Photography positioning measurement system based on double-spliced camera

Abstract

The invention, which relates to the image processing field, discloses a photographing positioning measurement system based on a double-spliced camera, comprising a measurement center which is in communication connection with a data acquisition module, a data processing module, an image analysis module and a measurement feedback module. The data acquisition module is used for performing data acquisition on an area needing to be measured to obtain corresponding area information; the data processing module is used for carrying out data processing on the obtained regional information to obtain a corresponding photographic image; the image analysis module is used for carrying out image analysis on the obtained high-frequency image and constructing a corresponding region fusion image based on an analysis result; the measurement feedback module is used for performing measurement feedback based on the obtained region fusion image; according to the method, the image quality in the measurement process is effectively improved, the image analyzability is enhanced, and the measurement efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and in particular to a photography positioning measurement system based on a double-stitched camera. Background Art

[0002] With the rapid development of advanced manufacturing, dual-camera photogrammetry has been widely used in fields such as aircraft, automobiles, and urban mapping.

[0003] Compared with existing technologies, traditional photogrammetric positioning measurement systems usually use a single camera for shooting. This configuration has certain limitations in improving image resolution and expanding coverage. Single-camera systems either sacrifice coverage at high resolution or reduce image resolution when expanding coverage, making it difficult to simultaneously meet the needs of high resolution and large coverage. This greatly reduces the final measurement results. These are the problems we need to solve, and for this reason we provide a photogrammetric positioning measurement system based on dual cameras. Summary of the Invention

[0004] The object of the present invention is to provide a photographic positioning measurement system based on a double-stitched camera.

[0005] The object of the present invention can be achieved by the following technical solution: a photographic positioning measurement system based on a double-stitched camera, characterized in that it includes a measurement center, wherein the measurement center is communicatively connected to a data acquisition module, a data processing module, an image analysis module, and a measurement feedback module;

[0006] The data acquisition module is used to collect data on the required measurement area and obtain corresponding area information;

[0007] The data processing module is used to process the obtained regional information to obtain corresponding photographic images;

[0008] The image analysis module is used to perform image analysis on the obtained high-frequency image and construct a corresponding regional fusion image based on the analysis result;

[0009] The measurement feedback module is used to perform measurement feedback based on the obtained regional fusion image.

[0010] Furthermore, the data acquisition module collects data from the required measurement area to obtain corresponding area information, including:

[0011] Obtaining pre-set hovering calibration points within the required measurement area and marking the corresponding required measurement area as a target area; and formulating a corresponding image acquisition route based on the hovering calibration points;

[0012] The data acquisition module is composed of a dual-camera and a monitoring node, and the data acquisition module is deployed on the airborne platform of the UAV;

[0013] The drone drives within the target area based on the constructed image acquisition route. When the drone drives to the same level as a hovering calibration point, the acquisition module acquires images of the target area based on the dual-camera to obtain a corresponding hovering image, which includes a left-view image and a right-view image. At the same time, the monitoring node acquires relevant information of the corresponding image acquisition process to obtain corresponding attitude data and radiation data.

[0014] The hovering images, attitude data, and radiation data corresponding to all hovering calibration points are counted to obtain the corresponding area information and upload it to the measurement center.

[0015] Furthermore, the data processing module processes the obtained regional information to obtain a corresponding photographic image, which includes:

[0016] Reading radiation data in the area information, the radiation data including solar radiation energy and solar radiation angle;

[0017] Based on big data technology, the functional relationship between solar radiation energy and different solar radiation angles is obtained, and the corresponding correction model is constructed based on it;

[0018] After the construction is completed, the collected radiation data is input into the constructed correction model to obtain the corresponding correction coefficient;

[0019] Read the collected hover image and obtain the grayscale value of the pixel in the corresponding hover image;

[0020] Performing grayscale correction on the pixels in the hovering image based on the obtained correction coefficient;

[0021] After grayscale correction is completed, the flight attitude parameters of the UAV in the corresponding attitude data are read, and based on them, the pixel points in the corresponding hovering image are corrected;

[0022] After the image correction is completed, the hovering image that has completed the image correction is marked as a high-frequency image, where the high-frequency image includes a left-view high-frequency image and a right-view high-frequency image.

