A parallel manual measurement method for control points in image aerotriangulation

Abstract

The present invention discloses a method for parallel manual measurement of control points for image aerial triangulation. It includes the following steps: first, performing aerial triangulation on the original image data with initial positioning parameters without ground control conditions to optimize the geometric positioning parameters; second, dividing the complete survey area into multiple partitions, and predicting the image and image point positions of the control points based on the control point coordinates and image positioning parameters, and cropping the local image blocks with the positions as the center, and saving them to multiple integrated image point data files according to the partitions; then, distributing the image point data files to multiple operators for parallel control point measurement; finally, exporting the measured control point image coordinates and restoring them to the original survey area project to complete the parallel measurement. The present invention maximizes the efficiency of large-scale control point measurement by improving data portability, improving single-point measurement efficiency, and supporting parallel measurement.

Description

Technical Field

[0001] The present invention belongs to the field of photogrammetry and remote sensing, and specifically relates to a parallel manual measurement method for control points for image aerial triangulation, wherein a parallel measurement scheme for local image cropping, management, reading and distribution based on control point coordinate projection is the key technology of the present invention. Background Art

[0002] Aerial triangulation (Aerial Triangulation) is a key technology in photogrammetric geometric processing. Its main task is to determine the precise geometric positioning parameters of all images within a region and the ground coordinates of the points to be determined. Aerial triangulation is the foundation and prerequisite for the production of 4D photogrammetric products. It is widely used in fields such as geographic information systems, remote sensing image processing, and cartography, and has a decisive impact on the accuracy and efficiency of the entire surveying and mapping project. In the process of aerial triangulation, control points are crucial for determining the absolute positioning accuracy of the image area network. The ground three-dimensional coordinates of the control points are known and can be used as geometric constraints to correct and optimize the positioning parameters of the image, thereby ensuring the accuracy of the image data. Specifically, by providing a geographic reference, the control points accurately align the image area network with the actual ground position, ensuring that it meets the accuracy requirements of the relevant scale surveying and mapping specifications. For example, in the production of large-scale topographic maps, accurate control points can significantly improve the quality of the final product.

[0003] With the widespread application of POS (Position and Orientation System) and the development of aerial triangulation processing technology, the bottleneck problems currently faced by image aerial triangulation mainly include huge data volume (the existing aerial triangulation image data is as high as tens of thousands or even hundreds of thousands), low processing efficiency, and difficulty in ensuring accuracy. Among them, the control point measurement problem is particularly prominent. With the increase in image resolution and number, the manual measurement workload is huge and inefficient, making it difficult to meet the needs of fast processing. After importing control points, current photogrammetry processing software can automatically predict the approximate position of the control points on the image based on the ground coordinates of the control points and the initial positioning parameters of the image. However, the precise position of the control points on the image still needs to be accurately adjusted through manual measurement. The current manual measurement of control points mainly has the following two problems: (1) Large workload. With the increasing richness of data sources and the continuous increase in data volume, the number of control points required also increases. A survey area usually contains hundreds, thousands, or even tens of thousands of images, and there is overlap between multiple images. Therefore, each control point may be located on multiple images, resulting in a huge workload for manual measurement. Existing software usually only allows one person to measure control points in a survey area, and it cannot be parallelized, resulting in low work efficiency; (2) Slow speed. As image resolution continues to improve, the size of original image files is getting larger and larger, especially satellite images, where each image is usually over 1GB. When a control point is observed on multiple images, it is necessary to open and read the image data containing the control point one by one; and after adjusting the position of the control point, the image needs to be re-read according to the position. This one-by-one processing method not only has a slow reading speed, but also leads to a slow measurement speed, and a lot of time is wasted on data reading and waiting. For example, if a control point is observed on 30 images, and the processing time for each image is 10 seconds, then the processing time for a single control point is 6 minutes. If the survey area contains hundreds of control points, the overall time consumption will be very huge. Obviously, the single-person measurement method is no longer suitable for the current needs of fast aerial triangulation processing of large-scale image data.

