A mapping image processing method based on UAV remote sensing

Abstract

The present invention discloses a method for processing mapping images based on UAV remote sensing, which relates to the technical field of map surveying and mapping. Before formal surveying and mapping, a number of reference areas are set within the target surveying and mapping area, and through the test flight of the UAV, the test flight surveying and mapping information of the reference areas obtained under different flight postures of the UAV is acquired. Then, the acquired test flight surveying and mapping information is compared with the actual location information of the reference areas, so as to obtain the surveying and mapping errors existing under different flight postures of the UAV. The surveying and mapping processes of different flight postures of the UAV are corrected through the obtained surveying and mapping errors, thereby reducing the surveying and mapping errors that may be caused under various flight postures of the UAV and improving the accuracy of UAV remote sensing surveying and mapping.

Description

Technical Field

[0001] The present invention relates to the technical field of map surveying and mapping, and specifically to a method for processing surveying and mapping images based on unmanned aerial vehicle (UAV) remote sensing. Background Art

[0002] As a new surveying and mapping method, UAV remote sensing technology is gradually becoming an important tool for obtaining geospatial information. Traditional surveying and mapping methods, such as ground surveying, aerial photogrammetry, etc., have certain limitations in practical applications. UAV remote sensing technology effectively makes up for the deficiencies of traditional surveying and mapping methods with its advantages of flexibility, convenience, low cost, and fast response speed. UAVs can fly at low altitudes, are not overly restricted by complex terrains and weather conditions, and can quickly and accurately obtain high-resolution remote sensing images.

[0003] When using UAV remote sensing technology for surveying and mapping, the data obtained under different flight postures of the UAV often has certain deviations, resulting in a decrease in surveying and mapping accuracy. To reduce the surveying and mapping deviations caused by the UAV flight posture, a method for processing surveying and mapping images based on UAV remote sensing is provided herein. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for processing surveying and mapping images based on UAV remote sensing.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A method for processing surveying and mapping images based on UAV remote sensing, including:

[0006] Construct a plane coordinate system for the target surveying and mapping area, and set a reference area within the target surveying and mapping area;

[0007] Set a test flight route, obtain test flight surveying and mapping information of each reference area through the UAV on the test flight route, and calibrate the surveying and mapping accuracy of the UAV through the obtained test flight surveying and mapping information;

[0008] Set a surveying and mapping flight route for the UAV after completing the surveying and mapping accuracy calibration, obtain real-time surveying and mapping information through the UAV on the surveying and mapping flight route, and process the obtained real-time surveying and mapping information to obtain a registered image and surveying and mapping parameters of the target surveying and mapping area, where the real-time surveying and mapping information includes real-time image information and real-time remote sensing information;

[0009] Based on the obtained registered image, fill simulation elements in the plane coordinate system, configure the layers of the filled simulation elements, and combine the surveying and mapping parameters to obtain a regional surveying and mapping model of the target surveying and mapping area.

[0010] Further, the process of constructing a plane coordinate system for the target surveying and mapping area and setting a reference area within the target surveying and mapping area is as follows:

[0011] Select a target surveying and mapping area, and construct a plane coordinate system based on the selected target surveying and mapping area;

[0012] Set several reference areas within the target surveying and mapping area, obtain the relative positions between the reference areas, generate corresponding virtual reference areas in the plane coordinate system according to the relative positions between the reference areas, and obtain the coordinate ranges of the virtual reference areas in the plane coordinate system, denoted as reference coordinate ranges.

[0013] Furthermore, the process of obtaining the flight test surveying and mapping information of each reference area is as follows:

[0014] Set a flight test route, set surveying and mapping areas on the flight test route, and associate each surveying and mapping area with a reference area;

[0015] Preset several flight postures of the unmanned aerial vehicle (UAV), and record the flight posture of the UAV when it reaches the surveying and mapping area as the initial posture;

[0016] Based on the initial posture, change the posture of the UAV in the surveying and mapping area according to the preset flight postures of the UAV, and obtain the flight test surveying and mapping information of the reference area in different flight postures. The flight test surveying and mapping information includes flight test image information and flight test remote sensing information.