[0023] Furthermore, the process of the image analysis module performing image analysis on the obtained high-frequency image includes:

[0024] Read the high-frequency image corresponding to the corresponding hovering calibration point;

[0025] Based on the SIFT feature algorithm, feature extraction is performed on the left-view high-frequency image and the right-view high-frequency image in the corresponding high-frequency image to obtain corresponding left-view features and right-view features;

[0026] Constructing a two-dimensional rectangular coordinate system, and mapping the obtained left-view features and right-view features into the constructed two-dimensional rectangular coordinate system, respectively, to obtain pixel coordinates of the corresponding left-view features and right-view features in the respective high-frequency images;

[0027] Then, based on the pre-built coordinate mapping model, coordinate mapping is performed on the pixel coordinates corresponding to the corresponding left-view features and right-view features to obtain the corresponding ground mapping coordinates;

[0028] Obtain the ground mapping coordinates corresponding to the corresponding left-view feature and right-view feature respectively, compare them, and mark the pixel points corresponding to the left-view feature and the right-view feature at the same ground mapping coordinate as the first splicing point;

[0029] A node evaluation is performed on the obtained first splicing points based on a preset splicing standard, and whether the corresponding first splicing points meet the requirements is determined according to the evaluation result. If not, the corresponding first splicing points are eliminated; if yes, the corresponding first splicing points are retained.

[0030] Furthermore, the stitching criterion includes that the pixel difference between the pixels corresponding to the corresponding left-view features and the right-view features is minimum, and the similarity of the geometric structures within the corresponding pixels satisfies a pre-set similarity threshold.

[0031] Furthermore, the process of obtaining the corresponding region fusion image includes:

[0032] Overlapping the corresponding left-view high-frequency image and the right-view high-frequency image based on the retained first splicing point;

[0033] After the overlap is completed, obtaining the image intersection point between the corresponding left-view high-frequency image and the right-view high-frequency image; constructing a corresponding image stitching curve based on the image intersection point and in combination with the first stitching point;

[0034] Stitching the corresponding left-view high-frequency image and the right-view high-frequency image based on the image stitching curve, and fusing the overlapping parts of the left-view high-frequency image and the right-view high-frequency image during the image stitching process; after the image fusion is completed, obtaining the corresponding calibration image;

[0035] The same method as above is used to process the hovering images corresponding to other hovering calibration points to obtain corresponding calibration images; and the corresponding calibration images are stitched to obtain the regional fusion image corresponding to the corresponding target area.

[0036] Furthermore, the corresponding image fusion process includes:

[0037] Performing image extraction on corresponding overlapping image parts to obtain corresponding left overlapping images and right overlapping images;

[0038] Obtain the grayscale values ​​corresponding to the pixels of the left and right overlapping images respectively, obtain the deviation values ​​between the pixels in the overlapping state, and mark them as visual deviations;

[0039] Based on the guided filtering algorithm, the corresponding left overlapping image and right overlapping image are decomposed to obtain the corresponding low-frequency image and high-frequency image;

[0040] Performing logarithm processing on the obtained low-frequency component image and high-frequency component image until the processing is completed;

[0041] Fusing the low-frequency image and the high-frequency image corresponding to the left overlapping image and the right overlapping image respectively to obtain a corresponding low-frequency fused image and a high-frequency fused image;

[0042] Superimposing the corresponding low-frequency fusion image and the high-frequency fusion image, and performing weighted summation to obtain the corresponding fusion image;

[0043] Based on the obtained visual deviation, the deviation value corresponding to the pixel point in the corresponding fused image is corrected. Once the correction is completed, the image fusion is completed.

[0044] Furthermore, the process of the measurement feedback module performing measurement feedback based on the obtained regional fusion image includes:

[0045] Obtaining pixel coordinates corresponding to corresponding hovering calibration points in the corresponding region fusion image, and obtaining ground mapping coordinates corresponding to the corresponding hovering calibration points based on the coordinate mapping model;

[0046] Then, the pixel coordinates in the fused image of the corresponding area are adjusted twice based on the ground coordinates corresponding to the corresponding hovering calibration points;

[0047] After the adjustment is completed, the latitude and longitude in the posture data are read and marked in the corresponding area fusion image to obtain a high-resolution image file with a geographic tag, which is fed back to the measurement center and stored.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By integrating solar radiation data, the present invention realizes real-time correction of solar radiation energy received by the UAV during flight, thereby improving the accuracy and reliability of remote sensing monitoring data;