[0004] Therefore, it is necessary to develop a measurement method with fast measurement speed, high measurement efficiency, and the ability to meet the needs of rapid aerial triangulation processing of large-scale image data. Summary of the Invention

[0005] The present invention aims to provide a control point parallel manual measurement method for image aerotriangulation. This method, based on control point-centered image cropping and parallel measurement, boasts fast measurement speed and high efficiency, meeting the requirements for rapid aerotriangulation processing of large-scale image data. This method predicts the position of a control point on all associated images, crops a local image centered on the predicted control point, then integrates all local images of all control points into a single file for copying and distribution. This method achieves image cropping and optimized preservation, significantly reducing image file size while preserving image texture feature information, maximizing image reading speed and measurement efficiency. Furthermore, the cropped image files are distributed according to work tasks, allowing any operator to edit and measure the distributed images, and multiple operators can perform editing and measurement simultaneously, minimizing the workload of a single operator and significantly improving control point measurement efficiency. This method addresses the high workload of single-person measurement in traditional aerotriangulation, as well as the slow and inefficient manual measurement of a large number of control points.

[0006] In order to achieve the above object, the technical solution of the present invention is: a control point parallel manual measurement method for image aerotriangulation, characterized by comprising the following steps:

[0007] Step 1: Obtain the image positioning parameters of the survey area;

[0008] Step 2: Partition the survey area: partition the image data of the survey area and divide the work tasks in advance;

[0009] Step 3: predict the position of the control point on the image based on the image positioning parameters and the control point coordinates in step 1;

[0010] Step 4: With the control point of the predicted point as the center, perform local image cropping and output it to the integrated image point data file;

[0011] Step 5, performing parallel control point measurement according to the partitioning in step 2;

[0012] Step 6: Export the measurement results, obtain the precise image coordinates of the control points, and restore the control point positions.

[0013] In the above technical solution, in step 1, the method of obtaining the positioning parameters of the survey area image includes but is not limited to performing uncontrolled aerial triangulation on the survey area image to obtain the image positioning parameters, obtaining the initial positioning parameters of the original image of the survey area and using them as the image positioning parameters, and directly using the initial positioning parameters of the original image of the survey area to predict the positions of the control points;

[0014] Uncontrolled aerial triangulation is an aerial triangulation of the survey area images without control conditions, which is assisted by the initial positioning parameters to optimize the image positioning parameters.

[0015] In the above technical solution, the specific method of uncontrolled aerial triangulation is as follows:

[0016] Using the initial positioning parameters of the survey area image, aerial triangulation is performed without ground control points to optimize the image positioning parameters and improve the accuracy and consistency of control point position prediction.

[0017] In the above technical solution, in step 2, the entire survey area is divided into several sub-areas based on the geographical scope of the survey area and the coverage of the image data, and then the control point measurement task of each sub-area is assigned to different operators.

[0018] In the above technical solution, in step 3, based on the initial positioning parameters of the image and the coordinates of the control points, projection calculation is performed to predict the survey area image where the control points are located and the image point coordinates of the control points on each image, and the projection is achieved by the following formula:

[0019] (x,y)=f k (X,Y,Z) (1)

[0020] Among them, (X, Y, Z) is the ground coordinate of the control point, (x, y) is the projection function f through image n k (*) Predicted image coordinates of control points.

[0021] In the above technical solution, the latitude and longitude ranges in the metadata of the original image are used to determine whether a control point is located on the image, so that the control point and the image are retrieved and matched before projection to improve computing efficiency. The determination method is as follows:

[0022] Assume that the longitude and latitude coordinates of control point i are (Lat i ,Lon i ), for the latitude and longitude range of the kth image If the control point satisfies the following formula (2), the control point is on the image; otherwise, the control point is not on the image and is deleted.