[0017] Furthermore, the process of calibrating the surveying and mapping accuracy of the UAV through the obtained flight test surveying and mapping information is as follows:

[0018] Intercept the flight test image information obtained in the same flight posture, convert it into corresponding image frames, rasterize each image frame, and then obtain the corresponding grayscale image;

[0019] Extract the characteristic values of each grayscale image regarding the area where the reference area is located, and overlap the grayscale images to obtain the characteristic fusion area of the area where the reference area is located;

[0020] Obtain the flight test remote sensing information obtained in the same flight posture, obtain the relative position between the reference area and the UAV according to the obtained flight test remote sensing information and the current position of the UAV, so as to obtain the flight test surveying and mapping coordinates of the reference area in the current flight posture;

[0021] Map the obtained characteristic fusion area to the corresponding position in the plane coordinate system according to the obtained flight test surveying and mapping coordinates;

[0022] Compare the characteristic fusion area with the reference coordinate range to obtain the coordinate offset;

[0023] Associate the obtained coordinate offset with the flight attitude, summarize the coordinate offsets of the same flight attitude in each reference area, and obtain the corresponding average value as the mapping calibration value of this flight attitude.

[0024] Further, convert the obtained real-time image information into image frames, and input the converted image frames into the trained neural network model for feature extraction to obtain various image features within the image frames.

[0025] Determine whether any of the extracted image features in each image frame belong to a reference area, so as to obtain captured image frames and off-screen image frames.

[0026] Summarize the captured image frames containing the image features corresponding to the same reference area to obtain an image frame set associated with this reference area.

[0027] Traverse the image features extracted from each captured image frame in the image frame set, and mark the same image features.

[0028] For the relative position relationship between the marked same image features and the features corresponding to the reference area in each image frame, if the relative position relationship remains unchanged, it indicates that the corresponding image feature is a solid feature, otherwise it is a dynamic feature, and all dynamic features are removed.

[0029] Fit each image frame and perform feature fusion on the same image features in the fitted image frames to obtain a registered image associated with this reference area.

[0030] Further, obtain the relative position distances between each solid feature in the registered image and the reference area. Taking the reference area as the center, select the solid features with the farthest relative position distances at both ends along the mapping flight route and denote them as off-screen reference features.

[0031] Determine whether there are image features in the off-screen image frames that are the same as the off-screen reference features. If so, summarize all the off-screen image frames with off-screen reference features, and obtain the relative position distances between each image feature in the summarized off-screen image frames and the off-screen reference features. Determine the solid features and dynamic features in the off-screen image frames according to the relative position distances, remove the dynamic features, and then fit the summarized off-screen image frames with the registered image to obtain a new registered image.

[0032] Select new off-screen reference features from the new registered image and process the remaining off-screen image frames, and so on.

[0033] Map the registered image associated with the reference area to the corresponding position in the plane coordinate system.

[0034] Further, based on the flight attitude of the drone during flight, obtain the corresponding mapping calibration value, and calibrate the obtained real-time remote sensing information based on the mapping calibration value;

[0035] According to the calibrated real-time remote sensing information, obtain the position information corresponding to each image feature;

[0036] Obtain the relative position with the benchmark reference area closest in distance according to the position information of each image feature;

[0037] Compare the position information of the benchmark reference area obtained according to the calibrated real-time remote sensing information with the reference coordinate range of the corresponding benchmark reference area, and obtain the secondary mapping calibration value according to the comparison result;

[0038] Calibrate the position information of each image feature according to the secondary mapping calibration value and the relative position of each image feature's benchmark reference area;

[0039] Map the position information of each calibrated image feature to each image feature in the plane coordinate system, and update the position and coordinate information of each image feature in the plane coordinate system.