[0050] 2. The present invention adopts a dual-hyperspectral camera design, which can simultaneously capture hyperspectral images of two adjacent areas, achieving full coverage of a wide river surface and improving data acquisition efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0052] like Figure 1 As shown, the photogrammetric positioning measurement system based on the double-stitched camera includes a measurement center, which is communicatively connected to a data acquisition module, a data processing module, an image analysis module, and a measurement feedback module;

[0053] The data acquisition module is used to collect data on the required measurement area and obtain corresponding area information;

[0054] The data processing module is used to process the obtained regional information to obtain corresponding photographic images;

[0055] The image analysis module is used to perform image analysis on the obtained high-frequency image and construct a corresponding regional fusion image based on the analysis result;

[0056] The measurement feedback module is used to perform measurement feedback based on the obtained regional fusion image;

[0057] It should be further explained that, in a specific implementation process, the data acquisition module collects data from the required measurement area, and the process of obtaining corresponding area information includes:

[0058] Obtain hovering calibration points set within the desired measurement area and mark the corresponding desired measurement area as the target area. The hovering calibration points represent ground control points that are selected within the corresponding target area, which are obvious, stable, uniformly located at the same terrain height and evenly distributed. This helps significantly improve the accuracy of coordinate transformation and the spatial reference of the image. For example, the hovering calibration points can be set at obvious identification marks within the target area, such as road intersections, building boundaries, farmland boundaries, airports, or city outlines.

[0059] Then, a corresponding image acquisition route is formulated based on the hovering calibration points, and the image acquisition route must include all the set hovering calibration points;

[0060] The data acquisition module is composed of a dual-camera and a monitoring node, and the data acquisition module is deployed on the airborne platform of the UAV;

[0061] The drone drives within the target area based on the constructed image acquisition route. When the drone drives to the same level as a hovering calibration point, the acquisition module acquires images of the target area based on the dual-camera to obtain a corresponding hovering image, which includes a left-view image and a right-view image. At the same time, the monitoring node acquires relevant information of the corresponding image acquisition process to obtain corresponding attitude data and radiation data.

[0062] Count the hovering images, attitude data, and radiation data corresponding to all hovering calibration points, obtain the corresponding area information, and upload it to the measurement center;

[0063] It should be further explained that, in a specific implementation process, the data processing module processes the obtained regional information to obtain a corresponding photographic image, which includes:

[0064] Reading radiation data in the area information, the radiation data including solar radiation energy and solar radiation angle;

[0065] Based on big data technology, the functional relationship between solar radiation energy and different solar radiation angles is obtained, and the corresponding correction model is constructed based on it;

[0066] After the construction is completed, the collected radiation data is input into the constructed correction model to obtain the corresponding correction coefficient. The correction coefficient is used to reflect the change in radiation energy caused by the change in the solar altitude angle and is used to adjust the image brightness;

[0067] Read the collected hover image and obtain the grayscale value of the pixel in the corresponding hover image;

[0068] Grayscale correction is performed on the pixels in the hovering image based on the obtained correction coefficient. The corresponding mathematical formula is as follows:

[0069] Where Hi represents the grayscale value corresponding to pixel i after grayscale correction of the hover image, where i = 1, 2, ..., n, n > 0 and n is an integer; hi represents the grayscale value corresponding to pixel i in the hover image before correction;

[0070] z1 and z2 can be provided by the collected attitude data, which includes the collection time, collection parameters, longitude and latitude, and flight attitude parameters of the UAV;

[0071] After grayscale correction is completed, read the flight attitude parameters of the UAV in the corresponding attitude data, and based on them, obtain the corresponding internal element parameters, external element parameters and the elevation information corresponding to the photography center of the corresponding dual-camera;

[0072] Then, based on the obtained internal element parameters, external element participation and the elevation information corresponding to the photographic center of the corresponding double-stitched camera, the pixel points in the corresponding hovering image are corrected graphically. The corresponding mathematical formula is as follows:

