Method and System for Rapid Image Transmission of Unmanned Aerial Vehicle and Comparison and Analysis of Historical Images

Abstract

The present invention discloses a method and system for rapid image transmission and comparison and analysis of historical images based on an unmanned aerial vehicle. The method includes: obtaining images of a target area from the unmanned aerial vehicle in real time; registering the current images of the target area to obtain a current panoramic image of the target area; performing aerial triangulation on the current panoramic image of the target area to obtain a current digital elevation model of the target area; correcting the digital elevation model of the current target area to obtain a current digital orthophoto map of the target area; determining a matching area of the current digital orthophoto map of the target area; comparing the current digital orthophoto map with historical images of the matching area of the current digital orthophoto map, and updating the images of the target area in real time. The method of the present invention realizes rapid image transmission of the unmanned aerial vehicle and high-efficiency image stitching, and also realizes accurate matching and real-time update of images.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing, and particularly relates to a method and system for rapid image transmission of an unmanned aerial vehicle and comparison and analysis of historical images. Background Art

[0002] With the continuous development of China's economic level in recent years, the requirements for remote sensing image data in all walks of life have become higher and higher. People have begun to pursue more accurate and timely information. The traditional satellite remote sensing technology that was once widely used has gradually been neglected due to limitations such as altitude and weather conditions. Unmanned aerial vehicles (UAVs) have been used as an auxiliary to satellite remote sensing technology because of their low cost and flexible takeoff and landing.

[0003] The low-altitude remote sensing technology of UAVs is a continuation and supplement of traditional satellite photogrammetry means. The main features are as follows: First, it can fly at low altitude, can fly autonomously and navigate automatically, and is one of the most advanced means in current field geographic information data acquisition technology. Second, the photographic equipment carried has a small volume, and although the volume is small, the images taken have distinct colors and relatively high resolution. In addition to the above advantages, the low-altitude photogrammetry of UAVs also has many advantages such as flexible operation, improved work efficiency, and low accident rate.

[0004] Due to the limited field of view of a single UAV image and the large overlapping area between multiple images, generally, the panoramic stitching of the image sequence needs to be completed first before it can be applied in practice. And because the UAV operates at low altitude, it is easily affected by airflows and wind speeds, which causes deviations in the flight direction and angle of the UAV and unstable flight trajectories. Moreover, the photographic equipment carried on the UAV is a camera without measurement properties, which will cause image distortion during photography, bringing difficulties to subsequent image processing.

[0005] In order to quickly obtain the overall information of the target area and achieve rapid image transmission of the UAV and high-efficiency image stitching, quickly completing the feature extraction and matching between images is the key to improving the UAV image stitching efficiency. Therefore, there is an urgent need for a method and system for rapid image transmission of the UAV and comparison and analysis of historical images to improve the UAV aerial photography image stitching efficiency. Summary of the Invention

[0006] Aiming at the above problems, the present invention provides a method and system for rapid image transmission of the UAV and comparison and analysis of historical images to achieve rapid image transmission of the UAV and high-efficiency image stitching.

[0007] A method for rapid image transmission and comparison analysis of historical images based on drones, comprising the following steps: obtaining images of a target area from a drone in real time; registering the current images of the target area to obtain a current panoramic image of the target area; performing aerial triangulation on the current panoramic image of the target area to obtain a current digital elevation model of the target area; correcting the current digital elevation model of the target area to obtain a current digital orthophoto map of the target area; determining a matching area of the current digital orthophoto map of the target area; comparing the current digital orthophoto map with historical images in the matching area of the current digital orthophoto map, and updating the images of the target area in real time.

[0008] Further, registering the current images of the target area to obtain a current panoramic image of the target area includes:

[0009] Evaluating the quality of the current images of the target area to obtain qualified images of the current target area;

[0010] Preprocessing the qualified images of the current target area;

[0011] Registering the preprocessed images to obtain a panoramic image of the current target area.

