to a gray scale value;

[0014] The pixel points of the forest images at different angles are one-to-one corresponding, and the image gray scale values of the pixel points at the same position are combined into a pixel gray scale set;

[0015] The median of the pixel gray scale set is taken as the gray scale median value, and the mean of the pixel gray scale value set is calculated as the gray scale mean value;

[0016] The average of the gray scale median value and the gray scale mean value is calculated as the image gray scale value of the pixel point.

[0017] In a preferred embodiment, in step S1, the forest area is scanned by a radar detection device carried by the unmanned aerial vehicle, electromagnetic waves are emitted to the forest area, and echo signals are received;

[0018] According to the echo signal, the signal strength value of each pixel point is extracted as the radar echo amplitude value;

[0019] Taking each pixel point as a center pixel point, the adjacent pixel points of the center pixel point are combined into a neighborhood set, and the image gray scale values of the center pixel point and the pixel points in the neighborhood set are subtracted to obtain each neighborhood gray scale difference value;

[0020] The standard deviation of each neighborhood gray scale difference value is calculated as the vegetation change gradient.

[0021] In a preferred embodiment, in step S2, the vegetation change gradient and the radar echo amplitude of the pixel point are respectively normalized and the product result is used as the vegetation distribution feature of the pixel point;

[0022] Combine the vegetation distribution features of all pixels in the forest image into a vegetation feature set;

[0023] The median of the vegetation feature set is used as the median of the distribution feature. The median of the distribution feature is subtracted from each vegetation distribution feature to obtain the absolute value to obtain each absolute deviation. The median of each absolute deviation is multiplied by the preset adjustment coefficient to obtain the distribution feature amplitude.

[0024] In a preferred embodiment, in step S2, the distribution feature median is subtracted from the distribution feature amplitude to obtain the distribution feature threshold;

[0025] If the vegetation distribution feature of a pixel point is less than the distribution feature threshold, the pixel point is marked as a ground pixel under the forest; otherwise, the pixel point is not marked;

[0026] Four ground pixels under the forest that form a regular rectangular structure in space are selected as corner points, and the ground grid units under the forest are constructed based on the corner points.

[0027] The bilinear interpolation method is used to fit the pixel points of the forest ground grid unit to generate uniformly extrapolated ground points.

[0028] In a preferred embodiment, in step S3, a ground inclinometer is arranged at the center of a grid area formed by a predetermined grid spacing as a measuring point, and the elevation and horizontal coordinates of each measuring point are collected and the local slope value is calculated;

[0029] When the local slope value is greater than or equal to the preset slope threshold, the grid area where the measuring point is located is defined as a steep slope area;

[0030] Otherwise, it is not defined;

[0031] The ratio of the number of measuring points in the grid area defined as the steep slope area to the total number of measuring points is taken as the steep slope ratio.

[0032] In a preferred embodiment, in step S3, the instantaneous displacement of the tree crown is monitored by deploying a three-dimensional displacement sensor at the tree crown position of the measuring point;

[0033] The difference between the maximum and minimum values ​​of the instantaneous displacement of the crown within the preset observation period is calculated as the crown fluctuation amplitude;

[0034] The steep slope ratio and crown fluctuation amplitude were standardized to construct a forest terrain feature vector, and the topographic survey characteristics of each measuring point were output through the logistic regression algorithm.

[0035] In a preferred embodiment, in step S3, if the topographic survey feature is greater than or equal to a preset survey feature threshold, the measurement point is defined as a target area measurement point;

[0036] Otherwise, the measuring point is not defined;

[0037] Based on connectivity analysis, the target area measurement points with intervals less than the preset distance are connected to form continuous areas as the division areas of the forest area.

[0038] In a preferred embodiment, in step S4, the number of pixels of the ground under the forest and the divided areas is obtained by oblique photography using a drone, and the number of pixels is converted into spatial area to obtain the ground area under the forest of each divided area and the total area of ​​each divided area;

[0039] The ratio of the understory area of ​​each divided area to the area of ​​each divided area is taken as the understory area ratio of each divided area;

[0040] When the proportion of the ground under the forest is lower than the preset proportion threshold, the vegetation coverage state of the divided area is determined to be abnormal coverage;

[0041] On the contrary, the vegetation cover status of the divided area is normal coverage;

[0042] The divided areas with abnormal vegetation coverage are screened and marked as abnormal areas, and the extrapolated ground points of the abnormal areas are output for subsequent survey and correction.