[0073] The corresponding mathematical formula is to start from the corrected pixel point P (X, Y), obtain the coordinates (x, y) of the corresponding pixel point p on the original hovering image based on the intrinsic element parameters, extrinsic element parameters and elevation information of the image, and assign them to the corresponding pixel point P based on the interpolation algorithm; where P = p, and both are natural numbers;

[0074] After the image correction is completed, the hovering image that has completed the image correction is marked as a high-frequency image, wherein the high-frequency image includes a left-view high-frequency image and a right-view high-frequency image;

[0075] It should be further explained that, in the specific implementation process, the internal element data is the parameter describing the relative position between the photographic center (i.e., the optical center) and the photo, and is used to determine the projection center position and scale on the photo; the external element data refers to the basic data for determining the geometric relationship of the photographic light beam in the object space, and is used to characterize the spatial position of the photographic light beam at the moment of photography; the elevation information of the photographic center refers to the height value of the photographic center on a certain elevation reference plane; for example: the vertical distance between the photographic center and the corresponding hovering calibration point.

[0076] It should be further explained that, in a specific implementation process, the image analysis module performs image analysis on the obtained high-frequency image and constructs a corresponding regional fusion image based on the analysis result, including the following process:

[0077] Taking a certain hovering calibration point as an example, read the high-frequency image corresponding to the corresponding hovering calibration point;

[0078] Based on the SIFT feature algorithm, feature extraction is performed on the left-view high-frequency image and the right-view high-frequency image in the corresponding high-frequency image to obtain corresponding left-view features and right-view features;

[0079] Constructing a two-dimensional rectangular coordinate system, and mapping the obtained left-view features and right-view features into the constructed two-dimensional rectangular coordinate system, respectively, to obtain pixel coordinates of the corresponding left-view features and right-view features in the respective high-frequency images;

[0080] Then, based on a pre-built coordinate mapping model, coordinate mapping is performed on the pixel coordinates corresponding to the corresponding left-view features and right-view features to obtain corresponding ground mapping coordinates. The coordinate mapping model is used to express the mapping relationship between the pixel coordinates in the corresponding hover image and the ground coordinates. The construction process of the coordinate mapping model is prior art and will not be elaborated in detail in this invention.

[0081] Obtain the ground mapping coordinates corresponding to the corresponding left-view feature and right-view feature respectively, compare them, and mark the pixel points corresponding to the left-view feature and the right-view feature at the same ground mapping coordinate as the first splicing point;

[0082] Furthermore, a node evaluation is performed on the obtained first stitching points based on a preset stitching standard, and a determination is made based on the evaluation result as to whether the corresponding first stitching points meet the requirements. If not, the corresponding first stitching points are discarded; if they meet the requirements, the corresponding first stitching points are retained. The stitching standard includes that the pixel difference between the pixels corresponding to the corresponding left-view feature and the right-view feature is minimized, and the similarity of the geometric structures within the corresponding pixels satisfies a similarity threshold, where the pixel threshold is a fixed value determined according to actual needs.

[0083] Then, overlapping the corresponding left-view high-frequency image and the right-view high-frequency image based on the first splicing point;

[0084] After the overlap is completed, the image intersection between the corresponding left-view high-frequency image and the right-view high-frequency image is obtained;

[0085] constructing a corresponding image stitching curve based on the image intersection points and in combination with the first stitching points;

[0086] Furthermore, the corresponding left-view high-frequency image and the right-view high-frequency image are stitched based on the image stitching curve, and the overlapping portion of the left-view high-frequency image and the right-view high-frequency image during the image stitching process is fused. The corresponding image fusion process includes:

[0087] Performing image extraction on corresponding overlapping image parts to obtain corresponding left overlapping images and right overlapping images;

[0088] Obtain the grayscale values ​​corresponding to the pixels of the left and right overlapping images respectively, obtain the deviation values ​​between the pixels in the overlapping state, and mark them as visual deviations;

[0089] Based on the guided filtering algorithm, the corresponding left overlapping image and right overlapping image are decomposed to obtain the corresponding low-frequency image and high-frequency image;

[0090] Performing logarithm processing on the obtained low-frequency component image and high-frequency component image until the processing is completed;

[0091] Fusing the low-frequency image and the high-frequency image corresponding to the left overlapping image and the right overlapping image respectively to obtain a corresponding low-frequency fused image and a high-frequency fused image;

[0092] Superimposing the corresponding low-frequency fusion image and the high-frequency fusion image, and performing weighted summation to obtain the corresponding fusion image;

[0093] Based on the obtained visual deviation, the deviation value corresponding to the pixel point in the corresponding fused image is corrected, and once the correction is completed, the image fusion is completed;

[0094] After image fusion is completed, the corresponding calibration image is obtained;

[0095] The same method as above is used to process the hovering images corresponding to other hovering calibration points to obtain corresponding calibration images; and the corresponding calibration images are stitched to obtain the regional fusion image corresponding to the corresponding target area.