[0012] Further, determining a matching area of the current digital orthophoto map of the target area includes:

[0013] Based on the current digital orthophoto map of the target area, finding the nearest neighbor and the second nearest neighbor of the same name points of the sample points. If the ratio of the distance of the nearest neighbor of the same name point to the distance of the second nearest neighbor is less than a set threshold, it indicates that the matching confidence of the nearest neighbor matching point is high, and this point is accepted as a point of the same name, otherwise this matching is not accepted, and a set of paired points of the same name is obtained;

[0014] For the set of paired points of the same name, further screening the matching pairs through reverse matching, removing the matching gross errors, and obtaining an accurate matching model;

[0015] Determining the matching area of the current digital orthophoto map of the target area through the accurate matching model.

[0016] Further, registering the preprocessed images to obtain a panoramic image of the current target area includes:

[0017] Measuring the image overlap degree of the preprocessed images and obtaining the same name points of adjacent images;

[0018] Determining the actual overlap degree according to the coordinate values of the same name points of adjacent images;

[0019] If the horizontal overlap rate of adjacent images meets the first set value and the lateral overlap rate meets the second set value, splicing the adjacent images to obtain a panoramic image of the current target area.

[0020] Further, the first set value is 68% - 75%, and the second set value is 35% - 40%.

[0021] Further, the preprocessing includes color equalization and edge trimming of the image.

[0022] Further, the quality evaluation indicators include horizontal overlap rate, lateral overlap rate, aerial photograph curvature, and rotation angle of the photo.

[0023] Further, the qualified images need to meet the following conditions simultaneously: the horizontal overlap rate is not less than 53%, the lateral overlap rate is not less than 15%, the aerial photograph curvature does not exceed 3%, and the rotation angle control of the photo does not exceed 5%.

[0024] The present invention also provides a system for rapid image transmission and comparison and analysis of historical images based on an unmanned aerial vehicle, including an unmanned aerial vehicle and a data processing unit;

[0025] Among them, the unmanned aerial vehicle is used to obtain images of the target area in real time and send them to the data processing unit;

[0026] The data processing unit is used to register the current images of the target area to obtain the current panoramic image of the target area;

[0027] The data processing unit is also used to perform aerial triangulation on the current panoramic image of the target area to obtain the current digital elevation model of the target area;

[0028] The data processing unit is also used to correct the current digital elevation model of the target area to obtain the current digital orthophoto map of the target area;

[0029] The data processing unit is also used to determine the matching area of the current digital orthophoto map of the target area;

[0030] The data processing unit is also used to compare the current digital orthophoto map with the historical images of the matching area of the current digital orthophoto map to update the images of the target area in real time.

[0031] Further, the data processing unit is specifically used for:

[0032] Evaluate the quality of the images of the current target area to obtain qualified images of the current target area;

[0033] Preprocess the qualified images of the current target area;

[0034] Register the preprocessed images to obtain the panoramic image of the current target area.

[0035] Advantages of the present invention: The method of the present invention can directly screen out qualified images, quickly stitch panoramic images of the target area, and greatly improve the image transmission speed; it realizes the comparison and analysis of new images obtained and processed by the unmanned aerial vehicle with historical images, and realizes the accurate matching and real-time update of images.

[0036] Other features and advantages of the present invention will be described in the following specification, and in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures pointed out in the specification, claims, and drawings. Brief Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 Shows a schematic structural diagram of an unmanned aerial vehicle remote sensing system according to the prior art;

[0039] Figure 2 Shows a schematic flow diagram of a method for rapid image transmission and comparison and analysis of historical images based on an unmanned aerial vehicle according to an embodiment of the present invention. Detailed Embodiments

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to describe the embodiments of the present application here. In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings.

[0042] Such as Figure 1As shown in the figure, the existing UAV remote sensing system includes a ground part, an aerial part, and a data post-processing part.

[0043] Among them, the aerial part includes a remote sensing sensor subsystem, a remote sensing aerial control subsystem, and a UAV platform. The ground part includes a flight path planning subsystem, a UAV ground control subsystem, and a data reception and display subsystem. The main functions of the remote sensing aerial subsystem are: planning flight routes and uploading them to the controller on the aircraft; monitoring the aircraft status during flight and changing some control parameters when data can be reliably transmitted. The main functions of the ground part are to design and plan the flight path of the UAV, real-time control of the UAV, real-time reception of flight attitude data, and display of remote sensing images. The data post-processing part includes preview of image data and post-processing of image data.

[0044] Generally, UAVs can fly in the altitude range of 150 - 2500m, and the flight span area is relatively large.

