Coastal zone measurement method and system based on aerial image and laser point data fusion

Abstract

The invention provides a coastal zone measurement method and system based on aerial image and laser point cloud data fusion. The method comprises the following steps: acquiring LiDAR point cloud data and aerial remote sensing images by using an unmanned aerial vehicle carried integrated system; preprocessing the remote sensing image; preprocessing the airborne laser sounding data; constructing a water depth inversion model; applying the model to remote sensing image water depth inversion; and outputting the water depth data of the target water area and the high-precision three-dimensional submarine topographic map. According to the invention, the precision and efficiency of coastal zone detection can be improved, high-precision submarine topography data can be obtained, the problem of multi-system fusion light and small size is solved, rapid water depth measurement of shallow water areas such as islands and reefs can be realized, and the depth measurement precision can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of marine technology, and in particular relates to a coastal zone measurement method and system based on the fusion of aerial images and laser point cloud data. Background Art

[0002] The coastal zone is the area where the land meets the ocean, and it has important ecological, economic and strategic value. The detection and monitoring of the coastal zone is an important part of marine scientific research and the development and utilization of marine resources. At present, the commonly used coastal zone detection methods are mainly the following:

[0003] Remote sensing satellite-based method: Use remote sensing satellites to obtain coastal image data, then process and analyze the images, extract feature information, and perform water depth inversion. This method has the advantages of wide coverage, good periodicity, and low cost, but it also has disadvantages such as low resolution, great weather influence, and poor real-time performance.

[0004] UAV-based method: UAVs are equipped with various sensors, such as visible light cameras, infrared cameras, radars, etc., to detect and photograph the coastal zone, and then process and analyze the images to extract feature information and perform water depth inversion. This method has the advantages of high resolution, good real-time performance, and strong flexibility, but it also has disadvantages such as low flight altitude, high interference, and short endurance.

[0005] Sonar-based method: Use sonar to emit sound waves, receive the reflected sound wave signals, and detect the coastal zone based on the intensity, frequency, phase and other parameters of the sound wave signals. This method has the advantages of being suitable for underwater environments and having strong anti-interference capabilities, but it also has the disadvantages of low resolution, being greatly affected by water flow, and requiring a lot of calculations.

[0006] The above methods each have their own advantages and disadvantages, but they all have a common problem, that is, it is difficult to accurately and quickly identify unknown or complex maritime targets.

[0007] In view of this, it is very meaningful to propose a coastal zone measurement method and system based on the fusion of aerial images and laser point cloud data. Summary of the invention

[0008] The present invention provides a coastal zone measurement method and system based on the fusion of aerial images and laser point cloud data to solve the above-mentioned technical defects.

[0009] In a first aspect, the present invention proposes a coastal zone measurement method based on the fusion of aerial images and laser point cloud data, the method comprising the following steps:

[0010] S1. Use drones equipped with an integrated system to detect and collect coastal zones in designated areas to obtain LiDAR point cloud data and aerial remote sensing images;

[0011] S2. Preprocessing the acquired remote sensing image, including radiation correction and geometric correction. The radiation correction eliminates the influence of sensor characteristics and atmospheric conditions on the image by adopting a correction algorithm based on a physical model. The geometric correction uses high-precision ground control points and image matching technology to accurately align the image data with a known coordinate system to ensure the spatial consistency of the data.

[0012] S3, preprocessing the acquired LiDAR point cloud data, including data preprocessing, waveform data processing, error correction and point cloud data processing, to obtain high-precision water depth data on the measurement route; further using the measured high-precision water depth points as control points, combined with the grayscale value of the aerial photogrammetry image, to construct a water depth inversion model;

[0013] S4, using the processed laser data and image data to perform water depth inversion based on the constructed water depth inversion model, optimizing the model by introducing a machine learning algorithm, and generating high-precision seabed topography data using three-dimensional reconstruction technology; and

[0014] S5. Further output the water depth data and high-precision three-dimensional seabed topographic map of the target water area, and output the generated topographic data in a graphical or textual form as a high-precision three-dimensional seabed topographic map and display it on a terminal device for users to view and analyze.

[0015] Preferably, step S3 also includes:

[0016] An algorithm that uses the difference between the radiance of pixels in shallow water areas and the radiance of pixels in deep water areas in the same image for logarithmic linear transformation is used to explain the exponential attenuation of visible light in water using only a single band, and then the linear model is further extended to dual-band and multi-band. The algorithm formula is: Among them, a 0 and a i is a constant coefficient, N is the number of bands participating in the inversion, R i is the gray value of the band, (R ∞ ) i It is the grayscale value of the pixel in the optically deep water area adjacent to the shallow water area.