[0043] The image processing measurement system based on UAV radar and oblique photography includes a data acquisition module, a vegetation recognition module, a terrain analysis module, and a regional assessment module. The functions of each module are as follows:

[0044] The data acquisition module is used to survey forest areas. It collects the grayscale value of each pixel in the forest area through oblique photography, obtains the radar echo amplitude of the pixel through radar detection, and calculates the vegetation change gradient based on the image grayscale value.

[0045] The vegetation recognition module is used to generate vegetation distribution characteristics of each pixel point by integrating vegetation change gradient and radar echo amplitude. Based on the vegetation distribution characteristics, each pixel point is identified and marked to obtain the understory ground in the forest area, and uniformly extrapolated ground points are generated based on the understory ground.

[0046] The terrain analysis module is used to detect the slope of forest land and the fluctuation amplitude of tree crown in forest areas, calculate the proportion of steep slopes in forest areas based on the slope of forest land, evaluate the terrain survey characteristics of forest areas based on the fluctuation amplitude of tree crown, and divide forest areas into regions based on the terrain survey characteristics;

[0047] The regional assessment module is used to count the proportion of ground under the forest in the divided area, evaluate the vegetation coverage status of the divided area based on the proportion of ground under the forest, filter the divided area according to the vegetation coverage status, mark the abnormal area and output the extrapolated ground points of the abnormal area.

[0048] Technical effects and advantages of the present invention:

[0049] The present invention obtains high-resolution image data of forest areas through unmanned aerial vehicle (UAV) oblique photography, obtains the echo amplitude of each pixel point through radar detection, calculates vegetation change gradients based on image grayscale values, forms vegetation distribution characteristics, uses vegetation distribution characteristics to identify and mark each pixel point, and extracts the ground beneath the forest. On this basis, uniformly extrapolated ground points are generated. The elevation and horizontal coordinates of the forest floor are obtained by deploying ground inclinometers, and local slope values ​​are calculated. The instantaneous displacement of tree crowns within a preset observation period is recorded, and then the crown fluctuation amplitude is calculated. The steep slope ratio and the crown fluctuation amplitude are integrated to construct terrain survey characteristics, and the forest area is divided into regions to form continuous and quantifiable divided regions. The proportion of the ground beneath the forest in each divided region is calculated using UAV images, the regional vegetation cover status is evaluated, and abnormal areas are screened. The extrapolated ground points in the abnormal areas are marked and output, providing key references for subsequent survey and correction, thereby achieving high-precision survey of forest areas, accurately reflecting terrain complexity and vegetation distribution characteristics, providing reliable data support for forest management and terrain analysis, and improving survey efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the image processing measurement method based on drone radar and oblique photography of the present invention.

[0051] Figure 2 This is a module schematic diagram of the image processing and measurement system based on UAV radar and oblique photography of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] The present invention obtains high-resolution image data of forest areas through unmanned aerial vehicle (UAV) oblique photography, obtains the echo amplitude of each pixel point through radar detection, calculates the vegetation change gradient based on the image grayscale value, forms vegetation distribution characteristics, uses the vegetation distribution characteristics to identify and mark each pixel point and extracts the ground under the forest, and generates uniformly extrapolated ground points on this basis, obtains the forest elevation and horizontal coordinates by deploying ground inclinometers, calculates the local slope value, records the instantaneous displacement of the tree crown within a preset observation period, and then calculates the crown fluctuation amplitude, comprehensively constructs the terrain survey characteristics by combining the steep slope ratio and the crown fluctuation amplitude, and divides the forest area into regions to form continuous and quantifiable divided areas. The ratio of the ground under the forest in each divided area is counted through UAV images, the regional vegetation coverage status is evaluated and abnormal areas are screened, and the extrapolated ground points in the abnormal areas are marked and output, providing key references for subsequent surveys and corrections, thereby achieving high-precision surveys of forest areas.

