Beach DEM generation method based on multi-source data fusion

Abstract

The invention provides a beach DEM generation method based on multi-source data fusion, and relates to the technical field of beach landform construction. According to the method, three data sources are preprocessed and calibrated, a proper interpolation algorithm is used, the consistency of the three data sources in resolution and spatial reference is ensured, and the difference of the spatial reference is eliminated through coordinate transformation and projection transformation. Through the measures, beach DEM data with high temporal-spatial resolution are constructed, finally, high-precision data acquisition and DEM generation of the beach terrain are realized through fusion of a multi-source sensor technology, and the method has important application in traditional marine surveying and mapping, environmental monitoring and marine scientific research, and has wide application prospects. The method also plays an important role in the fields of disaster early warning, infrastructure construction and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of beach topography and landform construction, and in particular to a beach DEM generation method based on multi-source data fusion. Background Art

[0002] Beaches are an important part of the coastal system, and their topographic changes directly affect the stability of the coastline, the health of the marine ecosystem, and the safety of human activities. The monitoring and analysis of beach topography is of great significance to coastal engineering, environmental management, ecological protection, and disaster warning.

[0003] Although the traditional beach topography measurement method has high accuracy, it has limitations. For example, RTK (real-time dynamic differential positioning technology) requires surveyors to set up base stations and mobile stations on the beach, perform complex parameter settings and calibration work, and also need to pay attention to the quality and stability of satellite signals in real time. The total station requires surveyors to accurately align the target point, measure the angle and distance, and manually record and process the data. In addition, these devices can only realize single-point measurement of topographic data and cannot meet the requirements for the entire beach topography data.

[0004] In addition, the complexity and variability of beach terrain also poses considerable challenges to the measurement work. Tidal and sea breeze interference may cause errors in the measurement results. Therefore, surveyors need to constantly adjust the measurement methods and equipment parameters to ensure the accuracy and reliability of the measurement.

[0005] In the process of directly using three data sources, namely terrestrial laser scanners, unmanned boats and video image systems, to invert and stitch DEM (digital elevation model), there are problems such as inconsistent resolution and spatial reference. Among them, terrestrial laser scanners have high precision and fast scanning speed, but their point cloud density is not evenly distributed in space. The single-beam depth sounder carried by the unmanned boat can obtain accurate data on underwater terrain, but its resolution is closely related to the navigation speed, water depth and performance of the unmanned boat and the measuring equipment. The video image system extracts terrain information by shooting continuous ground or water surface images using image processing technology. Its resolution is limited by the resolution, focal length and environmental conditions of the camera during shooting. Therefore, the three use different coordinate systems and projection methods, which makes it difficult to accurately align the data when stitching.

[0006] Therefore, a reasonable method is needed to fuse the three data and an efficient method is needed to generate beach DEM. Summary of the invention

[0007] A technical problem to be solved by the present disclosure is that the existing beach DEM generation method is labor-intensive, requires a lot of manpower and time, and is inefficient; it is difficult to quickly obtain a large range of beach terrain data; and the temporal and spatial resolution is low, making it impossible to integrate and splice data.

[0008] In order to solve the above technical problems, the present disclosure provides a beach DEM generation method based on multi-source data fusion, which includes: S1. Obtain the recent highest and lowest tide levels of the beach, and use historical video image data to extract the waterside line of the corresponding time as the boundary line for demarcating the backshore, inner shore and foreshore of the study area, and determine the unified geographic coordinate system for subsequent data processing; S2. Use a 3D laser scanner combined with RTK method to collect field data at low tide, obtain the foreshore point cloud data of the entire beach, and convert it into the first DEM; S3, invert the inner shore topography by combining the video image data with the water edge method, and convert it to obtain the second DEM; S4, using a single-beam unmanned vessel to collect topographic data of the foreshore at high tide, projecting the measured data into the coordinate system in S1, and converting it into the third DEM; S5. Based on the mid-backshore, inner shore and foreshore areas in the historical image of S1 as mask layers, the first DEM in S2, the second DEM in S3 and the third DEM in S4 are mosaicked and spliced ​​according to their geographical locations, and finally a complete beach DEM is obtained.

