An urban topographic mapping method based on image analysis

Abstract

The present application relates to the technical field of image processing, and more particularly to a city terrain mapping method based on image analysis, comprising: a UAV collects terrain images and point cloud data, the terrain images are subjected to radiation correction, geometric correction and feature extraction to obtain multi-dimensional terrain features, the point cloud data is subjected to cleaning, denoising and standardization to serve as standardized point cloud data; a three-dimensional network framework is constructed by combining the two and TIN, image texture is optimized to a city terrain three-dimensional model, and terrain mapping is completed accordingly; the mapping accuracy is determined by the accuracy rate of multi-dimensional terrain feature extraction, if it is not up to standard, it is determined whether to increase the ground point classification height threshold; if it is not necessary to increase, the image acquisition integrity is determined by the building shadow coverage rate of the terrain images, if it is not up to standard, it is determined whether to increase the terrain feature texture roughness coefficient; if it is not necessary to increase, the TIN modeling triangular net edge number correlation redundancy rate is determined based on the image frame loss rate. The present application improves the mapping accuracy of city terrain.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an urban topographic mapping method based on image analysis. Background Technology

[0002] In existing technologies, UAV aerial surveying is widely used in topographic mapping. However, the complex urban environment easily leads to problems such as building shadows and occlusion. Furthermore, these methods often focus on optimizing a single aspect, relying on image processing algorithms to correct backend errors. They lack a quantitative correlation mechanism between data acquisition integrity, transmission reliability, and modeling accuracy, and lack practical dynamic parameter adjustment schemes. Simultaneously, traditional methods do not set clear threshold criteria, making the identification of surveying anomalies highly subjective and hindering the formation of a closed-loop optimization system encompassing data acquisition, transmission, and modeling. This results in insufficient accuracy in urban topographic mapping.

[0003] Chinese Patent Publication No. CN117354054A discloses a method and system for transmitting geological mapping data from an unmanned aerial vehicle (UAV). The system includes: a tiered data determination end, which determines the mapping data generated during the UAV geological mapping process; a format partitioning and marking end, which partitions and integrates the image data and parameter data within the mapping data, confirms the parameter data packet and image data packet for this stage, and sets markings at the partition nodes; a data packet encryption end, which encrypts the parameter data packet as a whole using an intermittent compression and hiding method, and transmits the encrypted parameter data packet to the transmission parameter confirmation end; an image packet encryption end, which formulates an encryption template based on the specific number of images in a single group within the image data packet, and encrypts the single group of images within the image packet according to the encryption template; and a transmission parameter confirmation end, which extracts the transmission frequency parameters and corresponding packet loss rates from the database, analyzes and confirms the packet loss rate generated by the UAV under different transmission frequency parameter states, thereby locking in the optimal transmission frequency. Therefore, it can be seen that the UAV geological mapping data transmission method and system have the following problems: relying solely on a single image processing algorithm to correct errors, failing to avoid the risk of data loss from the transmission source, lacking a response to frame loss caused by complex urban electromagnetic environments or link blockages, and failing to build a dynamically adjustable linkage verification mechanism, resulting in insufficient accuracy in urban terrain mapping. Summary of the Invention

[0004] Therefore, this invention provides an image analysis-based urban terrain mapping method to overcome the problems of insufficient accuracy in urban terrain mapping caused by relying solely on a single image processing algorithm to correct errors, failing to avoid the risk of data loss from the transmission source, lacking a response to frame loss caused by complex urban electromagnetic environments or link obstruction, and failing to construct a dynamically adjustable linkage verification mechanism.

[0005] To achieve the above objectives, the present invention provides an urban topographic mapping method based on image analysis, comprising:

[0006] The terrain images and point cloud data collected by the UAV are acquired, and the terrain images are subjected to radiometric correction, geometric correction and feature extraction in sequence to obtain multi-dimensional terrain features. The point cloud data is subjected to cleaning, noise reduction and standardization processing in sequence to obtain standardized point cloud data.

[0007] A three-dimensional network framework is constructed based on the multi-dimensional terrain features and the standardized point cloud data, combined with TIN. The image texture in the multi-dimensional terrain features is mapped to the three-dimensional network framework for optimization to obtain a three-dimensional urban terrain model. The urban terrain is then surveyed based on the three-dimensional urban terrain model to obtain a surveyed image.