[0054] Example 1

[0055] See also Figure 1 ,The image processing measurement method based on UAV radar and oblique photography includes the following steps:

[0056] Step S1: When surveying a forest area, the grayscale value of each pixel in the forest area is collected through oblique photography, the radar echo amplitude of the pixel is obtained through radar detection, and the vegetation change gradient is calculated based on the image grayscale value;

[0057] Step S2: The vegetation distribution characteristics of each pixel are generated by integrating the vegetation change gradient and the radar echo amplitude. Each pixel is identified and marked according to the vegetation distribution characteristics to obtain the understory ground in the forest area, and uniformly extrapolated ground points are generated based on the understory ground.

[0058] Step S3: detecting the forestland slope and tree crown fluctuation amplitude in the forest area, calculating the steep slope ratio in the forest area based on the forestland slope, evaluating the topographic survey characteristics of the forest area in combination with the tree crown fluctuation amplitude, and dividing the forest area into regions based on the topographic survey characteristics;

[0059] Step S4: Count the proportion of ground under the forest in the divided area, evaluate the vegetation coverage status of the divided area based on the proportion of ground under the forest, filter the divided area according to the vegetation coverage status, mark the abnormal area and output the extrapolated ground points of the abnormal area.

[0060] The specific implementation is as follows:

[0061] In step S1, when surveying a forest area, a drone equipped with an oblique camera images the forest area from different angles to obtain a forest image.

[0062] The forest image is grayscaled to obtain a grayscale image, in which each pixel corresponds to a grayscale value;

[0063] For forest images at different angles, the pixels are mapped one by one, and the image grayscale values ​​of pixels at the same position are combined into a pixel grayscale set. The median of the pixel grayscale set is used as the grayscale median value, and the mean of the pixel grayscale value set is calculated as the grayscale mean.

[0064] It needs to be explained that an oblique photography camera refers to a photographic imaging device that is arranged at an angle relative to the vertical direction, including multiple lenses arranged at different angles, such as an orthophoto lens and oblique lenses in the front, back, left and right directions, which are used to obtain forest images at different angles in forest areas; grayscale processing refers to the image processing operation of converting the input color image into a single-channel grayscale image.

[0065] The average value of the grayscale median and grayscale mean is calculated as the image grayscale value of the pixel point. The image grayscale value reflects the optical reflection intensity at the corresponding pixel point.

[0066] The radar detection device carried by the drone is used to perform radar scanning of the forest area and emit electromagnetic waves into the forest area. The receiving module of the radar detection device receives the echo signal and performs frequency conversion processing to obtain an intermediate frequency signal. The high-frequency echo signal is input into the analog-to-digital conversion module to convert it into a digital signal. The signal processing module processes the digital signal and extracts the signal strength value of the pixel point as the radar echo amplitude. The radar echo amplitude refers to the intensity of the reflected or scattered electromagnetic wave signal.

[0067] It should be explained that a radar detection device is a device that uses the principle of electromagnetic wave propagation and reflection to perceive and measure the surface structure or object distribution of the area to be measured, and includes a transmitting module, a receiving module, a frequency conversion module, an analog-to-digital conversion module, and a signal processing module. In this embodiment, the position and attitude information of the radar detection device is obtained through the positioning unit of the drone, and the position and attitude information are combined according to the round-trip propagation time of the electromagnetic wave. The precise reflection position coordinates of the echo signal in the geographic space are calculated through the radar geometric positioning model, and the reflection position coordinates of the echo signal in the geographic space are obtained and matched with the pixel points of the forest image.

[0068] Each pixel is taken as the center pixel, and the adjacent pixels of the center pixel are combined into a neighborhood set. The grayscale value of the center pixel is subtracted from the image grayscale value of the pixels in the neighborhood set to obtain the grayscale difference of each neighborhood.

[0069] The standard deviation of the grayscale difference of each neighborhood is taken as the vegetation change gradient.