[0045] The system for rapid image transmission and historical image comparison and analysis based on UAVs in the embodiments of the present invention includes: a UAV and a data processing unit. Among them, the UAV is used to obtain images of the target area in real time and send them to the data processing unit. The data processing unit is used to register the current images of the target area to obtain the current panoramic image of the target area; it is also used to perform aerial triangulation on the current panoramic image of the target area to obtain the current digital elevation model of the target area; it is also used to correct the current digital elevation model of the target area to obtain the current digital orthophoto map of the target area; it is also used to determine the matching area of the current digital orthophoto map of the target area; it is also used to compare the current digital orthophoto map with the historical images of the matching area of the current digital orthophoto map to update the images of the target area in real time.

[0046] The work process of UAV image acquisition operation is as follows: According to the requirements of the remote sensing task, the flight path of the area to be photographed is planned, and the planned flight route is loaded into the remote sensing aerial control subsystem in the ground control subsystem. The UAV ground control subsystem controls the flight of the UAV according to the planned flight route, and the remote sensing aerial control subsystem controls the remote sensing sensor to take pictures according to the preset flight route and shooting method; the remote sensing sensor subsystem stores the taken data, and the UAV platform uses the wireless transmission channel to transmit the flight data to the ground control subsystem; ground staff can monitor the flight route of the UAV on the ground and, if necessary, change the flight plan of this flight according to the received data. For example, they can immediately perform supplementary shooting in some areas; after shooting, it can automatically switch to manual flight and wait for landing.

[0047] Based on the above system for rapid image transmission and historical image comparison and analysis based on UAVs, as Figure 2As shown in the figure, the present invention also provides a method for rapid image transmission and historical image comparison and analysis based on an unmanned aerial vehicle, comprising the following steps:

[0048] S1. Obtain images of the target area from the unmanned aerial vehicle in real time.

[0049] In this step, the unmanned aerial vehicle first conducts a test flight and tests and experiments for different landforms. During the experiment, the first experiment is mainly to verify the imaging ability of the unmanned aerial vehicle remote sensing system; the second experiment needs to take pictures throughout the pre-planned route, and the planned route includes subsystems and data acquisition, transmission, and image storage; the third experiment is to correct the problems found in the previous two experiments.

[0050] When it is determined in the third experimental flight that the problems found in the previous two experiments have been corrected, the unmanned aerial vehicle collects images of the target area.

[0051] S2. Register the current images of the target area to obtain the current panoramic image of the target area, specifically as follows:

[0052] S21. Evaluate the quality of the images of the current target area to obtain qualified images of the current target area.

[0053] The images taken by the remote sensing system of the unmanned aerial vehicle will be affected by factors such as the attitude angle of the aircraft and the accuracy of the control points. Therefore, before image processing, the images are first evaluated for quality.

[0054] In this step, the quality evaluation indicators include the horizontal overlap rate, the lateral overlap rate, the aerial curvature, and the rotation angle of the photo.

[0055] The selection of the above evaluation indicators is mainly based on the following considerations: During the flight, weather factors will also directly affect the quality of aerial images. Therefore, when acquiring data, it is necessary to process according to the horizontal overlap rate and the lateral overlap rate. The curvature of the flight strip will also affect the horizontal overlap rate and the lateral overlap rate. If the curvature exceeds the limit, aerial photography loopholes may occur. When taking pictures, the rotation angle of the flight photo also needs to be considered. This rotation angle is the angle between the connecting line of adjacent principal points on the photo and the connecting line of the inner frame marks in the same direction, usually caused by inaccurate camera orientation during photography. During the navigation process, the flight strip flight route involved cannot be too short, otherwise there will not be enough time reserved for adjusting the attitude and position during navigation, and the lateral track angle is likely to have a large deviation, resulting in a large tilt angle. And the change of the tilt angle will affect the flight route and the taken images, making it difficult to splice and process the flight-taken pictures.

[0056] Specifically, when evaluating the quality of the images of the current target area, the qualified images need to simultaneously meet the following conditions: the horizontal overlap rate is not less than 53%, the lateral overlap rate is not less than 15%, the aerial curvature does not exceed 3%, and the rotation angle of the photo is controlled not to exceed 5% to meet the basic image photography requirements.