[0008] The extraction accuracy of multi-dimensional terrain features is obtained, and the accuracy of urban terrain mapping is determined based on the extraction accuracy of the multi-dimensional terrain features.

[0009] If the accuracy of the urban terrain mapping does not meet the requirements, it is determined whether it is necessary to increase the classification height threshold of ground points in the terrain image;

[0010] If it is not necessary to increase the classification height threshold of ground points in the terrain image, then obtain the building shadow coverage of the terrain image to determine whether the acquisition integrity of the terrain image meets the requirements;

[0011] If the completeness of the acquired terrain image does not meet the requirements, then determine whether it is necessary to increase the multi-dimensional terrain feature texture roughness coefficient.

[0012] If it is not necessary to increase the texture roughness coefficient of multi-dimensional terrain features, the edge number and associated redundancy rate of TIN modeling triangular mesh are determined based on the image frame loss rate of the terrain image.

[0013] Furthermore, determining whether the accuracy of urban terrain mapping meets the requirements based on the extraction accuracy of the multi-dimensional terrain features includes:

[0014] The accuracy of multi-dimensional terrain feature extraction is compared with the preset second accuracy.

[0015] If the extraction accuracy of the multi-dimensional terrain features is greater than or equal to the preset second accuracy, then the accuracy of the urban terrain mapping is determined to meet the requirements.

[0016] If the extraction accuracy of the multi-dimensional terrain features is less than the preset second accuracy, then the accuracy of the urban terrain mapping is determined to be unsatisfactory.

[0017] Further, determine whether it is necessary to increase the classification height threshold for ground points in the terrain image, including:

[0018] The extraction accuracy of the multi-dimensional terrain features is compared with the preset first accuracy and the preset second accuracy, respectively.

[0019] If the extraction accuracy of the multi-dimensional terrain features is less than or equal to the preset first accuracy, then it is determined that the classification height threshold of ground points in the terrain image needs to be increased.

[0020] If the extraction accuracy of the multi-dimensional terrain features is greater than the preset first accuracy and less than the preset second accuracy, then it is determined that there is no need to increase the classification height threshold of ground points in the terrain image.

[0021] Furthermore, the increase in the ground point classification height threshold in the terrain image is determined by the difference between a preset first accuracy rate and the extraction accuracy rate of multi-dimensional terrain features.

[0022] Furthermore, the building shadow coverage rate of the terrain image is used to determine whether the terrain image acquisition integrity meets the requirements, including:

[0023] Compare the building shadow coverage of the terrain image with a preset first coverage rate;

[0024] If the building shadow coverage of the terrain image is less than or equal to the preset first coverage, then it is determined that the acquisition integrity of the terrain image meets the requirements, and it is determined whether the classification height threshold of the ground points in the terrain image meets the requirements.

[0025] If the building shadow coverage of the terrain image is greater than the preset first coverage rate, then the integrity of the terrain image acquisition is determined to be unsatisfactory.

[0026] Further, determine whether it is necessary to increase the texture roughness coefficient of the multi-dimensional terrain features, including:

[0027] The building shadow coverage of the terrain image is compared with the preset first coverage and the preset second coverage, respectively;

[0028] If the building shadow coverage of the terrain image is greater than the preset first coverage and less than the preset second coverage, then it is determined that the multi-dimensional terrain feature texture roughness coefficient needs to be increased.

[0029] If the building shadow coverage of the terrain image is greater than or equal to the preset second coverage, then it is determined that there is no need to increase the multi-dimensional terrain feature texture roughness coefficient.

[0030] Furthermore, the increase in the multi-dimensional terrain feature texture roughness coefficient is determined by the difference between the building shadow coverage of the terrain image and a preset first coverage.

[0031] Furthermore, the edge-related redundancy rate of the TIN-modeled triangulated mesh is determined based on the image frame loss rate of the terrain image, including:

[0032] Compare the image frame loss rate of the terrain image with the preset loss rate;

[0033] If the image frame loss rate of the terrain image is less than or equal to the preset loss rate, then the anti-interference capability of the terrain image transmission is determined to meet the requirements, and there is no need to increase the redundancy rate of the edge number association of the TIN modeling triangular mesh. It is also determined whether the texture roughness coefficient of the multi-dimensional terrain feature meets the requirements.