[0023]

[0024] In the above technical solution, in step 4, after predicting the position of the control point, the survey area image is cropped and saved as a small picture with the control point of the predicted point as the center. For the cropped image, the following parameter information should be recorded: the image plane coordinates of the control point (x, y); the coordinates of the starting point of the cropping window image plane (x0, y0); the cropped image size r×c, where r and c are the number of rows and columns of the cropped image respectively; the above parameter information will be used to restore the position of the point in the original complete image.

[0025] The above parameter information is integrated and output into an integrated image point data file, which includes the following data contents: ground coordinates of control points, cropped local image blocks, original image metadata and parameter information of the cropped image recorded therein.

[0026] In the above technical solution, in step 5, during the manual editing process, infinite scaling technology is used to ensure the accuracy of image analysis and avoid errors introduced by image scaling. At the same time, one image can be selected as a reference image, its image point position can be edited, and then the point can be automatically matched with the same-name point in other images to further improve efficiency. In addition, when the image meets the binocular stereo conditions, stereo measurement of control points in a stereo environment is supported.

[0027] In the above technical solution, in step 6, the precise image point coordinates of the control points are derived independently of the original image data, do not include the image file, and only retain the position data.

[0028] The present invention has the following advantages:

[0029] (1) The editing efficiency of a single control point is significantly improved; the present invention significantly reduces the amount of data read from the original large image to only read a partial image, thus achieving high reading efficiency and fast scaling speed; it overcomes the shortcomings of the prior art that requires a single person to read the entire image, resulting in large data reading volume, low reading efficiency, and slow scaling speed;

[0030] (2) It supports multi-person parallel measurement, improves the overall editing efficiency of multiple control points, and reduces the workload of a single person; by decomposing tasks, the editing tasks of all control points in the entire survey area are assigned to multiple people for parallel editing. Compared with single-person editing, the editing efficiency is improved several times (the editing efficiency of the present invention is approximately equal to the number of people editing in parallel), the measurement speed is fast, the measurement efficiency is high, and it can meet the needs of rapid aerial triangulation processing of large-scale image data (such as tens of thousands or even hundreds of thousands of images); it overcomes the problem that the existing technology usually only allows one person to measure control points in a survey area, cannot be parallelized, has a large workload for single-person measurement, a slow measurement speed, and low measurement efficiency, and cannot meet the needs of rapid aerial triangulation processing of large-scale image data;

[0031] (3) It is highly independent and does not rely on the overall aerial triangulation survey area data containing numerous files. It only needs to integrate the image point data files. It has strong mobility and can perform parallel measurement work on different computers or even different platforms, effectively improving the portability of data, simplifying the data management process, and facilitating the staff to perform real-time measurement operations anytime and anywhere. It overcomes the problem that the existing technology relies on the overall aerial triangulation survey area data containing numerous files, requires measurement work to be performed on a designated computer, cannot transplant and carry data, and cannot realize the real-time measurement operation of the staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is the overall technical flow chart of the present invention;

[0033] Figure 2 This is a schematic diagram of the control point distribution and survey area division of the present invention;

[0034] Figure 3 This is a schematic diagram of the control point image block cropping of the present invention;

[0035] Figure 4 This is a schematic diagram of the control point position measurement of the present invention;

[0036] Figure 5 Schematic diagram of the three-dimensional measurement of the present invention. DETAILED DESCRIPTION

[0037] The following detailed description of the embodiments of the present invention is given in conjunction with the accompanying drawings, which do not limit the present invention but are merely examples. The description makes the advantages of the present invention clearer and easier to understand.