[0040] Further, the process of obtaining the regional mapping model of the target mapping area includes:

[0041] Construct a feature - element association database, where the feature - element association database includes different image features, and each image feature is associated with a simulation element;

[0042] Traverse each image feature in the plane coordinate system, index the corresponding simulation element in the feature - element association database according to the traversed image feature, and replace the simulation element with the corresponding image feature in the plane coordinate system;

[0043] Read the coordinate information of each simulation element, and obtain whether there is coordinate overlap between each simulation element and other simulation elements;

[0044] If there is no coordinate overlap with other simulation elements, mark this simulation element as a single - layer element. If there is coordinate overlap with other simulation elements, mark this simulation element as a composite - layer element;

[0045] Obtain the coordinate information corresponding to each simulation element in the composite - layer element, compare the coordinate information of each simulation element, sort the coordinate overlap parts of the simulation elements according to the comparison result, sort the simulation elements according to the sorting result, generate an element layer corresponding to each simulation element according to the sorting result, and fit the corresponding positions of the element layer in the plane coordinate system to obtain the composite layer.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] Before formal surveying and mapping, several reference areas are set within the target surveying and mapping area, and through the test flight of the unmanned aerial vehicle (UAV), the test flight surveying and mapping information of the reference areas obtained in different flight postures of the UAV is acquired. Then, the acquired test flight surveying and mapping information is compared with the actual location information of the reference areas, so as to obtain the surveying and mapping errors existing in different flight postures of the UAV. The surveying and mapping process of different flight postures of the UAV is corrected through the obtained surveying and mapping errors, thereby reducing the possible surveying and mapping errors caused by the UAV in various flight postures and improving the accuracy of UAV remote sensing surveying and mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0049] Figure 1 is the method flow chart of the present invention;

[0050] Figure 2 is the test flight surveying and mapping flow chart of the present invention;

[0051] Figure 3 is the actual surveying and mapping flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] As Figure 1 shown, a method for processing surveying and mapping images based on UAV remote sensing includes:

[0053] Construct a plane coordinate system for the target surveying and mapping area, and set reference areas within the target surveying and mapping area. The specific process is as follows:

[0054] Select the target surveying and mapping area, and construct a plane coordinate system based on the selected target surveying and mapping area;

[0055] Set several reference areas within the target surveying and mapping area, and obtain the relative positions between the reference areas. According to the relative positions between the reference areas, generate corresponding virtual reference areas within the plane coordinate system, and obtain the coordinate ranges of the virtual reference areas within the plane coordinate system, denoted as reference coordinate ranges.

[0056] As Figure 2As shown, in another embodiment of the present invention, flight tests and mapping will be carried out on the unmanned aerial vehicle (UAV), that is: by setting a flight test route, the UAV obtains flight test mapping information of each reference area on the flight test route, and calibrates the mapping accuracy of the UAV through the obtained flight test mapping information. The specific process is as follows:

[0057] Set a flight test route that can pass through each reference area in sequence without repetition;

[0058] Set mapping areas on the flight test route, and each mapping area is associated with a reference area;

[0059] Preset several flight postures of the UAV, and record the flight posture of the UAV when it reaches the mapping area as the initial posture;

[0060] Based on the initial posture, change the posture of the UAV according to the preset flight postures of the UAV within the mapping area, and obtain the flight test mapping information of the reference area in different flight postures. The flight test mapping information includes flight test image information and flight test remote sensing information;

[0061] Intercept the flight test image information obtained in the same flight posture, convert it into corresponding image frames, rasterize each image frame, and then obtain the corresponding grayscale image;

[0062] Extract the characteristic values of each grayscale image regarding the area where the reference area is located, and overlap each grayscale image to obtain the characteristic fusion area of the area where the reference area is located;

[0063] Obtain the flight test remote sensing information obtained in the same flight posture, and obtain the relative position between the reference area and the UAV according to the obtained flight test remote sensing information and the current location of the UAV, so as to obtain the flight test mapping coordinates of the reference area in the current flight posture;

[0064] Map the obtained characteristic fusion area to the corresponding position in the plane coordinate system according to the obtained flight test mapping coordinates;

[0065] Compare the characteristic fusion area with the reference coordinate range to obtain the coordinate offset;

[0066] Associate the obtained coordinate offset with this flight posture, and so on, to obtain the coordinate offsets of each flight posture within the mapping area of this reference area;

[0067] Summarize the coordinate offsets of the same flight posture of each reference area to obtain the corresponding mean value as the mapping calibration value of this flight posture. It should be noted that in the specific implementation process, the flight process of the UAV usually maintains a fixed flight height.