[0057] It should be noted that the prior art performs image processing on the images collected by the drone to obtain qualified images, which reduces the efficiency of image transmission. The prior art mainly adopts the following methods:

[0058] First, correct the non-linear distortion of the digital camera lens. Second, correct the graphic rotation and the error between images caused by the change of attitude during flight. When acquiring images, the parameters of the camera can be adjusted through the control points in the field to ensure that single-frame correction can be performed after acquiring the images. Control points are selected for the photographed area through a scale map, and geometric correction is performed on the measurement method of the photograph. Third, orthoimage correction is performed on the original image. Fourth, rely on the navigation system to position the camera.

[0059] In summary, the embodiments of the present invention directly screen out qualified images, which can greatly improve the image transmission speed.

[0060] S22. Preprocess the qualified images of the current target area. The preprocessing includes color homogenization and edge trimming of the images.

[0061] By observing the photos of the images, it can be found that there are significant differences in color and brightness between the aerospace and flight strips. The reason may be the weather or a problem with the shooting camera of the drone remote sensing system during aerial photography. Therefore, color homogenization processing needs to be performed on the original images. When performing color homogenization, attention should be paid to the color contrast, the changes in gray scale and texture, and it is necessary to ensure that the image after color homogenization can have a natural transition. At the same time, the cropping software and system of the image can be used to crop the irrelevant image information in the edge area.

[0062] S23. Register the preprocessed images to obtain a panoramic image of the current target area, including the following steps:

[0063] S231. Measure the image overlap degree of the preprocessed images and obtain the corresponding points of adjacent images.

[0064] In this step, a large number of corresponding points can be obtained when measuring the image overlap degree, and there are approximately more than 400 matching points between adjacent images.

[0065] S232. Determine the actual overlap degree according to the coordinate values of the corresponding points of adjacent images.

[0066] In this step, the requirements in terms of overlap degree calculation and regional average difference need to be met in the overlapping area. After the automatic measurement is completed, the actual overlap degree is determined according to the coordinate values of the corresponding points.

[0067] S233. If the horizontal overlap rate of adjacent images meets the first set value and the lateral overlap rate meets the second set value, splice the adjacent images to obtain a panoramic image of the current target area.

[0068] Among them, the first set value is 68% - 75%, and the second set value is 35% - 40%.

[0069] In this step, the UAV quickly matches according to the data of ground control points to form homologous points and quickly generate a spliced image. The requirements for the quick spliced image are not strict. There may be edge-cutting errors in adjacent areas, and individual scenes may be misaligned during the splicing process. The phenomenon of missed shooting in the area can be analyzed through the spliced image.

[0070] S3. Conduct aerial triangulation on the panoramic image of the current target area to obtain the current digital elevation model of the target area.

[0071] Aerial triangulation is a measurement method in stereophotogrammetry. According to a small number of field control points, control points are encrypted indoors to obtain the elevation and planar position of the encrypted points.

[0072] Digital Elevation Model (DEM) is a digital simulation of the ground terrain through limited terrain elevation data (i.e., digital expression of the terrain surface morphology). It is a kind of solid ground model representing ground elevation in the form of an ordered numerical array and is a digital terrain model.

[0073] The digital elevation model is obtained through photogrammetry based on aerial or space images. For example, it can be obtained through observations with a stereocomparator and aerial triangulation.

[0074] S4. Correct the digital elevation model of the current target area to obtain the current digital orthophoto map of the target area, specifically as follows:

[0075] S41. Based on the digital elevation model of the current target area, by determining the orientation elements, discrete three-dimensional micro-points can appear in the image matching, and the digital orthophoto map to be adjusted is obtained through a human-computer interaction form.

[0076] S42. Randomly select detection control points on the ground of the target area, generate a precision inspection form for the digital orthophoto map according to the detection results, and at the same time number the detection points, record the coordinate difference and coordinate offset. The digital orthophoto map to be adjusted is adjusted for precision according to the recorded coordinate difference and coordinate offset to obtain the current digital orthophoto map of the target area.

[0077] S5. Determine the matching area of the digital orthophoto map of the current target area.