[0038] The present invention discloses a method for parallel manual measurement of control points for image aerial triangulation. The core of the method is to export the local image data required for control point measurement in photogrammetry aerial triangulation operations into independent files, and then perform parallel image point measurement according to the partitions to improve the efficiency of control measurement. With the original image data with initial positioning parameters and control point data as input, first, aerial triangulation is performed on the original image data with initial positioning parameters without ground control conditions to optimize the geometric positioning parameters, or the initial positioning parameters of the original image are directly used to predict the control point positioning. Secondly, the image data is partitioned according to the operation requirements, and the complete survey area is divided into multiple partitions. Based on the control point coordinates and the image initial positioning parameters, the control points are predicted in which images and their image point positions, and the local image blocks are cropped with the position as the center, and saved to multiple integrated image point data files according to the partitions; then, the image point data files are distributed to multiple operators for parallel control point measurement; finally, the measured control point image coordinates are exported and restored to the original survey area project, and the parallel measurement can be completed. Unlike the traditional single-person measurement mode, the method of the present invention is no longer limited to a single operation mode of raw data and engineering. Instead, it maximizes the efficiency of large-scale data control point measurement by improving data portability, improving single-point measurement efficiency and supporting parallel measurement.

[0039] As can be seen from the accompanying drawings, the present invention is primarily implemented through the following technical solution: a parallel manual measurement method for control points for image aerotriangulation. This method uses raw image data with initial positioning parameters and control point data as input. It first performs uncontrolled aerotriangulation on the raw image data, partitions the data according to operational requirements, and predicts the position of the points on the image based on the initial positioning parameters and control point coordinates. It then performs local image cropping, saves the data into a unified file, and distributes it for measurement. The core process includes the following steps:

[0040] Step 1: Uncontrolled aerial triangulation. Perform uncontrolled aerial triangulation on the survey area image with the aid of initial positioning parameters to optimize the image positioning parameters.

[0041] Step 2: Partition the survey area. Partition the survey area data and divide the work tasks in advance.

[0042] Step 3: Predict the position of the control point. Predict the position of the control point on the image based on the image positioning parameters and the coordinates of the control point.

[0043] Step 4: crop the local image and output it to the integrated image point data file;

[0044] Step 5: perform parallel control point measurement according to the partition division;

[0045] Step 6: Export the measurement results and restore the control point positions.

[0046] Furthermore, in step 1, aerial triangulation is performed using the initial positioning parameters of the survey area imagery without ground control points, aiming to optimize the imagery's positioning parameters. Different types of image data typically have different forms of initial positioning parameters. For example, satellite imagery typically uses RPC (Rational Polynomial Coefficients) parameters; aerial and drone imagery uses central projection based on the pinhole imaging principle, whose parameters include the camera's internal orientation elements (focal length, image principal point coordinates, objective lens distortion coefficients, etc.) and external orientation elements (including position and attitude). Through uncontrolled aerial triangulation, these initial positioning parameters can be used to further optimize the imagery's geometric positioning, eliminating systematic errors caused by factors such as sensor and platform motion and external interference, reducing model discrepancies, and thus improving the imagery's relative positioning accuracy and accurately predicting point positions. This is crucial for the subsequent prediction of control point positions on the imagery, significantly improving the accuracy of the predicted control point positions on the imagery and thus increasing the efficiency of manual measurement.

[0047] It's important to emphasize that uncontrolled aerial triangulation isn't strictly necessary. If the initial image positioning parameters are relatively accurate, they can be used directly to predict control point locations. However, because these raw parameters haven't been optimized, large systematic errors can exist between images, leading to significant deviations in the predicted positions of control points across different images. Therefore, to improve the accuracy and consistency of control point predictions, it's generally recommended to perform uncontrolled aerial triangulation before processing. This allows for optimization of the initial positioning parameters, ensuring that the predicted positions are close to the actual points and improving the efficiency of manual measurement.

[0048] Furthermore, in step 2, first, the entire survey area is divided into the following areas according to the geographical scope of the survey area and the coverage of the image data: Figure 2 The process of partitioning data into several sub-regions, as shown, requires consideration of the density of control points and image coverage. For example, data partitioning can be optimized based on the number of images surrounding each control point, ensuring a balanced ratio of images to control points within each partition, thereby improving the efficiency and accuracy of subsequent processing. Secondly, after partitioning is completed, specific work tasks are divided according to the data volume and complexity of each partition and assigned to different processing personnel to achieve an efficient parallel workflow.