[0096] It should be further explained that, in a specific implementation process, the process of the measurement feedback module for performing measurement feedback based on the obtained regional fusion image includes:

[0097] Obtaining pixel coordinates corresponding to corresponding hovering calibration points in the corresponding region fusion image, and obtaining ground mapping coordinates corresponding to the corresponding hovering calibration points based on the coordinate mapping model;

[0098] Then, the pixel coordinates in the fused image of the corresponding area are adjusted twice based on the ground coordinates corresponding to the corresponding hovering calibration points;

[0099] After the adjustment is completed, the latitude and longitude in the posture data are read and marked in the corresponding area fusion image to obtain a high-resolution image file with a geographic tag, which is fed back to the measurement center and stored;

[0100] It should be further explained that, in the specific implementation process, an embodiment of the present invention also includes: a visualization model is pre-set in the measurement center, and the visualization model can present the obtained high-resolution image files with geographic tags to the staff in a three-dimensional visual manner based on GIS technology.

[0101] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A photogrammetric positioning measurement system based on a double-camera, characterized in that: It includes a measurement center, which is communicatively connected to a data acquisition module, a data processing module, an image analysis module, and a measurement feedback module; The data acquisition module is used to collect data on the required measurement area and obtain corresponding area information; The data processing module is used to process the obtained regional information to obtain corresponding photographic images; The image analysis module is used to perform image analysis on the obtained high-frequency image and construct a corresponding regional fusion image based on the analysis result; The measurement feedback module is used to perform measurement feedback based on the obtained regional fusion image.

2. The photogrammetric positioning measurement system based on a double-stitched camera according to claim 1, characterized in that: The data acquisition module acquires data from the required measurement area to obtain corresponding area information, including: Obtaining pre-set hovering calibration points within the required measurement area and marking the corresponding required measurement area as a target area; and formulating a corresponding image acquisition route based on the hovering calibration points; The data acquisition module is composed of a dual-camera and a monitoring node, and the data acquisition module is deployed on the airborne platform of the UAV; The drone drives within the target area based on the constructed image acquisition route. When the drone drives to the same level as a hovering calibration point, the acquisition module acquires images of the target area based on the dual-camera to obtain a corresponding hovering image, which includes a left-view image and a right-view image. At the same time, the monitoring node acquires relevant information of the corresponding image acquisition process to obtain corresponding attitude data and radiation data. The hovering images, attitude data, and radiation data corresponding to all hovering calibration points are counted to obtain the corresponding area information and upload it to the measurement center.

3. The photogrammetric positioning measurement system based on a double-stitched camera according to claim 2, characterized in that: The data processing module processes the obtained regional information to obtain a corresponding photographic image, which includes: Reading radiation data in the area information, the radiation data including solar radiation energy and solar radiation angle; Based on big data technology, the functional relationship between solar radiation energy and different solar radiation angles is obtained, and the corresponding correction model is constructed based on it; After the construction is completed, the collected radiation data is input into the constructed correction model to obtain the corresponding correction coefficient; Read the collected hover image and obtain the grayscale value of the pixel in the corresponding hover image; Performing grayscale correction on the pixels in the hovering image based on the obtained correction coefficient; After grayscale correction is completed, the flight attitude parameters of the UAV in the corresponding attitude data are read, and based on them, the pixel points in the corresponding hovering image are corrected; After the image correction is completed, the hovering image that has completed the image correction is marked as a high-frequency image, where the high-frequency image includes a left-view high-frequency image and a right-view high-frequency image.