[0068] As Figure 3 shown, in another embodiment of the present invention, during actual surveying and mapping, a surveying and mapping flight route is set for the unmanned aerial vehicle (UAV) that has completed surveying and mapping accuracy calibration, and real-time surveying and mapping information is obtained through the UAV along the surveying and mapping flight route. The obtained real-time surveying and mapping information is processed to obtain a registered image and surveying and mapping parameters of the target surveying and mapping area. The specific process is as follows:

[0069] The real-time surveying and mapping information includes real-time image information and real-time remote sensing information;

[0070] The obtained real-time image information is converted into image frames, and the converted image frames are input into a trained neural network model for feature extraction to obtain various image features within the image frames;

[0071] It is determined whether any of the extracted image features within each image frame is a reference reference area;

[0072] If an image frame contains image features corresponding to any reference reference area, the image frame is marked as a captured image frame, and the remaining image frames that do not contain image features corresponding to any reference reference area are recorded as out-of-frame image frames;

[0073] The image frames containing image features corresponding to the same reference reference area are summarized to obtain an image frame set associated with the reference reference area;

[0074] Each image frame within the image frame set is traversed, and the same image features are marked;

[0075] The relative position relationship between the marked same image feature and the feature corresponding to the reference reference area within each image frame is determined. If the relative position relationship remains unchanged, it indicates that the corresponding image feature is a solid feature, otherwise it is a dynamic feature, and all dynamic features are removed;

[0076] Each image frame is fitted, and feature fusion is performed on the same image feature within the fitted image frame to obtain a registered image associated with the reference reference area;

[0077] The relative position distance between each solid feature within the registered image and the reference reference area is obtained. With the reference reference area as the center, the solid feature with the farthest relative position distance at both ends along the surveying and mapping flight route is selected and recorded as an out-of-frame reference feature;

[0078] Obtain whether there is an image feature identical to the off-picture comparison feature in the off-picture image frame; if so, aggregate all off-picture image frames with the off-picture comparison feature, and obtain the relative position distance between each image feature in the aggregated off-picture image frame and the off-picture comparison feature; determine the solid features and dynamic features in the off-picture image frame according to the relative position distance, remove the dynamic features, and then fit the aggregated off-picture image frame with the registration image to obtain a new registration image;

[0079] Then, new off-screen comparison features are selected from the new registered image, and the remaining off-screen image frames are processed, and so on;

[0080] The obtained registered image associated with the fiducial reference region is mapped to a corresponding position in the plane coordinate system.

[0081] In another embodiment of the present invention, based on the flight attitude of the UAV during the flight, a corresponding surveying and mapping calibration value is obtained, and the obtained real-time remote sensing information is calibrated based on the surveying and mapping calibration value;

[0082] According to the calibrated real-time remote sensing information, the position information corresponding to each image feature is obtained;

[0083] Obtaining a relative position to the nearest benchmark reference area according to the position information of each image feature;

[0084] Compare the position information of the benchmark reference area obtained according to the real-time remote sensing information after calibration with the reference coordinate range of the corresponding benchmark reference area, and obtain the secondary surveying and mapping calibration value according to the comparison result;

[0085] Calibrate the position information of each image feature according to the secondary surveying and mapping calibration value and the relative position of the benchmark reference area of each image feature;

[0086] The position information of each calibrated image feature is mapped to each image feature in the plane coordinate system, and the position and coordinate information of each image feature in the plane coordinate system is updated.

[0087] Based on the obtained registration image, simulation elements are filled in the plane coordinate system, and the filled simulation elements are configured in layers. The regional mapping model of the target mapping area is obtained in combination with the mapping parameters. The specific process includes:

[0088] Constructing a feature-element association database, wherein the feature-element association database includes different image features, each image feature is associated with a simulation element;

[0089] Traverse each image feature in the plane coordinate system. According to the traversed image features, index the corresponding simulation elements in the feature-element association database, map the simulation elements to the plane coordinate system, and replace the corresponding image features in the plane coordinate system;

[0090] After completing the replacement of all image features, read the coordinate information of each simulation element, and determine whether there is coordinate overlap between each simulation element and other simulation elements according to the coordinate information of each simulation element;