[0009] In some embodiments, in the aforementioned beach DEM generation method based on multi-source data fusion, in S5, when splicing the three parts of data, it is necessary to adjust the elevation, and the weight of the elevation adjustment is calculated by formula 1. (1) Among them, R 2 The Z is calculated by combining the coordinates of the RTK points actually measured on the beach with the DEM data obtained by interpolation or inversion. RTK is the actual measured RTK point elevation, Z interp The elevation value of the DEM obtained by interpolation or inversion.

[0010] In some embodiments, in the aforementioned beach DEM generation method based on multi-source data fusion, small gaps existing in the mask layer in S5 during extraction are filled using a closing operation method of mathematical morphology.

[0011] In some embodiments, in the aforementioned method for generating a beach DEM based on multi-source data fusion, the method for converting into the first DEM in S2 comprises the following steps: S21, denoising the collected data, and filtering out data with low reflectivity and large waveform deviation; S22, splicing the data of each measuring station; S23, converting the scanner coordinates and the RTK control point coordinates into geographic coordinates so that the two are located in the same reference system, and removing isolated noise points underwater and in the air through visual interpretation; S24. Collect point cloud data of appropriate density, and use the point cloud data to build a TIN model. After removing the nail-like objects in the model, finally convert it into the first DEM through the surface element method.

[0012] In some embodiments, in the aforementioned method for generating a beach DEM based on multi-source data fusion, the method for converting into a second DEM in S3 comprises the following steps: S31. Use RTK to collect image correction control points; S32, selecting at least 5 control points, obtaining their real-world coordinates and corresponding image pixel coordinates, and calculating conversion coefficients for converting the image from a side view image to an orthographic image using the principle of photogrammetry; S33, applying the conversion coefficient to all images to obtain a corrected image, stitching the cameras at the corresponding time in sequence according to the arrangement order, obtaining a panoramic image of the beach, and extracting the waterside line; S34, query the tidal level value at the corresponding time, use the data as the waterside elevation, repeat the above steps, extract all available video image watersides within a day, and then use these waterside elevations to perform spatial interpolation calculations to obtain the second DEM.

[0013] In some embodiments, in the aforementioned beach DEM generation method based on multi-source data fusion, Delaunay-TIN is combined with the natural domain interpolation method to obtain the third DEM in S4.

[0014] Through the above technical scheme, the present disclosure provides a beach DEM generation method based on multi-source data fusion, by pre-processing, calibrating and using appropriate interpolation algorithms for three data sources, ensuring their consistency in resolution and spatial reference, and eliminating the difference in spatial reference through coordinate conversion and projection transformation. Through these measures, we have constructed beach DEM data with high spatiotemporal resolution, and finally, by fusing multi-source sensor technology, we have realized high-precision data collection and DEM generation for beach terrain, and this method has important applications not only in traditional marine mapping, environmental monitoring and marine science research, but also in disaster warning, infrastructure construction and other fields. Play an important role. And through the implementation of the present invention, the accuracy and real-time performance of beach terrain data can be greatly improved, and the digital management method can also improve the sharing and utilization efficiency of data, and promote interdisciplinary research and application. In summary, the beach DEM construction can not only meet the needs of beach management, environmental monitoring and disaster warning, but also promote the application of digital management methods and the development of interdisciplinary research. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 It is a flow chart of a beach DEM generation method based on multi-source data fusion disclosed in an embodiment of the present disclosure; Figure 2 It is a point cloud data diagram in a beach DEM generation method based on multi-source data fusion disclosed in Example 1 of the present disclosure; Figure 3 It is an image processing flow chart of a beach DEM generation method based on multi-source data fusion disclosed in Example 1 of the present disclosure; Figure 4 It is a mask extraction map in a beach DEM generation method based on multi-source data fusion disclosed in Example 1 of the present disclosure; Figure 5 It is a DEM data map in a beach DEM generation method based on multi-source data fusion disclosed in Example 1 of the present disclosure. DETAILED DESCRIPTION

[0017] The following is a further detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present disclosure, but cannot be used to limit the scope of the present disclosure. The present disclosure can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

[0018] The present disclosure provides these embodiments to make the present disclosure thorough and complete, and to fully express the scope of the present disclosure to those skilled in the art. It should be noted that unless otherwise specifically stated, the relative arrangement of the parts and steps, the composition of the materials, the numerical expressions and the numerical values ​​set forth in these embodiments should be interpreted as being merely exemplary, and not as limiting.