4. The photogrammetric positioning measurement system based on double-stitched cameras according to claim 3, characterized in that: The process of the image analysis module performing image analysis on the obtained high-frequency image includes: Read the high-frequency image corresponding to the corresponding hovering calibration point; Based on the SIFT feature algorithm, feature extraction is performed on the left-view high-frequency image and the right-view high-frequency image in the corresponding high-frequency image to obtain corresponding left-view features and right-view features; Constructing a two-dimensional rectangular coordinate system, and mapping the obtained left-view features and right-view features into the constructed two-dimensional rectangular coordinate system, respectively, to obtain pixel coordinates of the corresponding left-view features and right-view features in the respective high-frequency images; Then, based on the pre-built coordinate mapping model, coordinate mapping is performed on the pixel coordinates corresponding to the corresponding left-view features and right-view features to obtain the corresponding ground mapping coordinates; Obtain the ground mapping coordinates corresponding to the corresponding left-view feature and right-view feature respectively, compare them, and mark the pixel points corresponding to the left-view feature and the right-view feature at the same ground mapping coordinate as the first splicing point; A node evaluation is performed on the obtained first splicing points based on a preset splicing standard, and whether the corresponding first splicing points meet the requirements is determined according to the evaluation result. If not, the corresponding first splicing points are eliminated; if yes, the corresponding first splicing points are retained.

5. The photogrammetric positioning measurement system based on double-stitched cameras according to claim 4, characterized in that: The stitching standard includes that the pixel difference between the pixels corresponding to the corresponding left-view feature and the right-view feature is minimum, and the similarity of the geometric structures within the corresponding pixels satisfies a pre-set similarity threshold.

6. The photogrammetric positioning measurement system based on double-stitched cameras according to claim 4, characterized in that: The process of obtaining the corresponding region fusion image includes: Overlapping the corresponding left-view high-frequency image and the right-view high-frequency image based on the retained first splicing point; After the overlap is completed, obtaining the image intersection point between the corresponding left-view high-frequency image and the right-view high-frequency image; constructing a corresponding image stitching curve based on the image intersection point and in combination with the first stitching point; Stitching the corresponding left-view high-frequency image and the right-view high-frequency image based on the image stitching curve, and fusing the overlapping parts of the left-view high-frequency image and the right-view high-frequency image during the image stitching process; after the image fusion is completed, obtaining the corresponding calibration image; The same method as above is used to process the hovering images corresponding to other hovering calibration points to obtain corresponding calibration images; and the corresponding calibration images are stitched to obtain the regional fusion image corresponding to the corresponding target area.

7. The photogrammetric positioning measurement system based on double-stitched cameras according to claim 6, characterized in that: The corresponding image fusion process includes: Performing image extraction on corresponding overlapping image parts to obtain corresponding left overlapping images and right overlapping images; Obtain the grayscale values ​​corresponding to the pixels of the left and right overlapping images respectively, obtain the deviation values ​​between the pixels in the overlapping state, and mark them as visual deviations; Based on the guided filtering algorithm, the corresponding left overlapping image and right overlapping image are decomposed to obtain the corresponding low-frequency image and high-frequency image; Performing logarithmic processing on the obtained low-frequency component image and high-frequency component image until the processing is completed; fusing the low-frequency image and high-frequency image corresponding to the left overlapping image and the right overlapping image respectively to obtain a corresponding low-frequency fused image and a high-frequency fused image; The corresponding low-frequency fusion image and high-frequency fusion image are superimposed and weighted summed to obtain the corresponding fusion image; the deviation value corresponding to the pixel point in the corresponding fusion image is corrected based on the obtained visual deviation, and once the correction is completed, the image fusion is completed.

8. The photogrammetric positioning measurement system based on double-stitched cameras according to claim 6, characterized in that: The process of the measurement feedback module performing measurement feedback based on the obtained regional fusion image includes: Obtaining pixel coordinates corresponding to corresponding hovering calibration points in the corresponding region fusion image, and obtaining ground mapping coordinates corresponding to the corresponding hovering calibration points based on the coordinate mapping model; Then, the pixel coordinates in the fused image of the corresponding area are adjusted twice based on the ground coordinates corresponding to the corresponding hovering calibration points; After the adjustment is completed, the latitude and longitude in the posture data are read and marked in the corresponding area fusion image to obtain a high-resolution image file with a geographic tag, which is fed back to the measurement center and stored.

Images