[0049] Furthermore, in step 3, based on the initial positioning parameters of the image and the coordinates of the control points, projection calculation is performed to predict which images the control points are located in and the coordinates of the image points on each image. This can be achieved by projection using the following formula:

[0050] (x,y)=f k (X,Y,Z) (1)

[0051] Among them, (X, Y, Z) is the ground coordinate of the control point, (x, y) is the projection function f through image n k (*) Predicted image coordinates of control points.

[0052] Considering that there are many control points and each control point may correspond to multiple images, in order to improve the prediction efficiency, before predicting the control points, it is necessary to retrieve the image number corresponding to the control point in advance. To this end, the latitude and longitude range in the metadata of the original image can be used to determine whether a control point is located on the image. Assume that the longitude and latitude coordinates of the control point i are (Lat i ,Lon i ), for the kth image with a longitude and latitude range Then when the control point satisfies the following formula (2), the control point is on the image;

[0053]

[0054] Furthermore, in step 4, after predicting the position of the control point, the image is cropped with the control point as the center and saved as shown in the attached image. Figure 3 The small local picture shown ( Figure 3 This demonstrates the image segmentation method of the present invention, which significantly reduces storage space, improves portability, and enhances image reading efficiency. For cropped images, the following cropping parameters should be recorded: the image-side coordinates (x, y) of the control point; the image-side coordinates (x0, y0) of the cropping window; and the cropped image size (r×c), where r and c represent the number of rows and columns of the cropped image, respectively. This parameter information will be used to restore the point's position in the original, complete image.

[0055] This information and related image data are integrated and output into a single data file, referred to in this invention as an integrated image point data file. This file includes the following data: the ground coordinates of the control points, the cropped local image blocks, the original image metadata, and the recorded cropping parameters. By storing each control point and its corresponding local image block in each image, this file effectively improves data portability. Workers can directly copy this file to their personal devices for reading, writing, and measurement work, streamlining data management.

[0056] Furthermore, in step 5, the data file generated in step 4 is used to perform the control point measurement. This step is carried out in parallel in different partitions, and each operator performs accurate measurement of the control points according to the assigned task. During the manual editing process, infinite scaling technology is used to ensure the accuracy of image analysis and avoid errors introduced by image scaling. The measurement operation must be completed at high resolution to ensure the accuracy and reliability of the control point position. Single point measurement is shown in the attached figure. Figure 4 As shown ( Figure 4 The measurement effect of the image block of the present invention is demonstrated. Figure 4 It can be seen that the image analysis of the present invention is highly accurate, proving the feasibility of using image blocks for measurement. It can be seen that the present invention has the function of simultaneously measuring different images of a single point using only image blocks). Figure 4 The measurement process of the control point to be measured on multiple image blocks is shown; the multiple image blocks are cut from different scene images using the method of the present invention. In a measurement project, n control points need to be measured, and the project area covers m scene images. For each control point, it is not necessarily located in only one image, but may exist in multiple images (such as 2, 3, 4, etc.); Figure 4 The above shows the measurement process of the control point to be measured on two image blocks; when the control point is on a larger number of images (such as 5, 6, etc.), the method of the present invention can also be used to measure the control point.

[0057] To further improve measurement efficiency, operators can use image matching to assist in image point editing. Specifically, you can select an image as a reference image, edit its image point position, and then automatically match the point with the same name on other images. This method is particularly effective when the control points involve multiple images. For most control points with obvious features, this method can complete the control point measurement with one click, and the operator only needs to quickly visually check it. In addition, when the image meets the binocular stereo conditions, it can also be used in the following examples. Figure 5 Stereo measurement of control points in the stereo environment shown ( Figure 5 It is an effect diagram of stereoscopic image superposition, showing the three-dimensional effect of the stereoscopic image. The present invention can measure the control points in stereoscopic manner based on the three-dimensional effect of the stereoscopic image); Figure 5 It shows that when the image meets the stereo condition, additional control points (including plane and elevation) can be directly measured. It can be seen that the present invention has a stereo measurement function, improves the control point measurement effect, and improves the applicability of the present invention.