[0017] Further preferably, X i =ln(R i -(R ∞ ) i ), combined with the panchromatic band image of aerial photogrammetry, the water depth inversion model is constructed as follows: z = a 0 +a 1 ln(R 全色 -(R ∞ )全色 ), the high-precision water depth extracted from the laser radar echo waveform or point cloud is input into the water depth inversion model, and the model parameter a is calculated 0 and a 1 , that is, the construction of the water depth inversion model is completed.

[0018] Preferably, in step S1, the integrated system includes a visible light camera and an airborne lidar, and the integrated system can simultaneously acquire remote sensing images and laser depth sounding data of the coastal zone of a designated area, and the drone has the function of adjusting flight parameters to adapt to different coastal zone measurement environments.

[0019] Preferably, in step S3, the data preprocessing removes outliers and noise through a preset data cleaning algorithm; the waveform data processing uses preset waveform decomposition and feature extraction techniques to improve the accuracy of waveform recognition and the precision of depth calculation; the error correction is based on real-time environmental monitoring data and sensor error models for precise correction; the point cloud data processing uses an optimized point cloud filtering and classification algorithm to automatically adjust filtering parameters according to coastal terrain characteristics to obtain high-precision water depth data on the measurement route, and the accuracy of the point cloud data after processing is at the centimeter level.

[0020] Preferably, in step S4, the machine learning algorithm includes a neural network algorithm, a linear regression algorithm and a logistic regression algorithm.

[0021] Preferably, in step S5, it also includes: the output high-precision three-dimensional seabed topographic map supports multiple format exports and interactive operations.

[0022] In a second aspect, an embodiment of the present invention provides a coastal zone measurement system based on the fusion of aerial images and laser point cloud data, comprising:

[0023] A data acquisition module configured to detect and collect coastal zones in a designated area using a drone equipped with an integrated system to obtain LiDAR point cloud data and aerial remote sensing images;

[0024] An image preprocessing module is configured to preprocess the acquired remote sensing images, including radiation correction and geometric correction. The radiation correction eliminates the influence of sensor characteristics and atmospheric conditions on the images by adopting a correction algorithm based on a physical model. The geometric correction uses high-precision ground control points and image matching technology to accurately align the image data with a known coordinate system to ensure the spatial consistency of the data.

[0025] A laser point cloud preprocessing module is configured to preprocess the acquired LiDAR point cloud data, including data preprocessing, waveform data processing, error correction and point cloud data processing, so as to obtain high-precision water depth data on the measurement route;

[0026] A water depth inversion construction module is configured to construct a water depth inversion model by using the measured high-precision water depth points as control points and combining the grayscale values ​​of the aerial photogrammetry images;

[0027] A model application module is configured to use the processed laser data and image data to perform water depth inversion based on the constructed water depth inversion model, optimize the model by introducing a machine learning algorithm, and generate high-precision seabed topography data using a three-dimensional reconstruction technology;

[0028] The display result module is configured to output the water depth data and high-precision three-dimensional seabed topographic map of the target water area, and output the generated topographic data in a graphical or textual form as a high-precision three-dimensional seabed topographic map and display it on the terminal device for users to view and analyze.

[0029] Compared with the prior art, the beneficial results of the present invention are:

[0030] The present invention can improve the accuracy and efficiency of coastal zone detection, obtain high-precision seabed topography data at the same time, solve the problem of multi-system fusion and miniaturization, realize rapid measurement of water depth in shallow water areas such as islands and reefs, and improve depth measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present invention. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0032] Figure 1 A schematic diagram of a process of a coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to an embodiment of the present invention;

[0033] Figure 2 A flowchart of laser point cloud and aerial image water depth inversion according to a specific embodiment of the present invention;

[0034] Figure 3 The present invention is a schematic diagram of a coastal zone measurement system architecture based on the fusion of aerial images and laser point cloud data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0036] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] Figure 1 A coastal zone measurement method based on the fusion of aerial images and laser point cloud data disclosed in an embodiment of the present invention is shown. Figure 1 As shown, the method comprises the following steps:

[0038] S1. Use drones equipped with an integrated system to detect and collect coastal zones in designated areas to obtain LiDAR point cloud data and aerial remote sensing images;

[0039] Specifically, the integrated system includes a visible light camera and an airborne lidar. The integrated system can simultaneously obtain remote sensing images and laser depth sounding data of the coastal zone of a specified area. The drone has the function of adjusting flight parameters to adapt to different coastal measurement environments.