[0091] If there is no coordinate overlap with other simulation elements, mark the simulation element as a single-layer element. If there is coordinate overlap with other simulation elements, mark the simulation element as a composite-layer element;

[0092] Obtain the coordinate information corresponding to each simulation element in the composite-layer element, compare the coordinate information of each simulation element, sort the coordinate overlapping parts of the simulation elements according to the comparison results, sort the simulation elements according to the sorting results, generate an element layer corresponding to each simulation element according to the sorting results, and fit the corresponding positions of the element layer in the plane coordinate system to obtain a composite layer, and so on until the layer configuration of all image features is completed, thereby completing the construction of the regional mapping model.

[0093] In another embodiment of the present invention, after completing the layer configuration of all image features, associate the positions and coordinate information of each image feature in the latest plane coordinate system with the corresponding layers to generate information index items. The user can complete the retrieval of the layer and coordinate information corresponding to the information index item by clicking on each information index item.

[0094] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Although the present invention has been disclosed as above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to obtain an equivalent embodiment with equivalent changes. However, as long as the technical content of the present invention is not departed from, any modification or equivalent replacement made to the above embodiments based on the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A mapping image processing method based on drone remote sensing, characterized in that Including: Construct a planar coordinate system for the target surveying and mapping area, and set a reference area within the target surveying and mapping area; Set a test flight route, obtain the test flight surveying and mapping information of each reference area through the UAV on the test flight route, and calibrate the surveying and mapping accuracy of the UAV through the obtained test flight surveying and mapping information; Set a surveying and mapping flight route for the UAV after the surveying and mapping accuracy calibration is completed, obtain real-time surveying and mapping information through the UAV on the surveying and mapping flight route, and process the obtained real-time surveying and mapping information to obtain a registered image and surveying and mapping parameters of the target surveying and mapping area. The real-time surveying and mapping information includes real-time image information and real-time remote sensing information; Based on the obtained registered image, fill simulation elements in the planar coordinate system, configure the layers of the filled simulation elements, and combine the surveying and mapping parameters to obtain a regional surveying and mapping model of the target surveying and mapping area.

2. The method for processing mapping images based on UAV remote sensing according to claim 1, wherein, The process of constructing a planar coordinate system for the target surveying and mapping area and setting a reference area within the target surveying and mapping area is as follows: Select the target surveying and mapping area, and construct a planar coordinate system based on the selected target surveying and mapping area; Set several reference areas within the target surveying and mapping area, obtain the relative positions between the reference areas, and generate corresponding virtual reference areas in the planar coordinate system according to the relative positions between the reference areas, and obtain the coordinate ranges of the virtual reference areas in the planar coordinate system, denoted as the reference coordinate ranges.

3. The method for processing mapping images based on UAV remote sensing according to claim 2, characterized in that, The process of obtaining the test flight surveying and mapping information of each reference area is as follows: Set a test flight route, set a surveying and mapping area on the test flight route, and each surveying and mapping area is associated with a reference area; Preset several UAV flight postures, and record the flight posture of the UAV when it reaches the surveying and mapping area as the initial posture; Based on the initial posture, change the posture of the UAV in the surveying and mapping area according to the preset UAV flight postures, and obtain the test flight surveying and mapping information of the reference area in different flight postures. The test flight surveying and mapping information includes test flight image information and test flight remote sensing information.

4. A mapping image processing method based on UAV remote sensing according to claim 3, characterized in that, The process of calibrating the surveying and mapping accuracy of the UAV through the obtained test flight surveying and mapping information is as follows: Intercept the test flight image information obtained in the same flight posture, convert it into corresponding image frames, rasterize each image frame, and then obtain the corresponding grayscale image; Extract the characteristic values of each grayscale image regarding the area where the reference area is located, and overlap the grayscale images to obtain a characteristic fusion area of the area where the reference area is located; Obtain the test flight remote sensing information obtained in the same flight posture, and obtain the relative position between the reference area and the UAV according to the obtained test flight remote sensing information and the current position of the UAV, so as to obtain the test flight surveying and mapping coordinates of the reference area in the current flight posture; Map the obtained characteristic fusion area to the corresponding position in the planar coordinate system according to the obtained test flight surveying and mapping coordinates; Compare the characteristic fusion area with the reference coordinate range to obtain a coordinate offset; Associate the obtained coordinate offset with the flight posture, and summarize the coordinate offsets of the same flight posture of each reference area to obtain the corresponding mean value as the surveying and mapping calibration value of the flight posture.