[0058] Furthermore, in step 6, the precise image coordinates of the manually edited control points are derived. These coordinates are independent of the original image data and do not include the image file, retaining only the position data. The original control point positions are updated based on the measured data, ensuring that each control point is a manually measured result.

[0059] Example

[0060] The present invention is now described in detail by taking the application of the present invention to aerotriangulation of image data in a certain province as an example, which can also provide guidance for the application of the present invention to aerotriangulation of image data in other regions.

[0061] This example uses satellite imagery covering the entire province of a certain region. The province contains 145 scenes of Ziyuan-3 three-line array imagery, totaling approximately 224GB of data and containing 722 field control points. After creating a project, the control point data is imported.

[0062] This embodiment uses the existing single-person measurement method for measurement. The data read volume is large, the reading efficiency is low, the scaling speed is slow, and the measurement efficiency is low. Specifically, the following are reflected: First, 224GB of data is not suitable and convenient to distribute, and each copy requires more than several hours. Second, when a control point is observed on multiple images, assuming that a point is on 7 images, then the measurement of a single point requires opening approximately 11GB of images simultaneously, which consumes a lot of memory and places high demands on the computer during measurement. Even if the computer has sufficient memory, opening such a large image at the same time will take a long time to render, and zooming and moving will be stuck. Finally, it takes about 5 minutes for a skilled operator to measure a control point (data reading time + manual measurement and adjustment time). Based on the existing technology, since the data can only be measured by a single person, the overall measurement time of this example is approximately 3610 minutes (about 60 hours). The measurement workload is large and the measurement efficiency is low.

[0063] The flowchart of the parallel editing of control points using the method of the present invention is shown in the attached figure. Figure 1 As shown, the following steps are included:

[0064] Step 1: Perform aerial triangulation on the satellite image of the survey area without ground control points to optimize the satellite image RPC parameters. In this embodiment, the survey area data uses satellite image data, so the image positioning parameters here are RPC model parameters, as shown in the following formula:

[0065]

[0066] Among them, (P n ,L n ,H n ) is the regularized ground coordinate, (r n ,c n) are the regularized image coordinates.

[0067] Step 2: Partition the measurement area data. In this embodiment, the measurement area data is divided per person according to three testers (it can also be distributed to other numbers of operators as needed).

[0068] Step 3: Predict control point positions.

[0069] Import all control points that need to be measured. After importing, use the point prediction function to calculate the satellite images and image point coordinates of the predicted control points through RPC projection.

[0070] Step 4: Output the integrated image point data file.

[0071] Based on step 3, export the control point parallel editing data. This exported file contains a small image of each control point across all images, along with cropping parameters and metadata for the original image. After the export is complete, you'll see that the exported small image file size is 3.9GB, a 98.26% reduction in data size compared to the original image. This speeds up scaling and image loading.

[0072] Step 5: Measure control points according to the partition division.

[0073] The file exported in the previous step is copied to the work computers of all operators. The operators can open the files on other devices according to the task division in step 2 to start measurement. It has strong independence and only needs to integrate the image point data files. It has strong mobility and can perform parallel measurement work on different computers or even different platforms. Since the present invention supports multi-person parallel measurement, the overall editing efficiency of multiple control points is improved and the workload of a single person is reduced. Through task decomposition, the editing tasks of all control points in the entire measurement area are assigned to multiple people for parallel editing. Compared with single-person editing, the editing efficiency is improved several times.

[0074] Step 6: Export the measurement results and restore the control point positions.

[0075] After measurement is complete, the measured image point positions are exported as an image point coordinate file. This file does not contain image data and is therefore relatively small. Use the restore function to import the measured control point image point coordinates back into the measurement area. Once all control point positions in the partition file have been restored, control point measurement is complete.