[0040] S2. Preprocessing the acquired remote sensing image, including radiation correction and geometric correction. The radiation correction eliminates the influence of sensor characteristics and atmospheric conditions on the image by adopting a correction algorithm based on a physical model. The geometric correction uses high-precision ground control points and image matching technology to accurately align the image data with a known coordinate system to ensure the spatial consistency of the data.

[0041] S3, preprocessing the acquired LiDAR point cloud data, including data preprocessing, waveform data processing, error correction and point cloud data processing, to obtain high-precision water depth data on the measurement route; further using the measured high-precision water depth points as control points, combined with the grayscale value of the aerial photogrammetry image, to construct a water depth inversion model;

[0042] Further, step S3 also includes: using an algorithm that uses the difference between the radiance of the pixels in the shallow water area and the radiance of the pixels in the deep water area in the same image to perform a logarithmic linear transformation, using only a single band to explain the exponential attenuation of visible light in water, and then further extending the linear model to dual-band and multi-band, the algorithm formula is: Among them, a 0 and a i is a constant coefficient, N is the number of bands participating in the inversion, R i is the gray value of the band, (R∞ ) i It is the grayscale value of the pixel in the optically deep water area adjacent to the shallow water area.

[0043] Note X i =ln(R i -(R ∞ ) i ), combined with the panchromatic band image of aerial photogrammetry, the water depth inversion model is constructed as follows: z = a 0 +a 1 ln(R 全色 -(R ∞ ) 全色 ), the high-precision water depth extracted from the laser radar echo waveform or point cloud is input into the water depth inversion model, and the model parameter a is calculated 0 and a 1 , that is, the construction of the water depth inversion model is completed.

[0044] Among them, the data preprocessing removes outliers and noise through a preset data cleaning algorithm; the waveform data processing uses preset waveform decomposition and feature extraction technology to improve the accuracy of waveform recognition and the precision of depth calculation; the error correction is based on real-time environmental monitoring data and sensor error models for precise correction; the point cloud data processing adopts optimized point cloud filtering and classification algorithms, automatically adjusts filtering parameters according to the coastal terrain characteristics, and obtains high-precision water depth data on the measurement route, and the accuracy after the point cloud data processing is at the centimeter level.

[0045] S4, using the processed laser data and image data to perform water depth inversion based on the constructed water depth inversion model, optimizing the model by introducing a machine learning algorithm, and generating high-precision seabed topography data using three-dimensional reconstruction technology; and

[0046] Specifically, machine learning algorithms include neural network algorithms, linear regression algorithms, and logistic regression algorithms. Machine learning algorithms are a type of algorithm that enables computers to automatically learn patterns and rules from data and use the learned knowledge to perform prediction, classification, clustering, or other tasks. These algorithms continuously optimize their own model parameters through analysis and training of large amounts of data to improve their ability to process unknown data.

[0047] S5. Further output the water depth data and high-precision three-dimensional seabed topographic map of the target water area, and output the generated topographic data in a graphical or textual form as a high-precision three-dimensional seabed topographic map and display it on a terminal device for users to view and analyze.

[0048] Furthermore, the output high-precision three-dimensional seabed topographic map supports multiple formats of export and interactive operation. In addition, the output three-dimensional seabed topographic map supports virtual reality (VR) and augmented reality (AR) display, and users can perform immersive viewing and analysis by wearing corresponding devices.

[0049] Figure 2 FIG. 1 is a flowchart of laser point cloud and aerial image water depth inversion according to a specific embodiment of the present invention. Figure 2 As shown, specifically including:

[0050] Step 1: Use the drone to carry an integrated system to obtain LiDAR point cloud data and aerial remote sensing images;

[0051] Use drones equipped with miniaturized integrated land and water measurement equipment, including visible light cameras and airborne lidar, to detect and collect data on the coastal areas of designated areas, while obtaining remote sensing images and laser depth sounding data.

[0052] Step 2: Preprocess the remote sensing image;

[0053] Perform radiometric correction on image data to eliminate the effects of sensor characteristics and atmospheric conditions on images. Perform geometric correction to align image data with a known coordinate system to ensure spatial consistency of the data.

[0054] Step 3: Preprocess the airborne laser bathymetric data;

[0055] After data preprocessing, waveform data processing, error correction, and point cloud data processing, the water depth data on the measurement route is finally obtained.