[0078] During the process of regional matching of drones, the operation is mainly carried out by a computer, simulating a person's three-dimensional observation. Finding corresponding points is a relatively crucial procedure in the process of low-altitude remote sensing image processing. During the matching process of low-altitude remote sensing images, due to significant defects at the computational level, the matching accuracy needs to be improved. During the process of information matching, multi-source information matching may occur. Due to the multi-source nature of remote sensing image information itself, it adds a certain degree of difficulty to the matching of low-altitude remote sensing images. In addition to the increased difficulty, the matching speed also needs to be improved. With the development of computers, the data of low-altitude remote sensing images is constantly increasing. Matching mainly uses relevant functional relationships to find the similar structures between them, and calculates the coordinate positions of the images and the transformation of the mapping through mathematical definitions. When matching, it is also necessary to monitor the matching measure of the remote sensing images, which is calculated through functional relationships, correlation coefficients, and covariance functions.

[0079] In this step, the embodiments of the present invention adopt a spatio-temporal correlation matching technology and an improved algorithm for multi-source remote sensing images for matching, and the mean square error can be continuously reduced, which is much smaller than that of the classical algorithm. Image matching can also adopt matching based on the intersection points of linear features to obtain good matching results.

[0080] Specifically, determining the matching area of the digital orthophoto map of the current target area includes:

[0081] S51. Based on the digital orthophoto map of the current target area, using the approximate nearest neighbor matching algorithm, first find the nearest neighbor and the second nearest neighbor corresponding points of the sample points. If the ratio of the distance of the nearest neighbor corresponding point to the distance of the second nearest neighbor is less than the set threshold, it indicates that the matching confidence of the nearest neighbor matching point is high, and accept this point as the corresponding point; otherwise, do not accept this matching, and obtain a set of corresponding point pairs.

[0082] S52. For the set of corresponding point pairs, further screen the matching pairs through reverse matching, eliminate the gross matching errors, and obtain an accurate matching model.

[0083] S53. Through the accurate matching model, determine the matching area of the digital orthophoto map of the current target area.

[0084] In this step, the strategy of pyramid matching can be used to transform the image into a pyramid structure. Starting from the bottom layer, new pixels are formed after resampling between adjacent pixels until the top of the pyramid is formed. The matching strategy also includes global optimal matching, which considers from a global perspective and matches based on gray levels and image features. In addition, there is also matching with multiple constraint conditions, including compatibility constraints, uniqueness constraints, epipolar constraints, similarity constraints, and multi-primitive matching strategies.

[0085] S6. Compare the current digital orthophoto map with the historical images in the matching area of the current digital orthophoto map, and update the images of the target area in real time.

[0086] In this step, the new images acquired and processed by the UAV are compared and analyzed with the historical images to achieve accurate matching and real-time update of the images.

[0087] It should be noted that the historical images in this embodiment can be the existing images of the target area before, or the images of the target area updated in real time in the previous round through this embodiment.

[0088] The embodiment of the present invention utilizes technical advantages such as advanced unmanned aerial vehicle technology and remote sensing sensor technology to achieve automation, intelligence, and specialization, and collect valuable data for aerial survey work.

[0089] Quickly completing the feature extraction and matching between images is the key to improving the UAV image stitching efficiency.

[0090] The method for rapid UAV image transmission back and comparison and analysis with historical images according to the embodiment of the present invention is to quickly obtain the overall information of the target area, realize rapid UAV image transmission back and high-efficiency image stitching. The calculation of the image overlap degree and the automatic measurement of homologous points are carried out to determine the actual overlap degree of adjacent images, and rapid matching is carried out according to the data of ground control points to form a rapid generation of a stitching map of homologous points, and then approximate nearest neighbor matching is carried out, and finally accurate matching is carried out. Compared with the feature extraction and matching in the traditional UAV image stitching algorithm, this algorithm has a greater improvement in the extraction speed and matching speed of features, and also has an improvement in the stability and distinctiveness of features.

