Land space planning modeling system based on image processing

Abstract

The application discloses a land space planning modeling system based on image processing and relates to the technical field of land space planning.The system comprises an image acquisition unit, an image verification unit, a parameter analysis unit, a total control unit and a three-dimensional modeling unit.The target image in a sampling area is acquired by a UAV, the definition of the target image is analyzed to calculate the image blurring coefficient, and the flight state is evaluated according to the flight parameters and the flight environment is analyzed according to the real-time environmental data.When the target image is not clear due to the influence of the flight state, the UAV can be controlled to return along the UAV flight path to realize secondary sampling.When the target image is not clear due to the influence of the flight environment, the UAV can be controlled to return to the starting point to realize secondary sampling at another time, so that the flight efficiency of the UAV in executing the flight task is ensured and the definition of the modeling source image is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of land space planning, and particularly relates to a land space planning modeling system based on image processing. BACKGROUND

[0002] With the acceleration of urbanization and rapid social and economic development, land space planning plays a crucial role in the development of countries and regions. It involves land use, urban construction, resource allocation, environmental protection and other aspects, and has far-reaching significance for achieving sustainable development, improving the quality of life of residents and promoting regional coordinated development. However, traditional land space planning methods have gradually shown many limitations in the face of increasingly complex real-world demands and rapidly changing environments.

[0003] The most important part of land space planning is data acquisition, and then presenting data effects through modeling. Compared with traditional two-dimensional maps, three-dimensional space modeling provides more rich data, which can contain more dimensions and information, and provides more comprehensive basis for planning design and decision analysis. In the prior art, in the traditional three-dimensional space modeling process, in order to obtain accurate information of the ground, an unmanned aerial vehicle is usually used for aerial photography. However, due to environmental factors, flight parameters and coverage rate, the photos taken by the unmanned aerial vehicle may have problems such as deviation and blur, which affects the imaging accuracy of the ground and further affects the accuracy and accuracy of three-dimensional space modeling.

[0004] In view of the above technical defects, the present application provides a solution. SUMMARY

[0005] The purpose of the present application is to evaluate the flight state according to the flight parameters and analyze the flight environment in real time. When the target image is not clear due to the influence of the flight state, the unmanned aerial vehicle can be controlled to return along the unmanned aerial vehicle flight path to realize secondary sampling. When the target image is not clear due to the influence of the flight environment, the unmanned aerial vehicle is controlled to return to the starting point and needs to be resampled at another time, which ensures the flight efficiency of the unmanned aerial vehicle in executing the flight task and ensures the image clarity of the modeling source.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a land space planning modeling system based on image processing, comprising an image acquisition unit, an image verification unit, a parameter analysis unit, a total control unit and a three-dimensional modeling unit.

[0007] The image acquisition unit is used to divide the target area of land space planning into multiple sampling areas, and plan the unmanned aerial vehicle flight path in the sampling area and send it to the total control unit. The target image in the sampling area is obtained by the unmanned aerial vehicle, and the target image is sent to the image verification unit in real time.

[0008] The image verification unit is used to acquire and process the target image, perform sharpness analysis on the target image to calculate the image blur coefficient, and determine that the target image does not meet the sharpness requirements based on the preset blur threshold to generate an error retrieval signal.

[0009] The parameter analysis unit is used to acquire and process error retrieval signals, including a flight parameter analysis module and an environmental factor analysis module. The flight parameter analysis module is used to acquire the real-time flight parameters of the UAV, evaluate the flight status based on the flight parameters, and generate a flight return signal based on the flight status evaluation results and send it to the main control unit.

[0010] The environmental factor analysis module is used to acquire real-time environmental data of the environment in which the UAV is located, perform flight environment analysis based on the real-time environmental data, and generate a flight withdrawal signal based on the flight environment analysis results and send it to the main control unit.