[0034] If the image frame loss rate of the terrain image is greater than the preset loss rate, it is determined that the anti-interference capability of the terrain image transmission does not meet the requirements, and the redundancy rate of the TIN modeling triangulation edge number needs to be increased.

[0035] Furthermore, the image frame loss rate of the terrain image is the ratio of the number of lost terrain image frames to the total number of image frames in the terrain image.

[0036] Furthermore, the increase in the redundancy rate associated with the edge number of the TIN modeling triangular mesh is determined by the difference between the image frame loss rate of the terrain image and the preset loss rate.

[0037] Compared with existing technologies, the beneficial effects of this invention are as follows: The method of this invention adjusts the classification height threshold of ground points in the terrain image based on the extraction accuracy of multi-dimensional terrain features. Because TIN modeling prioritizes the generation of large-size triangular units, the height of ground points is indirectly compressed, making it difficult to effectively distinguish between ground points and non-ground points in micro-topography such as the edges of small depressions and steep slope inflections. By increasing the classification height threshold of ground points in the image, the attribution of ground points in micro-topographic areas can be defined, preventing key feature points at the bottom of small depressions and the surface of steep slopes from being incorrectly classified as non-ground points and removed. This increases the number of effective ground points participating in TIN modeling, thereby improving multi-dimensional terrain features and allowing the reproduction of micro-topographic details such as depressions and steep slope undulations. Furthermore, the method adjusts the texture roughness coefficient of multi-dimensional terrain features based on the building shadow coverage of the terrain image. Because high-rise buildings are densely packed in urban core areas, the shadows of multiple buildings easily overlap to form large continuous shadow areas, resulting in low gray values ​​and masked texture details in overlapping shadows, which are inconsistent with terrain anomalies such as potholes and cracks. Feature confusion and low success rate of terrain feature point extraction in shadowed areas can be addressed by increasing the multi-dimensional terrain texture roughness coefficient. This enhances the texture convexity and concaveness of the terrain in shadowed areas, amplifies the texture grayscale differences of road crack edges and depression contours, improves previously blurred texture details, and increases the distinction between terrain anomalies and the shadow background. The proportion of effective shape information in the image is increased. The redundancy rate of the TIN modeling triangulation edge number is adjusted according to the image frame loss rate of the terrain image. Due to the presence of electromagnetic radiation sources such as high-voltage lines and industrial equipment in the urban core area, the image transmission signal of the UAV is attenuated, leading to an increased probability of data packet loss during image transmission. By increasing the redundancy rate of the TIN modeling triangulation edge number and allocating core frames with redundant resources according to the proportion of the number of edges of the triangulation in a single frame to the total number of edges in the region, redundant backups are allocated. Even if the original data packet is damaged due to interference, the complete frame data can be quickly restored through the redundant packets. The recovery rate of key data packets in the image transmission link is improved, the image frame loss rate is reduced, and the accuracy of urban terrain mapping is improved.

[0038] Furthermore, the method of the present invention adjusts the classification height threshold of ground points in the terrain image by setting a preset first accuracy rate and a preset second accuracy rate. Since large-sized triangular units are preferentially generated during TIN modeling, the height of ground points is indirectly compressed, making it impossible to effectively distinguish between ground points and non-ground points in micro-topography such as the edges of small depressions and steep slope inflections. By increasing the classification height threshold of ground points in the image, the attribution of ground points in micro-topographic areas can be defined, avoiding the incorrect classification of key feature points at the bottom of small depressions and the slope surface of steep slopes as non-ground points and their removal. This increases the number of effective ground points participating in TIN modeling, thereby improving multi-dimensional terrain features and allowing the reproduction of micro-topographic details such as depressions and steep slope undulations, further improving the accuracy of urban terrain mapping.

[0039] Furthermore, the method of the present invention adjusts the texture roughness coefficient of multi-dimensional terrain features by setting a preset first coverage rate and a preset second coverage rate. Due to the high density of high-rise buildings in the urban core area, the shadows of multiple buildings are easily superimposed to form large continuous shadow areas, resulting in low gray values ​​of overlapping shadows and the obscuring of texture details. This leads to confusion with the features of terrain anomalies such as potholes and cracks, resulting in a low success rate of terrain feature point extraction in the shadow-covered area. By increasing the texture roughness coefficient of multi-dimensional terrain features, the texture convex and concave features of the terrain in the shadow area can be enhanced, the texture gray value difference of the edges of road cracks and the undulating contours of depressions can be amplified, the originally blurred texture details can be improved, the distinction between terrain anomalies in the shadow area and the shadow background can be increased, the proportion of effective shape information in the image can be increased, and the accuracy of urban terrain mapping can be further improved.