[0070] The vegetation change gradient reflects the vegetation change at the pixel point. By analyzing the difference in image grayscale values ​​between the central pixel point and adjacent pixels, the change amplitude of the vegetation coverage status at the pixel point is characterized.

[0071] In step S2, the vegetation change gradient and radar echo amplitude of the pixel point are respectively normalized and the product result is used as the vegetation distribution feature of the pixel point;

[0072] When the vegetation change gradient is small, it means that the vegetation coverage of the pixel point is relatively uniform, the more continuous the distribution of the ground under the forest is, and the smaller the vegetation distribution characteristics are; when the radar echo amplitude is small, it means that the electromagnetic wave return energy at the ground under the forest is weak, the ground under the forest is more blocked by vegetation cover, and the smaller the vegetation distribution characteristics are.

[0073] The vegetation distribution features of all pixels in the forest image are combined into a vegetation feature set. The median of the vegetation feature set is used as the median of the distribution feature. The median of the distribution feature is subtracted from each vegetation distribution feature to obtain the absolute value of each absolute deviation. The median of each absolute deviation is multiplied by the preset adjustment coefficient to obtain the distribution feature amplitude.

[0074] The distribution feature threshold is obtained by subtracting the median value of the distribution feature from the distribution feature amplitude.

[0075] It should be explained that the preset adjustment coefficient is used to adjust the sensitivity of the distribution feature amplitude, and the value range is determined by professionals based on the scene complexity of the forest area and the accuracy requirements of the recognition task. For example, multiple forest image samples are selected, and the ground area under the forest is marked. The median and distribution feature amplitude of the distribution feature are calculated, and the different preset adjustment coefficient values ​​are tuned by the least squares method. The set of pixel points identified as the ground under the forest within the corresponding threshold interval is counted, and the overlap of the pixel point sets between the marked results is compared. The preset adjustment coefficient with the largest overlap is selected as the determined value; the standardization processing method includes but is not limited to the standard linear transformation based on interval scaling, the Z-Score standardization method based on statistics, or the normalization method based on the nonlinear mapping function. The application method of the standardization processing will not be elaborated here.

[0076] If the vegetation distribution feature of a pixel point is less than the distribution feature threshold, the pixel point is marked as a ground pixel under the forest; otherwise, the pixel point is not marked.

[0077] Four ground pixels forming a regular rectangular structure in space are selected as corner points. Ground grid units under the forest are constructed based on the corner points. The bilinear interpolation method is used to fit the pixels of the ground grid units under the forest to generate uniformly extrapolated ground points.

[0078] Extrapolated ground points refer to supplementary points generated within the region through bilinear interpolation method based on the pixel points of the ground under the forest. They are used to fill the blank areas between the original measurement points, thereby forming evenly distributed ground data under the forest.

[0079] It should be explained that the bilinear interpolation method is an interpolation algorithm used to estimate the value of unknown points in a two-dimensional grid.

[0080] In step S3, the slope of the forest land in the forest area is detected by deploying ground inclinometers. The ground inclinometers are deployed in the forest area according to a predetermined grid spacing. That is, the ground inclinometers are deployed with the center of the grid area formed by the predetermined grid spacing as the measuring point. Each measuring point is firmly connected to the ground through a fixed bracket. The elevation and horizontal coordinates are collected at each measuring point, and the local slope value is calculated based on the elevation and horizontal coordinates of the measuring point. The specific calculation formula is as follows:

[0081] ;

[0082] in, is the local slope value, is the elevation of the measuring point, is the horizontal coordinate of the measuring point, is the horizontal spacing of the measuring points on the horizontal x-axis, is the horizontal spacing of the measuring points on the horizontal y-axis, is the inverse tangent function, which is used to convert the slope ratio into an angle value.

[0083] It should be noted that a ground inclinometer is a measuring device that can collect ground elevation and corresponding horizontal coordinates at a set measuring point. It obtains the spatial position parameters of ground points through laser ranging and provides input data for the calculation of local slope values. The predetermined grid spacing refers to the spatial layout interval of measuring points divided according to a regular grid in the forest area to ensure the uniform distribution of measuring points in space. Its value is set according to the area of ​​the forest area and the number of target measuring points. For example, when the area of ​​the forest area is , when the number of target measuring points is 100, the predetermined grid spacing is 100m.