[0011] The main control unit is used to acquire the UAV flight path to control the UAV to fly along the predetermined path within the acquired area to acquire images. It can also acquire flight withdrawal signals to control the UAV to return to the starting point, and acquire flight return signals to return along the UAV flight path to achieve secondary sampling. The sampled target image is then sent to the 3D modeling unit.

[0012] The 3D modeling unit is used to acquire and process target images, construct a 3D geographic model based on the target images, build a building model on the basis of the 3D geographic model, enhance the realism and three-dimensionality of the model through texture mapping modeling algorithms, and generate the final 3D spatial model.

[0013] Furthermore, the 3D modeling unit also includes a model dynamic update module. When a new target image is acquired and compared with historical target images, the 3D spatial model is automatically updated when the difference between the new target image and the historical target image reaches a preset update reference value, ensuring that the 3D spatial model is consistent with the actual situation.

[0014] Furthermore, the specific process for generating the error retrieval signal is as follows:

[0015] S101. Obtain the flight parameters of the UAV, including flight speed and shooting coverage. Set UAV shooting nodes according to the flight parameters and shooting coverage. During the UAV's flight mission along the flight path, acquire target images at each UAV shooting node.

[0016] S102. After denoising the target image, the image blur coefficient D(f) of the target image is calculated using the weighted gradient algorithm: , where e1 and e2 are preset weight coefficients, f(x,y) is the gray value of the corresponding pixel (x,y) in the target image, x and y are the horizontal and vertical coordinates of the pixel in the target image, and G(x,y) is the convolution of the Laplacian operator of the pixel (x,y).

[0017] S103. The image blur coefficient is used to reflect the clarity of the target image. The larger the image blur coefficient, the higher the clarity of the target image. Conversely, the smaller the image blur coefficient, the lower the clarity of the target image. A preset blur threshold is obtained. If the image blur coefficient is less than or equal to the blur threshold, the clarity of the target image does not meet the requirements, and an error retrieval signal is generated.

[0018] Furthermore, the specific process for generating the flight return signal is as follows:

[0019] S201. Obtain the real-time flight parameters of the UAV, including real-time flight altitude data Hi, real-time flight speed data Vi, and flight attitude coefficient Fi. The flight attitude coefficient Fi is calculated by combining the flight roll angle, flight pitch angle, and flight yaw angle.

[0020] S202. By acquiring the front target image of the target image, feature extraction is performed based on the front target image to obtain the ground occlusion coverage η in the front target image;

[0021] S203. Calculate the flight stability coefficient Ki according to the following formula: , where α, β and γ are preset weighting coefficients, Hmax is the restricted flight altitude within the sampling area, Vmax is the restricted flight speed within the sampling area, and the flight stability coefficient is used to reflect the flight state of the UAV when performing the sampling task. The larger the flight stability coefficient, the more stable the flight state of the UAV tends to be, and vice versa.

[0022] S204. Obtain the preset flight stability threshold. If the flight stability coefficient Ki is less than or equal to the flight stability threshold, it means that the UAV is in an unstable state and does not meet the sampling task requirements. At this time, a flight return signal is generated, and updated flight parameters to be adjusted are generated according to the real-time flight parameters and sent to the main control unit.

[0023] Furthermore, the specific process for generating the flight withdrawal signal is as follows:

[0024] S301. Obtain real-time environmental data of the environment in which the drone is located, including light intensity data E, wind speed data Vb, and temperature data Yi;

[0025] S302. Obtain real-time weather forecast data, determine the probability of rainfall based on the real-time weather forecast data, and convert the probability of rainfall into a specific rainfall coefficient value ω according to the preset probability judgment standard;

[0026] S303. Calculate the flight environment assessment coefficient Wi according to the following formula: Among them, e3, e4 and e5 are preset weight coefficients. The flight environment evaluation coefficient is used to reflect the degree of influence of the current environment of the UAV on the flight status. The larger the flight environment evaluation coefficient, the more favorable the environment of the UAV is to perform flight sampling tasks. The smaller the flight environment evaluation coefficient, the more unfavorable the environment of the UAV is to perform flight sampling tasks.