[0076] This embodiment uses the method of the present invention to distribute the cropped and packaged data to three people for simultaneous measurement. Even without considering the scaling increase, the efficiency is increased by 3 times. Taking the scaling increase into account, the speed of image loading is effectively improved, and the time for manual measurement of one point can be reduced to less than 1 minute. The overall measurement time of this embodiment is approximately 12 hours, which is reduced to about 1 / 5 of the existing single-person measurement method, greatly improving the overall measurement efficiency.

[0077] This invention primarily addresses the slow and inefficient control point measurement in traditional aerial triangulation. It proposes a control-point-centered image cropping and parallel measurement method. This method crops images centered on control points and integrates them into separate files for distribution. This method significantly reduces image file size while preserving image texture features and maximizing image access speed. Each participant can simultaneously edit and measure the distributed images, significantly improving measurement efficiency, reducing individual workload, and significantly increasing control point measurement efficiency.

[0078] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

[0079] Other parts not described belong to the prior art.

Claims

1. A control point parallel manual measurement method for image aerotriangulation, characterized by: The following steps are included: Step 1: Obtain the image positioning parameters of the survey area; Step 2: Partition the survey area: partition the survey area data and divide the work tasks in advance; In step 2, the entire survey area is divided into several sub-areas based on the geographical scope of the survey area and the coverage of the image data. The control point measurement tasks of each sub-area are then assigned to different operators. Step 3: Predict the position of the control point on the image based on the image positioning parameters and the coordinates of the control point. Step 4: crop the local image and output the integrated image point data file; In step 4, the image of the survey area is cropped and saved as a small picture with the control point of the predicted point as the center. For the cropped image, the following parameter information is recorded: the image coordinates of the control point ; Coordinates of the starting point of the cropping window image square ;Crop image size ,in 、 Crop the image by number of rows and columns respectively; The above parameter information is integrated and output into an integrated image point data file and copied and distributed. The file includes the following data content: the ground coordinates of the control points, the cropped local image blocks, the original image metadata and the parameter information of the cropped image recorded. The cropped image files are distributed according to the work tasks, and multiple operators can edit and measure the distributed images simultaneously. Step 5: perform parallel control point measurement according to the partition division; In step 5, during the manual editing process, infinite scaling technology is used to ensure the accuracy of image analysis. At the same time, one image is selected as the reference image, its image point position is edited, and then the image point is automatically matched with the same-name points on other images; Step 6: Export the measurement results and restore the control point positions; In step 6, the precise image point coordinates of the control points are derived independently of the original image data, and only the position data is retained; the original control point positions are updated based on the measured data.

2. The control point parallel manual measurement method for image aerotriangulation according to claim 1, characterized in that: In step 1, the method for obtaining the positioning parameters of the survey area image includes performing uncontrolled aerial triangulation on the survey area image to obtain the image positioning parameters, and obtaining the initial positioning parameters of the original image of the survey area as the image positioning parameters.

3. The control point parallel manual measurement method for image aerotriangulation according to claim 2, characterized in that: The specific method of uncontrolled aerial triangulation is as follows: Using the initial positioning parameters of the survey area image, aerial triangulation is performed without ground control points.

4. The control point parallel manual measurement method for image aerotriangulation according to claim 2, characterized in that: In step 3, based on the initial positioning parameters of the image and the coordinates of the control points, projection calculation is performed to predict the survey area image where the control points are located and the image point coordinates of the control points on each image. This is achieved by projection using the following formula: in, are the ground coordinates of the control points, For passing images The projection function The predicted image coordinates of the control points.

5. The control point parallel manual measurement method for image aerotriangulation according to claim 4, characterized in that: The latitude and longitude ranges in the metadata of the original image are used to determine whether a control point is located on the image, so that the control point and the image can be retrieved and paired before projection. The determination method is as follows: Assumed control point The latitude and longitude coordinates are , for the The latitude and longitude range of the image , then when the control point satisfies the following formula (2), the control point is on the image; otherwise, the control point is not on the image and is deleted; 。 6. The control point parallel manual measurement method for image aerotriangulation according to claim 5, characterized in that: In step 5, when the image meets the binocular stereo condition, stereo measurement of control points is supported in a stereo environment.

Images