[0084] Compare the local slope values ​​of all measured points with the preset slope threshold:

[0085] When the local slope value is greater than or equal to the preset slope threshold, the grid area where the measuring point is located is defined as a steep slope area;

[0086] Otherwise, it is not defined.

[0087] The preset slope threshold is used to determine whether the grid area where the measuring point is located is a steep slope. Its value is set according to the slope classification standard in topographic research. For example, the steep slope standard in the geographic information slope classification standard is greater than 25°.

[0088] Calculate the proportion of steep slopes in forested areas based on the above definition:

[0089] ;

[0090] in, is the steep slope ratio, For measurement points where the grid area is defined as a steep slope area, is the total number of measurement points, is the local slope value, is the preset slope threshold.

[0091] At the same time, the crown fluctuation amplitude is monitored by deploying a three-dimensional displacement sensor at the crown position of the measuring point. The three-dimensional displacement sensor is fixed to the crown branch through a flexible bracket. The instantaneous displacement of the crown in the horizontal and vertical directions is recorded over time at a fixed sampling frequency within the preset observation period. The difference between the maximum and minimum values ​​of the instantaneous displacement of the crown within the preset observation period is calculated as the crown fluctuation amplitude.

[0092] It should be noted that a three-dimensional displacement sensor refers to a measuring device that can measure the displacement of a target in three orthogonal directions. It is fixed to the branches of a tree canopy through a flexible bracket to ensure that it can move synchronously with the branches under the action of wind or natural disturbances; the preset observation period refers to the length of continuous monitoring time set to capture the dynamic fluctuation characteristics of the tree canopy, which is determined based on the wind speed and the crown vibration period in the forest area. For example, for forest areas with a wind speed of 3 to 5 m / s, the crown vibration period is 5 to 10 seconds, and the preset observation period can be set to 5 minutes; the fixed sampling frequency refers to the time interval for recording displacement data by the three-dimensional displacement sensor within the preset observation period. The setting method satisfies the Nyquist sampling theorem, that is, the sampling frequency is at least twice the crown vibration frequency to avoid signal aliasing. For example, if the crown vibration period is 5 seconds, that is, the crown vibration frequency is 0.2 Hz, then the sampling frequency can be set to 1 Hz or 2 Hz to ensure that the crown displacement change curve can be accurately captured.

[0093] The steep slope ratio and crown fluctuation amplitude are normalized to construct a forest terrain feature vector. The regional evaluation is performed using a logistic regression algorithm to output the terrain survey characteristics of each measuring point:

[0094] ;

[0095] in, For topographic survey features, is a natural constant, is the steep slope ratio, is the crown fluctuation amplitude, is the intercept term, and is the logistic regression coefficient.

[0096] It should be noted that the logistic regression algorithm is a statistical modeling method for binary or multi-classification problems. It predicts the probability that a measurement point belongs to a specific category by modeling the relationship between the input feature vector and the category label. The intercept term and the logistic regression coefficient are obtained through training, that is, the parameters are optimized using the gradient descent method based on historical survey data to minimize the error between the predicted probability and the true category. The algorithm training process is not described in detail here.

[0097] Compare the topographic survey feature with a preset survey feature threshold. If the topographic survey feature is greater than or equal to the preset survey feature threshold, define the measuring point as a target area measuring point; otherwise, do not define it.