[0027] S304. Obtain the preset flight environment assessment threshold. If the flight stability coefficient Ki is less than or equal to the flight environment assessment threshold, it indicates that the flight environment is interfering with the sampling task. At this time, a flight withdrawal signal is generated.

[0028] Furthermore, after the main control unit controls the UAV to return to the starting point, it obtains the UAV's flight mission content and marks the flight mission content as incomplete.

[0029] After the main control unit receives the flight withdrawal signal, it controls the UAV to turn around at the signal point and acquires the UAV's real-time flight trajectory. Based on the preset resampling data S, it controls the UAV to complete the flight distance S meters along the real-time flight trajectory. When the UAV reaches the resampling point, it adjusts the UAV parameters to acquire the target image again. After the UAV completes the flight mission and returns to the starting point, it marks the flight mission as completed.

[0030] Furthermore, the specific process for generating the final three-dimensional spatial model is as follows: Based on the geographic information system, obtain the planar distribution data of the target area of ​​the national land spatial planning; construct basic two-dimensional coordinates based on the planar distribution data; acquire target images; use two or more target images taken from different angles using stereo vision method; calculate the three-dimensional structure using parallax; and then infer the depth information of the object by comparing the pixels in different images. Thus, a three-dimensional geographic model is constructed based on the basic two-dimensional coordinates. Extract the contour features of the object from target images from multiple different perspectives; and construct the building model using texture mapping modeling algorithm.

[0031] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0032] This image processing-based land spatial planning modeling system acquires target images within a sampling area using drones. It performs sharpness analysis on the target images to calculate the image blur coefficient. If the target image does not meet the sharpness requirements, it assesses the flight status based on flight parameters and performs flight environment analysis using real-time environmental data. If the target image is unclear due to flight status, the drone can be controlled to return along its flight path for secondary sampling. If the target image is unclear due to flight environment, the drone is controlled to return to its starting point and secondary sampling needs to be performed at a later time. This system ensures both the flight efficiency of the drone in performing its flight mission and the clarity of the images used for modeling. Attached Figure Description

[0033] Figure 1 A schematic diagram of the overall structure of the present invention is shown. Detailed Implementation

[0034] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example:

[0036] like Figure 1 As shown, the image processing-based land spatial planning modeling system includes an image acquisition unit, an image verification unit, a parameter analysis unit, a central control unit, and a 3D modeling unit.

[0037] The image acquisition unit is used to divide the target area of ​​the land spatial planning into multiple sampling areas, and to plan the flight path of the UAV within the sampling area and send it to the central control unit. The UAV acquires the target image within the sampling area and sends the target image to the image verification unit in real time.

[0038] The image verification unit is used to acquire and process the target image, perform sharpness analysis on the target image to calculate the image blur coefficient, and determine that the target image does not meet the sharpness requirements based on the preset blur threshold to generate an error retrieval signal.

[0039] The specific process of generating the error retrieval signal is as follows:

[0040] S101. Obtain the flight parameters of the UAV, including flight speed and shooting coverage. Set the UAV shooting nodes according to the flight parameters and shooting coverage. During the flight mission of the UAV along the flight path, acquire the target image at each UAV shooting node.

[0041] S102. After denoising the target image, the image blur coefficient D(f) of the target image is calculated using the weighted gradient algorithm: , where e1 and e2 are preset weight coefficients, f(x,y) is the gray value of the corresponding pixel (x,y) in the target image, x and y are the horizontal and vertical coordinates of the pixel in the target image, and G(x,y) is the convolution of the Laplacian operator of the pixel (x,y).

[0042] S103. The image blur coefficient is used to reflect the clarity of the target image. The larger the image blur coefficient, the higher the clarity of the target image. Conversely, the smaller the image blur coefficient, the lower the clarity of the target image. Obtain the preset blur threshold. If the image blur coefficient is less than or equal to the blur threshold, the clarity of the target image does not meet the requirements, and an error retrieval signal is generated.