[0040] Furthermore, the method described in this invention adjusts the redundancy rate of the TIN modeling triangulation edge number association by setting a preset loss rate. Due to the presence of electromagnetic radiation sources such as high-voltage lines and industrial equipment in the urban core area, the image transmission signal of the UAV is attenuated, leading to an increased probability of data packet loss during image transmission. By increasing the redundancy rate of the TIN modeling triangulation edge number association, the core frames of redundant resources are allocated according to the proportion of the number of edges of the triangulation edge in a single frame to the total number of edges in the region, and redundant backups are allocated. Even if the original data packet is damaged due to interference, the complete frame data can be quickly restored through the redundant packets. The recovery rate of key data packets for modeling in the image transmission link is improved, the image frame loss rate is reduced, and the accuracy of urban terrain mapping is further improved. Attached Figure Description

[0041] Figure 1 This is an overall flowchart of the urban topographic mapping method based on image analysis according to an embodiment of the present invention;

[0042] Figure 2 This is a flowchart illustrating the process of determining the classification height threshold of ground points in a terrain image using an image analysis-based urban terrain mapping method according to an embodiment of the present invention.

[0043] Figure 3 This is a flowchart illustrating the process of determining the multi-dimensional terrain feature texture roughness coefficient using an image analysis-based urban terrain mapping method according to an embodiment of the present invention.

[0044] Figure 4 This is a flowchart illustrating the process of determining the number of edges and associated redundancy rate of a TIN modeling triangulation network using the image analysis-based urban terrain mapping method of this invention. Detailed Implementation

[0045] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0046] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] Please see Figure 1 As shown, it is an overall flowchart of the urban terrain mapping method based on image analysis according to an embodiment of the present invention.

[0048] This invention discloses an image analysis-based urban topographic mapping method, comprising:

[0049] Step S1: Acquire terrain images and point cloud data collected by the UAV, and sequentially perform radiometric correction, geometric correction and feature extraction on the terrain images to obtain multi-dimensional terrain features, and sequentially perform cleaning, noise reduction and standardization processing on the point cloud data to obtain standardized point cloud data.

[0050] Step S2: Based on the multi-dimensional terrain features and the standardized point cloud data, and combined with TIN, a three-dimensional network framework is constructed, and the image texture in the multi-dimensional terrain features is mapped to the three-dimensional network framework for optimization to obtain a three-dimensional urban terrain model. Based on the three-dimensional urban terrain model, the urban terrain is surveyed to obtain a surveyed image.

[0051] Step S3: Obtain the extraction accuracy of multi-dimensional terrain features, and determine whether the mapping accuracy of urban terrain meets the requirements based on the extraction accuracy of the multi-dimensional terrain features.

[0052] Step S4: If the accuracy of the urban terrain mapping does not meet the requirements, determine whether it is necessary to increase the classification height threshold of ground points in the terrain image.

[0053] Step S5: If it is not necessary to increase the classification height threshold of ground points in the terrain image, then obtain the building shadow coverage of the terrain image to determine whether the acquisition integrity of the terrain image meets the requirements.

[0054] Step S6: If the completeness of the terrain image acquisition does not meet the requirements, determine whether it is necessary to increase the multi-dimensional terrain feature texture roughness coefficient.

[0055] Step S7: If it is not necessary to increase the texture roughness coefficient of the multi-dimensional terrain features, then determine the edge number association redundancy rate of the TIN modeling triangular mesh based on the image frame loss rate of the terrain image.

[0056] Specifically, the terrain images were acquired by a high-resolution visible light camera, and the point cloud data were acquired by a lidar system.

[0057] Specifically, topographic images include surface outlines, feature details, and surface cover types.

[0058] Specifically, point cloud data includes three-dimensional spatial coordinates, echo intensity values, and timestamps.