[0098] Based on the connectivity analysis of the neighboring relationship, the adjacent target area measurement points are connected to form a continuous area as the division area of ​​the forest area;

[0099] In the connectivity analysis, the four-neighborhood judgment method is adopted, and the measurement points with intervals less than the preset distance are regarded as part of the divided area to ensure the integrity and spatial continuity of the divided area;

[0100] It should be noted that the preset survey feature threshold is determined based on historical survey data, forest terrain characteristics, and task requirements. By analyzing the distribution patterns of steep slope proportions, tree crown fluctuation amplitude, and other relevant terrain indicators based on statistical data from previously surveyed areas, the critical value range for distinguishing target areas from non-target areas is determined. The preset distance is a judgment parameter used to determine whether measurement points are in a neighboring relationship in spatial analysis. It is set based on the grid spacing and measurement point density in the forest area. For example, a preset distance of 1 to 2 times the grid spacing is used to ensure that continuous areas can be correctly identified in connectivity analysis. Connectivity analysis refers to a method that performs spatial proximity analysis on a set of measurement points that meet the judgment criteria during region division or outlier area identification. It is used to group mutually adjacent measurement points into continuous areas. The four-neighborhood determination method is a commonly used neighborhood determination method in connectivity analysis. With the measurement point to be analyzed as the center, measurement points in the four adjacent grids (up, down, left, and right) along the two-dimensional grid are selected as neighbor candidates. If a neighbor candidate measurement point meets the preset criteria, it is considered to belong to the same continuous area as the center measurement point.

[0101] Topographic survey characteristics comprehensively reflect the complexity of the local terrain in the forest area and quantify the survey complexity of the divided area, that is, reflect the degree of difficulty faced by the divided area when implementing topographic survey operations; the higher the proportion of steep slopes, the greater the proportion of steep slope terrain in the divided area, and the greater the ground survey characteristics; the greater the crown fluctuation amplitude, the more complex the crown structure, the more significant the crown movement trajectory, the greater the difficulty of ground observation and mobile measurement under the forest, and the greater the ground survey characteristics.

[0102] In step S4, the number of pixels of the ground under the forest and the divided areas is obtained through oblique photography by the drone, and the number of pixels is converted into spatial area to obtain the ground area under the forest of each divided area and the total area of ​​each divided area;

[0103] The ratio of the understory area of ​​each divided area to the area of ​​each divided area is taken as the understory proportion of each divided area.

[0104] The vegetation coverage status of each divided area is evaluated based on the proportion of the ground under the forest. The vegetation coverage status reflects the density of vegetation in the divided area. The vegetation coverage status is divided into normal coverage or abnormal coverage according to the preset proportion threshold:

[0105] When the proportion of the ground under the forest is lower than the preset proportion threshold, the vegetation coverage state of the divided area is determined to be abnormal coverage;

[0106] On the contrary, the vegetation coverage status of the divided area is normal coverage.

[0107] It should be noted that the preset proportion threshold is set based on forest type, historical vegetation cover data and survey mission objectives. The distribution characteristics of the proportion of forest ground in the forest area are counted, the vegetation density corresponding to different forest ground proportions is analyzed, and the critical value is adjusted in combination with the survey objectives. For example, in the evergreen coniferous forest area, the preset proportion threshold is set to 0.3, that is, when the proportion of forest ground in the divided area is less than 30%, the vegetation coverage status of the divided area is judged to be abnormal coverage.

[0108] The areas with abnormal vegetation coverage are screened and marked as abnormal areas, and the extrapolated ground points of the abnormal areas are output for subsequent survey and correction, including high-resolution image review, on-site survey or other auxiliary data verification to ensure that their spatial position and terrain characteristics accurately reflect the ground conditions under the forest.

[0109] Example 2

[0110] like Figure 2 As shown in the figure, the image processing measurement system based on UAV radar and oblique photography is used to implement the image processing measurement method based on UAV radar and oblique photography. It includes a data acquisition module, a vegetation recognition module, a terrain analysis module and a regional assessment module. The functions of each module are as follows:

[0111] The data acquisition module is used to survey forest areas. It collects the grayscale value of each pixel in the forest area through oblique photography, obtains the radar echo amplitude of the pixel through radar detection, and calculates the vegetation change gradient based on the image grayscale value.

[0112] The vegetation recognition module is used to generate vegetation distribution characteristics of each pixel point by integrating vegetation change gradient and radar echo amplitude. Based on the vegetation distribution characteristics, each pixel point is identified and marked to obtain the understory ground in the forest area, and uniformly extrapolated ground points are generated based on the understory ground.