[0043] The parameter analysis unit is used to acquire and process error retrieval signals, including a flight parameter analysis module and an environmental factor analysis module. The flight parameter analysis module is used to acquire the real-time flight parameters of the UAV, evaluate the flight status based on the flight parameters, and generate a flight return signal based on the flight status evaluation results and send it to the main control unit.

[0044] The specific process for generating the flight return signal is as follows:

[0045] S201. Obtain the real-time flight parameters of the UAV. The real-time flight parameters include real-time flight altitude data Hi, real-time flight speed data Vi, and flight attitude coefficient Fi. The flight attitude coefficient Fi is calculated by combining the flight roll angle, flight pitch angle, and flight yaw angle.

[0046] S202. By acquiring the front target image of the target image, feature extraction is performed based on the front target image to obtain the ground occlusion coverage η in the front target image;

[0047] S203. Calculate the flight stability coefficient Ki according to the following formula: , where α, β and γ are preset weighting coefficients, Hmax is the restricted flight altitude within the sampling area, Vmax is the restricted flight speed within the sampling area, and the flight stability coefficient is used to reflect the flight state of the UAV when performing the sampling task. The larger the flight stability coefficient, the more stable the flight state of the UAV tends to be, and vice versa.

[0048] S204. Obtain the preset flight stability threshold. If the flight stability coefficient Ki is less than or equal to the flight stability threshold, it means that the UAV is in an unstable state and does not meet the sampling task requirements. At this time, a flight return signal is generated, and updated flight parameters to be adjusted are generated according to the real-time flight parameters and sent to the main control unit.

[0049] The environmental factor analysis module is used to acquire real-time environmental data of the environment in which the UAV is located, perform flight environment analysis based on the real-time environmental data, and generate a flight withdrawal signal based on the flight environment analysis results and send it to the main control unit.

[0050] The specific process for generating a flight withdrawal signal is as follows:

[0051] S301. Obtain real-time environmental data of the environment in which the drone is located. The real-time environmental data includes light intensity data E, wind speed data Vb, and temperature data Yi.

[0052] S302. Obtain real-time weather forecast data, determine the probability of rainfall based on the real-time weather forecast data, and convert the probability of rainfall into a specific rainfall coefficient value ω according to the preset probability judgment standard;

[0053] S303. Calculate the flight environment assessment coefficient Wi according to the following formula: Among them, e3, e4 and e5 are preset weight coefficients. The flight environment evaluation coefficient is used to reflect the degree of influence of the current environment of the UAV on the flight status. The larger the flight environment evaluation coefficient, the more favorable the environment of the UAV is to perform flight sampling tasks. The smaller the flight environment evaluation coefficient, the more unfavorable the environment of the UAV is to perform flight sampling tasks.

[0054] S304. Obtain the preset flight environment assessment threshold. If the flight stability coefficient Ki is less than or equal to the flight environment assessment threshold, it indicates that the flight environment is interfering with the sampling task. At this time, a flight withdrawal signal is generated.

[0055] The main control unit is used to acquire the UAV flight path to control the UAV to fly along the predetermined path within the acquired area to acquire images. It can also acquire flight withdrawal signals to control the UAV to return to the starting point, and acquire flight return signals to return along the UAV flight path to achieve secondary sampling. The sampled target image is then sent to the 3D modeling unit.

[0056] After the main control unit controls the drone to return to the starting point, it obtains the drone's flight mission information and marks the flight mission information as incomplete.

[0057] After the main control unit receives the flight withdrawal signal, it controls the UAV to turn around at the signal point and acquires the UAV's real-time flight trajectory. Based on the preset resampling data S, it controls the UAV to complete the flight distance S meters along the real-time flight trajectory. When the UAV reaches the resampling point, it adjusts the UAV parameters to acquire the target image again. After the UAV completes the flight mission and returns to the starting point, it marks the flight mission as completed.