[0019] In addition, the words "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different parts. "Vertical" does not mean vertical in the strict sense, but is within the tolerance range. "Parallel" does not mean parallel in the strict sense, but is within the tolerance range. "Include" or "comprising" and similar words mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of including other elements.

[0020] All terms used in the present disclosure have the same meanings as those understood by those of ordinary skill in the art to which the present disclosure belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries, for example, should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0021] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0022] Embodiment 1 Reference Figure 1-Figure 5 This embodiment discloses a beach DEM generation method based on multi-source data fusion. The applicant uses this method to conduct detection in a certain place in June 2024. The coordinate system of all data is unified in this study: the reference ellipsoid datum is CGCS2000, the map projection is Gauss-Krüger projection (epsg: 4550), and the elevation datum is the 1985 National Elevation Datum (mean sea level).

[0023] S1. First, we used the VZ-2000i ground laser scanner in conjunction with the Hi-Target RTK equipment to collect point cloud data. We arranged as few measuring stations as possible while ensuring the integrity of the target point cloud data. In addition, in order to ensure a certain degree of overlap between adjacent measuring stations for the convenience of subsequent point cloud data splicing, we set up a total of 27 measuring stations, divided into two rows, to ensure the comprehensiveness of the detection.

[0024] Specifically, a measurement method combining TLS and GNSS-RTK is used during collection. The coordinate conversion process of a single TLS station is carried out through a scanner-target method. The scanner is located in the station. During the conversion, the target is evenly placed about 5m in front of the scanner. The scanner then performs an environmental scan. The target can determine the coordinates of each station. The coordinates of the target are measured by the mobile station GNSS-RTK receiver. The above work is repeated to obtain point cloud data for the entire area.

[0025] The measured TLS coordinate data was imported into the RisCAN PRO 2.15 post-processing software. First, denoising was performed: data with a reflectivity lower than -25dB and a waveform deviation greater than 20 were filtered out; then the data of each station were spliced. Specifically, the overlap algorithm in the software was used for automatic splicing to achieve preliminary data synthesis. Then the first station was fixed and the feature matching plane was found to achieve precise alignment. In order to make the data accurately express the geographic location, the scanner data was converted to the same reference system by referring to the coordinates of the control points and using geographic coordinate conversion.

[0026] In addition, due to the accumulation of water on the beach that day, some point cloud data were mistakenly suspended below the beach after the laser was reflected. Such noise points and some isolated noise points in the air were removed by manual visual interpretation, and then the first DEM was created by the surface element minimum method. Finally, the grid doctor was used to automatically repair the results to remove minor defects such as spikes, small holes and non-manifolds, and the backshore terrain data used to construct the SBT-DEM was obtained. The grid resolution is 1m, as shown in the figure. Figure 2 shown.

[0027] S2. Reference Figure 3, by writing a matlab program, using the real-world coordinates of the RTK control points obtained by actual measurement and the corresponding image pixel coordinates, 11 parameters for image correction are calculated, including 7 external parameters: three rotation angles Φ, σ, τ of the camera in space, the real-world coordinates xc, yc, zc of the camera, the effective focal length of the camera and 4 external parameters: image center coordinates u0, v0 and two scale factors λu, λv. The obtained correction coefficients are applied to the instantaneous images of several cameras at the same time, and the orthophotos within the range of each camera are obtained respectively. They are fused and spliced ​​in sequence to obtain a panoramic view of the beach in the monitoring area.

[0028] Finally, the editing function of ArcGIS was used to draw the waterside at different times of the day, and then the tide level at the corresponding time was queried from the tide table as the contour elevation value of the waterside, and the ordinary Kriging interpolation method was used to interpolate the second DEM.

[0029] S3. The nearshore depth measurement was completed by an APACHE 6 CHCNAV unmanned boat equipped with a single-beam echo sounder and positioned in GNSS network RTK mode. The navigation path of the unmanned boat was planned using computer control software so that the unmanned boat could travel along the depth measurement track in automatic mode. In addition, for safety reasons, the navigation direction of the unmanned boat was manually controlled by a remote control in areas where obstacles (such as reefs) might exist. The data measured by the unmanned boat is the geodetic height H of the seabed point. The geodetic height system is based on the CGCS2000 reference ellipsoid as the reference surface, and the 1985 National Height System is based on the mean sea level as the reference surface. The formula is used for vertical reference conversion. h=H—N(2) h is the underwater terrain elevation under the 1985 National Height Datum, and N is the geoid difference.