[0113] The terrain analysis module is used to detect the slope of forest land and the fluctuation amplitude of tree crown in forest areas, calculate the proportion of steep slopes in forest areas based on the slope of forest land, evaluate the terrain survey characteristics of forest areas based on the fluctuation amplitude of tree crown, and divide forest areas into regions based on the terrain survey characteristics;

[0114] The regional assessment module is used to count the proportion of ground under the forest in the divided area, evaluate the vegetation coverage status of the divided area based on the proportion of ground under the forest, filter the divided area according to the vegetation coverage status, mark the abnormal area and output the extrapolated ground points of the abnormal area.

[0115] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0116] Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0117] As used herein, the singular forms "a," "an," and "the" may also include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include," "comprising," "having," and the like specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof. Furthermore, the term "and / or" as used in this specification includes any and all combinations of the relevant listed items.

[0118] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0119] The above description of the disclosed embodiments will enable those skilled in the art to implement or use various modifications of these embodiments, and it will be apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Image processing measurement method based on UAV radar and oblique photography, characterized by: The following steps are involved: Step S1: When surveying a forest area, the grayscale value of each pixel in the forest area is collected through oblique photography, the radar echo amplitude of the pixel is obtained through radar detection, and the vegetation change gradient is calculated based on the image grayscale value; Step S2: The vegetation distribution characteristics of each pixel are generated by integrating the vegetation change gradient and the radar echo amplitude. Each pixel is identified and marked according to the vegetation distribution characteristics to obtain the understory ground in the forest area, and uniformly extrapolated ground points are generated based on the understory ground. Step S3: detecting the forestland slope and tree crown fluctuation amplitude in the forest area, calculating the steep slope ratio in the forest area based on the forestland slope, evaluating the topographic survey characteristics of the forest area in combination with the tree crown fluctuation amplitude, and dividing the forest area into regions based on the topographic survey characteristics; Step S4: Count the proportion of ground under the forest in the divided area, evaluate the vegetation coverage status of the divided area based on the proportion of ground under the forest, filter the divided area according to the vegetation coverage status, mark the abnormal area and output the extrapolated ground points of the abnormal area.

2. The image processing measurement method based on drone radar and oblique photography according to claim 1 is characterized in that: In step S1, when surveying a forest area, a drone equipped with an oblique camera images the forest area from different angles to obtain a forest image; The forest image is converted into a grayscale image, where each pixel corresponds to a grayscale value. For forest images at different angles, the pixels are mapped one by one, and the image grayscale values ​​of pixels at the same position are combined into a pixel grayscale set; The median of the pixel grayscale set is taken as the grayscale median value, and the mean of the pixel grayscale value set is calculated as the grayscale mean; The average value of the grayscale median and grayscale mean is calculated as the image grayscale value of the pixel.

3. The image processing measurement method based on drone radar and oblique photography according to claim 2 is characterized in that: In step S1, a radar detection device carried by a UAV is used to perform a radar scan of the forest area, transmit electromagnetic waves to the forest area and receive echo signals; Extract the signal strength value of each signal as the radar echo amplitude according to the echo signal; Each pixel is taken as the center pixel, and the adjacent pixels of the center pixel are combined into a neighborhood set. The grayscale value of the center pixel is subtracted from the image grayscale value of the pixels in the neighborhood set to obtain the grayscale difference of each neighborhood. The standard deviation of the grayscale difference of each neighborhood is calculated as the vegetation change gradient.

4. The image processing measurement method based on drone radar and oblique photography according to claim 3 is characterized in that: In step S2, the vegetation change gradient and radar echo amplitude of the pixel point are respectively normalized and the product result is used as the vegetation distribution feature of the pixel point; Combine the vegetation distribution features of all pixels in the forest image into a vegetation feature set; The median of the vegetation feature set is used as the median of the distribution feature. The median of the distribution feature is subtracted from each vegetation distribution feature to obtain the absolute value to obtain each absolute deviation. The median of each absolute deviation is multiplied by the preset adjustment coefficient to obtain the distribution feature amplitude.