[0058] The 3D modeling unit is used to acquire and process target images, construct a 3D geographic model based on the target images, build a building model on the basis of the 3D geographic model, enhance the realism and three-dimensionality of the model through texture mapping modeling algorithms, and generate the final 3D spatial model.

[0059] The specific process for generating the final 3D spatial model is as follows: Based on the geographic information system, obtain the planar distribution data of the target area of ​​the national land spatial planning; construct basic 2D coordinates based on the planar distribution data; acquire target images; use two or more target images taken from different angles using stereo vision method; calculate the 3D structure using parallax; and then infer the depth information of the object by comparing the pixels in different images. Thus, a 3D geographic model is constructed based on the basic 2D coordinates. Extract the contour features of the object from target images from multiple different perspectives; and construct the building model using texture mapping modeling algorithm.

[0060] The 3D modeling unit also includes a model dynamic update module. When a new target image is acquired and compared with historical target images, the 3D spatial model is automatically updated when the difference between the new target image and the historical target image reaches a preset update reference value, ensuring that the 3D spatial model is consistent with the actual situation.

[0061] This invention acquires target images within a sampling area using a drone, performs sharpness analysis on the target images to calculate the image blur coefficient, and determines that the target image does not meet the sharpness requirements. It then performs flight environment analysis based on flight parameters and real-time environmental data. If the target image is unclear due to flight status, the drone can be controlled to return along its flight path for secondary sampling. If the target image is unclear due to flight environment, the drone is controlled to return to its starting point and secondary sampling needs to be performed at a later time. This approach ensures both the flight efficiency of the drone in performing its mission and the sharpness of the images used for modeling.

[0062] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0063] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0064] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A land spatial planning modeling system based on image processing, characterized in that, It includes an image acquisition unit, an image verification unit, a parameter analysis unit, a central control unit, and a 3D modeling unit; The image acquisition unit is used to divide the target area of ​​the land spatial planning into multiple sampling areas, and to plan the flight path of the UAV within the sampling area and send it to the central control unit. The UAV acquires the target image within the sampling area and sends the target image to the image verification unit in real time. The image verification unit is used to acquire and process the target image, perform sharpness analysis on the target image to calculate the image blur coefficient, and generate an error retrieval signal if the target image does not meet the sharpness requirements according to the preset blur threshold. The parameter analysis unit is used to acquire and process error retrieval signals, including a flight parameter analysis module and an environmental factor analysis module. The flight parameter analysis module is used to acquire the real-time flight parameters of the UAV, evaluate the flight status based on the flight parameters, and generate a flight return signal based on the flight status evaluation results and send it to the main control unit. The environmental factor analysis module is used to acquire real-time environmental data of the environment in which the UAV is located, perform flight environment analysis based on the real-time environmental data, and generate a flight withdrawal signal based on the flight environment analysis results and send it to the main control unit. The main control unit is used to acquire the UAV flight path to control the UAV to fly along the predetermined path within the sampling area to acquire images. It can also acquire flight withdrawal signals to control the UAV to return to the starting point, and acquire flight return signals to return along the UAV flight path to achieve secondary sampling. The sampled target image is then sent to the 3D modeling unit. The specific process for generating the flight return signal is as follows: S201. Obtain the real-time flight parameters of the UAV, including real-time flight altitude data Hi, real-time flight speed data Vi, and flight attitude coefficient Fi. The flight attitude coefficient Fi is calculated by combining the flight roll angle, flight pitch angle, and flight yaw angle. S202. By acquiring the front target image of the target image, feature extraction is performed based on the front target image to obtain the ground occlusion coverage η in the front target image; S203. Calculate the flight stability coefficient Ki according to the following formula: , where α, β and γ are preset weighting coefficients, Hmax is the restricted flight altitude within the sampling area, Vmax is the restricted flight speed within the sampling area, and the flight stability coefficient is used to reflect the flight state of the UAV when performing the sampling task; S204. Obtain the preset flight stability threshold. If the flight stability coefficient Ki is less than or equal to the flight stability threshold, it means that the UAV is in an unstable state and does not meet the sampling task requirements. At this time, a flight return signal is generated, and updated flight parameters to be adjusted are generated according to the real-time flight parameters and sent to the main control unit. The 3D modeling unit is used to acquire and process target images, construct a 3D geographic model based on the target images, build a building model on the basis of the 3D geographic model, enhance the realism and three-dimensionality of the model through texture mapping modeling algorithms, and generate the final 3D spatial model.