[0056] Step 4: Construct a water depth inversion model;

[0057] The high-precision water depth points obtained by lidar measurement are used as control points, and the water depth inversion model is constructed in combination with the grayscale values ​​of aerial photogrammetry images.

[0058] A log-linear transformation is used to transform the difference between the radiance of pixels in shallow water and the radiance of pixels in deep water in the same image. Only a single band is used to explain the exponential attenuation of visible light in water, and the linear model is then further extended to dual-band and multi-band. The specific algorithm formula is as follows:

[0059]

[0060] Among them, a 0 and a i is a constant coefficient, N is the number of bands participating in the inversion, R i is the gray value of the band, (R ∞ ) iIt is the gray value of the pixel in the optically deep water area near the shallow water area. Usually denoted by X i =ln(R i -(R ∞ ) i ), combined with the panchromatic band image of aerial photogrammetry, the water depth inversion model can be constructed as:

[0061] z=a 0 +a 1 ln(R 全色 -(R ∞ ) 全色 )

[0062] The high-precision water depth extracted from the laser radar echo waveform or point cloud is input into the water depth inversion model to solve the model parameter a 0 and a 1 , that is, the construction of the water depth inversion model is completed.

[0063] Step 5: Apply the model to remote sensing image water depth inversion;

[0064] Using laser data and image data, water depth inversion is performed based on the model, and high-precision seabed topography data is generated using technologies such as three-dimensional reconstruction.

[0065] Step 6: Output the water depth data of the target water area and a high-precision three-dimensional seabed topographic map;

[0066] The generated terrain data is output in graphical or textual form as a high-precision three-dimensional seabed topographic map and displayed on the terminal device for users to view and analyze.

[0067] Further references Figure 3 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a system, which is similar to Figure 1 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0068] In the second aspect, an embodiment of the present invention also discloses a coastal zone measurement system based on the fusion of aerial images and laser point cloud data, including: a data acquisition module 31, an image preprocessing module 32, a laser point cloud preprocessing module 33, a water depth inversion construction module 34, a model application module 35 and a display result module 36.

[0069] In a specific embodiment, the data acquisition module 31 is configured to detect and collect coastal zones in a designated area using an integrated system carried by a drone to obtain LiDAR point cloud data and aerial remote sensing images; the image preprocessing module 32 is configured to preprocess the acquired remote sensing images, including radiation correction and geometric correction. The radiation correction eliminates the influence of sensor characteristics and atmospheric conditions on the images by adopting a correction algorithm based on a physical model. The geometric correction uses high-precision ground control points and image matching technology to accurately align the image data with a known coordinate system to ensure the spatial consistency of the data; the laser point cloud preprocessing module 33 is configured to preprocess the acquired LiDAR point cloud data, including data preprocessing, waveform data processing, error correction and point cloud data processing, to obtain high-precision water depth data on the measurement route;

[0070] The water depth inversion construction module 34 is configured to use the measured high-precision water depth points as control points, combined with the grayscale values ​​of the aerial photogrammetry images, to construct a water depth inversion model; the model application module 35 is configured to use the processed laser data and image data to perform water depth inversion based on the constructed water depth inversion model, optimize the model by introducing a machine learning algorithm, and generate high-precision seabed topography data using three-dimensional reconstruction technology; the display result module 36 is configured to output the water depth data and high-precision three-dimensional seabed topography map of the target water area, and output the generated topography data in a graphical or textual manner as a high-precision three-dimensional seabed topography map and display it on a terminal device for user viewing and analysis.

[0071] The functions of the above modules correspond to the methods and will not be repeated here.

[0072] Compared with existing manufacturing technologies, the present invention can improve the accuracy and efficiency of coastal zone detection, while obtaining high-precision seabed topography data, solving the problem of multi-system fusion and miniaturization, and can achieve rapid measurement of water depth in shallow water areas such as islands and reefs and improve depth measurement accuracy.