5. The image processing measurement method based on drone radar and oblique photography according to claim 4 is characterized in that: In step S2, the distribution feature threshold is obtained by subtracting the distribution feature median from the distribution feature amplitude; If the vegetation distribution feature of a pixel point is less than the distribution feature threshold, the pixel point is marked as a ground pixel under the forest; otherwise, the pixel point is not marked; Four ground pixels under the forest that form a regular rectangular structure in space are selected as corner points, and the ground grid units under the forest are constructed based on the corner points. The bilinear interpolation method is used to fit the pixel points of the forest ground grid unit to generate uniformly extrapolated ground points.

6. The image processing measurement method based on drone radar and oblique photography according to claim 1 is characterized in that: In step S3, a ground inclinometer is arranged with the center of the grid area formed by the predetermined grid spacing as a measuring point, the elevation and horizontal coordinates of each measuring point are collected, and the local slope value is calculated; When the local slope value is greater than or equal to the preset slope threshold, the grid area where the measuring point is located is defined as a steep slope area; Otherwise, it is not defined; The ratio of the number of measuring points in the grid area defined as the steep slope area to the total number of measuring points is taken as the steep slope ratio.

7. The image processing measurement method based on drone radar and oblique photography according to claim 1 is characterized in that: In step S3, the instantaneous displacement of the tree crown is monitored by deploying a three-dimensional displacement sensor at the tree crown position of the measuring point; The difference between the maximum and minimum values ​​of the instantaneous displacement of the crown within the preset observation period is calculated as the crown fluctuation amplitude; The steep slope ratio and crown fluctuation amplitude were standardized to construct a forest terrain feature vector, and the topographic survey characteristics of each measuring point were output through the logistic regression algorithm.

8. The image processing measurement method based on drone radar and oblique photography according to claim 7, characterized in that: In step S3, if the topographic survey feature is greater than or equal to the preset survey feature threshold, the measurement point is defined as a target area measurement point; Otherwise, the measuring point is not defined; Based on connectivity analysis, the target area measurement points with intervals less than the preset distance are connected to form continuous areas as the division areas of the forest area.

9. The image processing measurement method based on drone radar and oblique photography according to claim 1, characterized in that: In step S4, the number of pixels of the ground under the forest and the divided areas is obtained through oblique photography by the drone, and the number of pixels is converted into spatial area to obtain the ground area under the forest of each divided area and the total area of ​​each divided area; The ratio of the understory area of ​​each divided area to the area of ​​each divided area is taken as the understory area ratio of each divided area; When the proportion of the ground under the forest is lower than the preset proportion threshold, the vegetation coverage state of the divided area is determined to be abnormal coverage; On the contrary, the vegetation cover status of the divided area is normal coverage; The divided areas with abnormal vegetation coverage are screened and marked as abnormal areas, and the extrapolated ground points of the abnormal areas are output for subsequent survey and correction.

10. An image processing and measurement system based on drone radar and oblique photography, used to implement the image processing and measurement method based on drone radar and oblique photography according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, vegetation identification module, terrain analysis module and regional assessment module. The functions of each module are as follows: The data acquisition module is used to survey forest areas. It collects the grayscale value of each pixel in the forest area through oblique photography, obtains the radar echo amplitude of the pixel through radar detection, and calculates the vegetation change gradient based on the image grayscale value. The vegetation recognition module is used to generate vegetation distribution characteristics of each pixel point by integrating vegetation change gradient and radar echo amplitude. Based on the vegetation distribution characteristics, each pixel point is identified and marked to obtain the understory ground in the forest area, and uniformly extrapolated ground points are generated based on the understory ground. The terrain analysis module is used to detect the slope of forest land and the fluctuation amplitude of tree crown in forest areas, calculate the proportion of steep slopes in forest areas based on the slope of forest land, evaluate the terrain survey characteristics of forest areas based on the fluctuation amplitude of tree crown, and divide forest areas into regions based on the terrain survey characteristics; The regional assessment module is used to count the proportion of ground under the forest in the divided area, evaluate the vegetation coverage status of the divided area based on the proportion of ground under the forest, filter the divided area according to the vegetation coverage status, mark the abnormal area and output the extrapolated ground points of the abnormal area.

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