5. The method for processing mapping images based on UAV remote sensing according to claim 4, characterized in that, Convert the obtained real-time image information into image frames, and input the converted image frames into the trained neural network model for feature extraction to obtain various image features within the image frames; Obtain whether any of the extracted image features within each image frame is a reference reference area, so as to obtain captured image frames and off-screen image frames; Summarize the captured image frames containing the image features corresponding to the same reference reference area to obtain an image frame set associated with the reference reference area; Traverse the image features extracted within each captured image frame in the image frame set, and mark the same image features; For the relative position relationship between the marked same image feature and the feature corresponding to the reference reference area within each image frame, if the relative position relationship remains unchanged, it indicates that the corresponding image feature is a solid feature, otherwise it is a dynamic feature, and all dynamic features are removed; Fit each image frame, and perform feature fusion on the same image feature within the fitted image frame to obtain a registered image associated with the reference reference area; 6. The mapping image processing method based on UAV remote sensing according to claim 5, characterized in that, Obtain the relative position distance between each solid feature in the registered image and the reference reference area. Taking the reference reference area as the center, select the solid feature with the farthest relative position distance at both ends along the mapping flight route, and record it as the off-screen comparison feature; Obtain whether there is an image feature in the off-screen image frame that is the same as the off-screen comparison feature. If so, summarize all the off-screen image frames with the off-screen comparison feature, and obtain the relative position distance between each image feature in the summarized off-screen image frames and the off-screen comparison feature. Determine the solid features and dynamic features in the off-screen image frames according to the relative position distance, remove the dynamic features, and then fit the summarized off-screen image frames with the registered image to obtain a new registered image; Select a new off-screen comparison feature from the new registered image and process the remaining off-screen image frames, and so on; Map the registered image associated with the reference reference area obtained to the corresponding position in the plane coordinate system; 7. A mapping image processing method based on UAV remote sensing according to claim 6, characterized in that, Based on the flight attitude of the drone during flight, obtain the corresponding mapping calibration value, and calibrate the obtained real-time remote sensing information based on the mapping calibration value; According to the calibrated real-time remote sensing information, obtain the position information corresponding to each image feature; Obtain the relative position with the reference reference area closest in distance according to the position information of each image feature; Compare the position information of the reference reference area obtained according to the calibrated real-time remote sensing information with the reference coordinate range of the corresponding reference reference area, and obtain a secondary mapping calibration value according to the comparison result; Calibrate the position information of each image feature according to the secondary mapping calibration value and the relative position of each image feature with the reference reference area; Map the calibrated position information of each image feature to each image feature in the plane coordinate system, and update the position and coordinate information of each image feature in the plane coordinate system; 8. A mapping image processing method based on drone remote sensing according to claim 7, characterized in that The process of obtaining the regional mapping model of the target mapping area includes: Construct a feature-element association database, and the feature-element association database includes different image features, and each image feature is associated with a simulation element; Traverse each image feature in the plane coordinate system. According to the traversed image features, index the corresponding simulation elements in the feature - element association database, and replace the simulation elements with the corresponding image features in the plane coordinate system; Read the coordinate information of each simulation element to obtain whether there is coordinate overlap between each simulation element and other simulation elements; If there is no coordinate overlap with other simulation elements, mark the simulation element as a single - layer element. If there is coordinate overlap with other simulation elements, mark the simulation element as a composite - layer element; Obtain the coordinate information corresponding to each simulation element in the composite - layer element, compare the coordinate information of each simulation element, sort the coordinate overlap parts of the simulation elements according to the comparison results, sort the simulation elements according to the sorting results, generate an element layer corresponding to each simulation element according to the sorting results, and fit the corresponding positions of the element layer in the plane coordinate system to obtain a composite layer.

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