2. The image processing-based land spatial planning modeling system according to claim 1, characterized in that, The 3D modeling unit also includes a model dynamic update module. When a new target image is acquired and compared with historical target images, the 3D spatial model is automatically updated when the difference between the new target image and the historical target image reaches a preset update reference value, ensuring that the 3D spatial model is consistent with the actual situation.

3. The image processing-based land spatial planning modeling system according to claim 1, characterized in that, The specific process of generating the error retrieval signal is as follows: S101. Obtain the flight parameters of the UAV, including flight speed and shooting coverage. Set UAV shooting nodes according to the flight parameters and shooting coverage. During the flight mission performed by the UAV along the flight path, acquire target images at each UAV shooting node. S102. After denoising the target image, the image blur coefficient D(f) of the target image is calculated using the weighted gradient algorithm: , where e1 and e2 are preset weight coefficients, f(x,y) is the gray value of the corresponding pixel (x,y) in the target image, x and y are the horizontal and vertical coordinates of the pixel in the target image, and G(x,y) is the convolution of the Laplacian operator of the pixel (x,y). S103. The image blur coefficient is used to reflect the clarity of the target image. A preset blur threshold is obtained. If the image blur coefficient is less than or equal to the blur threshold, the clarity of the target image does not meet the requirements, and an error retrieval signal is generated.

4. The image processing-based land spatial planning modeling system according to claim 1, characterized in that, The specific process for generating a flight withdrawal signal is as follows: S301. Obtain real-time environmental data of the environment in which the drone is located, including light intensity data E, wind speed data Vb, and temperature data Yi; S302. Obtain real-time weather forecast data, determine the probability of rainfall based on the real-time weather forecast data, and convert the probability of rainfall into a specific rainfall coefficient value ω according to the preset probability judgment standard; S303. Calculate the flight environment assessment coefficient Wi according to the following formula: Among them, e3, e4 and e5 are preset weighting coefficients, and the flight environment assessment coefficient is used to reflect the degree of influence of the current environment of the UAV on the flight status. S304. Obtain the preset flight environment assessment threshold. If the flight stability coefficient Ki is less than or equal to the flight environment assessment threshold, it indicates that the flight environment is interfering with the sampling task. At this time, a flight withdrawal signal is generated.

5. The image processing-based land spatial planning modeling system according to claim 1, characterized in that, After the main control unit controls the drone to return to the starting point, it obtains the drone's flight mission content and marks the flight mission content as incomplete. After the main control unit receives the flight withdrawal signal, it controls the UAV to turn around at the signal point and acquires the UAV's real-time flight trajectory. Based on the preset resampling data S, it controls the UAV to complete the flight distance S meters along the real-time flight trajectory. When the UAV reaches the resampling point, it adjusts the UAV parameters to acquire the target image again. After the UAV completes the flight mission and returns to the starting point, it marks the flight mission as completed.

6. The image processing-based land spatial planning modeling system according to claim 1, characterized in that, The specific process for generating the final 3D spatial model is as follows: Based on the geographic information system, obtain the planar distribution data of the target area of ​​the national land spatial planning; construct basic 2D coordinates based on the planar distribution data; acquire target images; use two or more target images taken from different angles using stereo vision method; calculate the 3D structure using parallax; and then infer the depth information of the object by comparing the pixels in different images. Thus, a 3D geographic model is constructed based on the basic 2D coordinates. Extract the contour features of the object from target images from multiple different perspectives; and construct the building model using texture mapping modeling algorithm.

Images