[0073] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

Claims

1. A coastal zone measurement method based on the fusion of aerial images and laser point cloud data, characterized in that: The method comprises the following steps: S1. Use drones equipped with an integrated system to detect and collect coastal zones in designated areas to obtain LiDAR point cloud data and aerial remote sensing images; S2. Preprocessing the acquired remote sensing image, including radiation correction and geometric correction. The radiation correction eliminates the influence of sensor characteristics and atmospheric conditions on the image by adopting a correction algorithm based on a physical model. The geometric correction uses high-precision ground control points and image matching technology to accurately align the image data with a known coordinate system to ensure the spatial consistency of the data. S3, preprocessing the acquired LiDAR point cloud data, including data preprocessing, waveform data processing, error correction and point cloud data processing, to obtain high-precision water depth data on the measurement route; further using the measured high-precision water depth points as control points, combined with the grayscale value of the aerial photogrammetry image, to construct a water depth inversion model; S4, using the processed laser data and image data to perform water depth inversion based on the constructed water depth inversion model, optimizing the model by introducing a machine learning algorithm, and generating high-precision seabed topography data using three-dimensional reconstruction technology; and S5. Further output the water depth data and high-precision three-dimensional seabed topographic map of the target water area, and output the generated topographic data in a graphical or textual form as a high-precision three-dimensional seabed topographic map and display it on a terminal device for users to view and analyze.

2. The coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to claim 1 is characterized in that: Step S3 also includes: An algorithm that uses the difference between the radiance of pixels in shallow water areas and the radiance of pixels in deep water areas in the same image for logarithmic linear transformation is used to explain the exponential attenuation of visible light in water using only a single band, and then the linear model is further extended to dual-band and multi-band. The algorithm formula is: Among them, a0 and a i is a constant coefficient, N is the number of bands participating in the inversion, R i is the gray value of the band, (R ∞ ) i It is the grayscale value of the pixel in the optically deep water area adjacent to the shallow water area.

3. The coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to claim 2 is characterized in that: Note X i =ln(R i -(R ∞ ) i ), combined with the panchromatic band image of aerial photogrammetry, the water depth inversion model is constructed as follows: z=a0+a1ln(R 全色 -(R ∞ ) 全色 ) The high-precision water depth extracted from the laser radar echo waveform or point cloud is input into the water depth inversion model, and the parameters a0 and a1 of the model are calculated, thus completing the construction of the water depth inversion model.

4. The coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to claim 1 is characterized in that: In step S1, the integrated system includes a visible light camera and an airborne laser radar. The integrated system can simultaneously obtain remote sensing images and laser depth sounding data of the coastal zone of a specified area. The drone has the function of adjusting flight parameters to adapt to different coastal zone measurement environments.

5. The coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to claim 1 is characterized in that: In step S3, the data preprocessing removes outliers and noise through a preset data cleaning algorithm; the waveform data processing uses preset waveform decomposition and feature extraction technology to improve the accuracy of waveform recognition and the precision of depth calculation; The error correction is accurately performed based on real-time environmental monitoring data and sensor error models; The point cloud data processing adopts an optimized point cloud filtering and classification algorithm, automatically adjusts the filtering parameters according to the terrain characteristics of the coastal zone, obtains high-precision water depth data on the measurement route, and the accuracy of the point cloud data after processing is at the centimeter level.

6. The coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to claim 1 is characterized in that: In step S4, the machine learning algorithm includes a neural network algorithm, a linear regression algorithm and a logistic regression algorithm.

7. The coastal zone measurement method based on the fusion of aerial images and laser point cloud data according to claim 1 is characterized in that: In step S5, it also includes: the output high-precision three-dimensional seabed topographic map supports multiple format exports and interactive operations.

8. A coastal zone measurement system based on the fusion of aerial images and laser point cloud data, characterized in that: include: A data acquisition module configured to detect and collect coastal zones in a designated area using a drone equipped with an integrated system to obtain LiDAR point cloud data and aerial remote sensing images; An image preprocessing module is configured to preprocess the acquired remote sensing images, including radiation correction and geometric correction. The radiation correction eliminates the influence of sensor characteristics and atmospheric conditions on the images by adopting a correction algorithm based on a physical model. The geometric correction uses high-precision ground control points and image matching technology to accurately align the image data with a known coordinate system to ensure the spatial consistency of the data. A laser point cloud preprocessing module is configured to preprocess the acquired LiDAR point cloud data, including data preprocessing, waveform data processing, error correction and point cloud data processing, so as to obtain high-precision water depth data on the measurement route; A water depth inversion construction module configured to construct a water depth inversion model by using the measured high-precision water depth points as control points and combining them with the grayscale values ​​of the aerial photogrammetry images; A model application module is configured to use the processed laser data and image data to perform water depth inversion based on the constructed water depth inversion model, optimize the model by introducing a machine learning algorithm, and generate high-precision seabed topography data using a three-dimensional reconstruction technology; The display result module is configured to output the water depth data and high-precision three-dimensional seabed topographic map of the target water area, and output the generated topographic data in a graphical or textual form as a high-precision three-dimensional seabed topographic map and display it on the terminal device for users to view and analyze.

Images