[0030] Finally, the underwater terrain was constructed using Delaunay-TIN in ArcGIS combined with the natural domain interpolation method to obtain the third DEM.

[0031] S4. Reference Figure 4 ,The three DEMs mentioned above are masked and extracted using the demarcated foreshore, inner shore and backshore areas to obtain DEM data of the corresponding areas.

[0032] S5. Reference Figure 5 , using the measured RTK point elevation data and the interpolated data to calculate the R 2 , (1) Among them, R 2The larger the value, the higher the accuracy of the DEM generated by this type of data. When encountering overlapping pixels during stitching, the elevation adjustment value is smaller. Therefore, this data has more reference value. Then the three DEMs are spliced ​​and fused in order. In addition, since the terrain is extracted using masks near the highest and lowest tide lines, there will be a small number of unnecessary small pores. In order to maintain data integrity, the closing operation method of mathematical morphology is used to fill them, and the values ​​of other areas remain unchanged. Among them, the structural element (Structuring Element) used in the closing operation is an 8*8 circular structure. The DEM data of the entire beach is obtained.

[0033] So far, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0034] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present disclosure. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict.

Claims

1. A beach DEM generation method based on multi-source data fusion, characterized in that: include: S1. Obtain the recent highest and lowest tide levels of the beach, and use historical video image data to extract the waterside line of the corresponding time as the boundary line for demarcating the backshore, inner shore and foreshore of the study area, and determine the unified geographic coordinate system for subsequent data processing; S2. Use a 3D laser scanner combined with RTK method to collect field data at low tide, obtain the foreshore point cloud data of the beach, and convert it into the first DEM; S3, invert the inner shore topography by combining the video image data with the water edge method, and convert it to obtain the second DEM; S4, using a single-beam unmanned vessel to collect topographic data of the foreshore at high tide, projecting the measured data into the coordinate system in S1, and converting it into the third DEM; S5. Based on the backshore, inner shore and foreshore areas in the historical image of S1 as mask layers, the first DEM in S2, the second DEM in S3 and the third DEM in S4 are mosaicked and spliced ​​according to their geographical locations, and finally a complete beach DEM is obtained.

2. A beach DEM generation method based on multi-source data fusion according to claim 1, characterized in that: In S5, elevation adjustment is required when splicing the three parts of data. The weight of elevation adjustment is calculated by formula 1. （1） Among them, R 2 The Z is calculated by combining the coordinates of the RTK points actually measured on the beach with the DEM data obtained by interpolation or inversion. RTK is the actual measured RTK point elevation, Z interp The elevation value of the DEM obtained by interpolation or inversion.

3. The beach DEM generation method based on multi-source data fusion according to claim 1 is characterized in that: The small gaps existing in the mask layer described in S5 during extraction are filled using the closing operation method of mathematical morphology.

4. The method for generating beach DEM based on multi-source data fusion according to claim 1 is characterized in that: The method of converting to the first DEM in S2 includes the following steps: S21, denoising the collected point cloud data, and filtering out data with low reflectivity and large waveform deviation; S22, splicing the data of each measuring station; S23, converting the scanner coordinates and the RTK control point coordinates into geographic coordinates so that the two are located in the same reference system, and removing isolated noise points underwater and in the air through visual interpretation; S24, construct a TIN model, remove the spikes in the model, and finally convert it into the first DEM through the surface element method.

5. The method for generating beach DEM based on multi-source data fusion according to claim 1, characterized in that: The method of converting to the second DEM in S3 includes the following steps: S31. Use RTK to collect image correction control points; S32, selecting at least 5 control points, obtaining their real-world coordinates and corresponding image pixel coordinates, and calculating conversion coefficients for converting the image from a side view image to an orthographic image using the principle of photogrammetry; S33, applying the conversion coefficient to all images to obtain a corrected image, stitching the cameras at the corresponding time in sequence according to the arrangement order, obtaining a panoramic image of the beach, and extracting the waterside line; S34, query the tidal level value at the corresponding time, use the data as the waterside elevation, repeat the above steps, extract all available video image watersides within a day, and then use these waterside elevations to perform spatial interpolation calculations to obtain the second DEM.

6. The method for generating beach DEM based on multi-source data fusion according to claim 1, characterized in that: In S4, Delaunay-TIN combined with the natural domain interpolation method is used to obtain the third DEM.

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