[0091] Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid image transmission and comparison analysis of historical images based on an unmanned aerial vehicle, comprising the following steps: Obtain images of the target area from the unmanned aerial vehicle in real time; Register the current images of the target area to obtain the current panoramic image of the target area, including: evaluate the quality of the images of the current target area to obtain qualified images of the current target area; Preprocess the qualified images of the current target area; Register the preprocessed images to obtain the panoramic image of the current target area; Among them, the quality evaluation indicators include the horizontal overlap rate, the lateral overlap rate, the aerial curvature, and the rotation angle of the photo; qualified images need to meet simultaneously: the horizontal overlap rate is not less than 53%, the lateral overlap rate is not less than 15%, the aerial curvature does not exceed 3%, and the rotation angle of the photo is controlled not to exceed 5%; Perform aerial triangulation on the current panoramic image of the target area to obtain the current digital elevation model of the target area; Correct the digital elevation model of the current target area to obtain the current digital orthophoto map of the target area; Determine the matching area of the current digital orthophoto map of the target area, including: based on the current digital orthophoto map of the target area, find the nearest neighbor and the second nearest neighbor homologous points of the sample points. If the ratio of the distance of the nearest neighbor homologous point to the distance of the second nearest neighbor is less than the set threshold, it indicates that the matching confidence of the nearest neighbor matching point is high, accept this point as the homologous point, otherwise do not accept this match, and obtain a set of homologous point pairs; For the set of homologous point pairs, further screen the matching pairs through reverse matching, eliminate the matching gross errors, and obtain an accurate matching model; Determine the matching area of the current digital orthophoto map of the target area through the accurate matching model; Compare the current digital orthophoto map with the historical images of the matching area of the current digital orthophoto map, and update the images of the target area in real time.

2. The method for rapid image transmission and historical image comparison and analysis based on an unmanned aerial vehicle according to claim 1, wherein, Register the preprocessed images to obtain the panoramic image of the current target area, including: Measure the image overlap of the preprocessed images and obtain the homologous points of adjacent images; Determine the actual overlap according to the coordinate values of the homologous points of adjacent images; If the horizontal overlap rate of adjacent images meets the first set value and the lateral overlap rate meets the second set value, splice the adjacent images to obtain the panoramic image of the current target area.

3. The method for rapid image transmission and historical image comparison and analysis based on an unmanned aerial vehicle according to claim 2, wherein, The first set value is 68% - 75%, and the second set value is 35% - 40%.

4. The method for rapid image transmission and historical image comparison and analysis based on an unmanned aerial vehicle according to claim 1, wherein, The preprocessing includes color homogenization and edge trimming of the images.

5. A system for rapid image transmission and comparison analysis of historical images based on an unmanned aerial vehicle, comprising an unmanned aerial vehicle and a data processing unit; Among them, The unmanned aerial vehicle is used to obtain images of the target area in real time and send them to the data processing unit; The data processing unit is used to register the current images of the target area to obtain the current panoramic image of the target area, including: evaluate the quality of the images of the current target area to obtain qualified images of the current target area; Preprocess the qualified images of the current target area; Register the preprocessed images to obtain the panoramic image of the current target area; Among them, the quality evaluation indicators include the horizontal overlap rate, the lateral overlap rate, the aerial photograph curvature, and the rotation angle of the photograph; the qualified images need to meet the following conditions simultaneously: the horizontal overlap rate is not less than 53%, the lateral overlap rate is not less than 15%, the aerial photograph curvature does not exceed 3%, and the rotation angle control of the photograph does not exceed 5%; The data processing unit is also used to perform aerial triangulation on the panoramic image of the current target area to obtain the current digital elevation model of the target area; The data processing unit is also used to correct the digital elevation model of the current target area to obtain the current digital orthophoto map of the target area; The data processing unit is also used to determine the matching area of the digital orthophoto map of the current target area, including: based on the digital orthophoto map of the current target area, finding the nearest neighbor and the second nearest neighbor homologous points of the sample points. If the ratio of the distance between the nearest homologous point and the distance between the second nearest neighbor is less than the set threshold, it indicates that the matching confidence of the nearest neighbor matching point is high, and this point is accepted as the homologous point; otherwise, this matching is not accepted, and a set of homologous point pairs is obtained; For the set of homologous point pairs, the matching pairs are further screened through reverse matching to eliminate the matching gross errors and obtain an accurate matching model; Through the accurate matching model, the matching area of the digital orthophoto map of the current target area is determined; The data processing unit is also used to compare the current digital orthophoto map with the historical images of the matching area of the current digital orthophoto map to update the images of the target area in real time.

Images