[0059] Specifically, radiometric correction is a closed-loop process that involves preprocessing, precise correction by type, and effect verification to correct radiometric distortion in terrain images and make pixel grayscale values ​​accurately match the true reflectance of the land surface.

[0060] Specifically, geometric correction is a closed-loop process that involves basic data preparation, hierarchical distortion correction, registration optimization, and effect verification to eliminate spatial positional deviations in terrain images and ensure that the coordinates of each pixel are accurately mapped to the real geographic coordinate system.

[0061] Specifically, multidimensional terrain features include the standard deviation of absolute elevation of surface points, plane curvature, and terrain edge gradient.

[0062] Specifically, standardized point cloud data includes cleaned echo intensity values, denoised timestamps, and standardized three-dimensional spatial coordinates.

[0063] Specifically, the process of constructing a three-dimensional network framework based on multi-dimensional terrain features and standardized point cloud data, combined with TIN, involves using standardized point cloud data as the spatial coordinate basis, multi-dimensional terrain features as the optimization basis, and generating a three-dimensional mesh framework that fits the real terrain shape through TIN topology construction.

[0064] Specifically, TIN is an irregular set of triangles formed by connecting discrete terrain points according to specific rules. Each triangle serves as a basic unit of the terrain surface, and together they are pieced together to form a three-dimensional skeleton that fits the undulations of the real terrain.

[0065] Specifically, the three-dimensional network framework is a structured three-dimensional carrier formed by data fusion, semantic binding and accuracy optimization, based on standardized point cloud data as the coordinate basis, multi-dimensional terrain features as the optimization basis, and TIN as the topology.

[0066] Specifically, the process of mapping image textures from multi-dimensional terrain features to a 3D network framework for optimization to obtain a 3D urban terrain model involves accurately matching the 2D image textures from multi-dimensional terrain features with the 3D network framework, eliminating the adaptation deviation between textures and terrain through multiple rounds of optimization, and finally forming a 3D urban terrain model.

[0067] Specifically, the urban terrain 3D model is a digital product of 3D terrain formed by using standardized point cloud data as the coordinate basis, multi-dimensional terrain features as semantic and texture support, and the TIN 3D network framework as the geometric skeleton, after precise image texture mapping, multiple rounds of precision optimization and urban scene adaptation.

[0068] Specifically, the process of surveying and mapping urban terrain based on a 3D urban terrain model to obtain survey images involves transforming the 3D urban terrain model into a visualized and quantifiable survey image that conforms to surveying and mapping standards through digital projection, data extraction, symbolization processing, and scene adaptation optimization.

[0069] Specifically, the mapping image is constructed by building a TIN framework using multi-dimensional terrain features and standardized point cloud data, and then the image texture is mapped to the framework to optimize the model, ultimately outputting a high-precision terrain map.

[0070] Specifically, the extraction accuracy of multi-dimensional terrain features is the ratio of the number of accurately extracted multi-dimensional terrain features to the total number of multi-dimensional terrain features.

[0071] Specifically, the extraction accuracy is that the multi-dimensional terrain feature extraction results are completely consistent with the quantitative matching of the true benchmark values.

[0072] Specifically, the ground point classification height threshold in a terrain image is a critical height value used to distinguish between ground points and non-ground points in a terrain image and standardized point cloud data.

[0073] Specifically, the building shadow coverage rate of a terrain image is the ratio of the area covered by building shadows in the terrain image to the total area of ​​the terrain image.

[0074] Specifically, the multidimensional terrain feature texture roughness coefficient is a quantitative index calculated based on the micro-undulations and macro-structural features of the terrain surface from a standardized terrain image. It reflects the degree of spatial variation of pixel gray values ​​on the terrain surface. That is, the larger the coefficient, the rougher the terrain texture.

[0075] Specifically, the redundancy rate of the edge number in a TIN modeling triangulated network is the ratio of the number of redundant edges to the total number of edges in the TIN triangulated network.

[0076] Specifically, redundancy refers to unnecessary associated edges that can be removed through algorithmic optimization without causing excessive modeling errors.

[0077] In practice, the beneficial effects of this invention are as follows: The method adjusts the classification height threshold of ground points in the terrain image based on the extraction accuracy of multi-dimensional terrain features. Because TIN modeling prioritizes the generation of large-size triangular units, the height of ground points is indirectly compressed, making it difficult to effectively distinguish between ground points and non-ground points in micro-topography such as the edges of small depressions and steep slope inflections. By increasing the classification height threshold of ground points in the image, the attribution of ground points in micro-topographic areas can be defined, preventing key feature points at the bottom of small depressions and the surface of steep slopes from being incorrectly classified as non-ground points and removed. This increases the number of effective ground points participating in TIN modeling, thereby enhancing multi-dimensional terrain features and allowing the reproduction of micro-topographic details such as depressions and steep slope undulations. Furthermore, the method adjusts the texture roughness coefficient of multi-dimensional terrain features based on the building shadow coverage of the terrain image. Because high-rise buildings are densely packed in urban core areas, the shadows of multiple buildings easily overlap to form large continuous shadow areas, resulting in low gray values ​​and masked texture details in overlapping shadows, which is inconsistent with the features of terrain anomalies such as potholes and cracks. The extraction success rate of terrain feature points in shadowed areas is low. By increasing the texture roughness coefficient of multi-dimensional terrain features, the texture convexity and concavity features of the terrain in shadowed areas can be enhanced. The texture grayscale difference of road crack edges and depression contours can be amplified, improving the originally blurred texture details and increasing the distinction between terrain anomalies in shadowed areas and the shadow background. The proportion of effective shape information in the image is increased. The redundancy rate of the TIN modeling triangulation edge number is adjusted according to the image frame loss rate of the terrain image. Due to the presence of electromagnetic radiation sources such as high-voltage lines and industrial equipment in the urban core area, the image transmission signal of the UAV is attenuated, which increases the probability of data packet loss during image transmission. By increasing the redundancy rate of the TIN modeling triangulation edge number, the core frames of redundant resources are allocated according to the proportion of the number of edges of the triangulation in a single frame to the total number of edges in the region. Redundancy backup is allocated. Even if the original data packet is damaged due to interference, the complete frame data can be quickly restored through the redundant packets. The recovery rate of key data packets in the image transmission link is improved, the image frame loss rate is reduced, and the accuracy of urban terrain mapping is improved.

[0078] Please continue reading. Figure 2 As shown, it is a logical flowchart of the process of determining the classification height threshold of ground points in a terrain image using an image analysis-based urban terrain mapping method according to an embodiment of the present invention.

[0079] Specifically, determining whether the accuracy of urban terrain mapping meets the requirements based on the extraction accuracy of the multi-dimensional terrain features includes:

[0080] The accuracy of multi-dimensional terrain feature extraction is compared with the preset second accuracy.

[0081] If the extraction accuracy of the multi-dimensional terrain features is greater than or equal to the preset second accuracy, then the accuracy of the urban terrain mapping is determined to meet the requirements.

[0082] If the extraction accuracy of the multi-dimensional terrain features is less than the preset second accuracy, then the accuracy of the urban terrain mapping is determined to be unsatisfactory.

[0083] The reasons why the accuracy of urban terrain mapping may not meet requirements could be due to incomplete terrain image acquisition or an inadequate ground point classification height threshold in the terrain image. The next step is to determine the specific cause, which is essentially the process of deciding whether to increase the ground point classification height threshold in the terrain image.

[0084] Specifically, determining whether to increase the classification height threshold for ground points in terrain images includes:

[0085] The extraction accuracy of the multi-dimensional terrain features is compared with the preset first accuracy and the preset second accuracy, respectively.

[0086] If the extraction accuracy of the multi-dimensional terrain features is less than or equal to the preset first accuracy, then it is determined that the classification height threshold of ground points in the terrain image needs to be increased.

[0087] If the extraction accuracy of the multi-dimensional terrain features is greater than the preset first accuracy and less than the preset second accuracy, then it is determined that there is no need to increase the classification height threshold of ground points in the terrain image.

[0088] Specifically, if the accuracy rate of multi-dimensional terrain feature extraction is less than or equal to a preset first accuracy rate, it indicates that the reason for the unsatisfactory mapping accuracy of the urban terrain is that the ground point classification height threshold in the terrain image does not meet the requirements. Therefore, it is necessary to increase the ground point classification height threshold in the terrain image. If the accuracy rate of multi-dimensional terrain feature extraction is greater than a preset first accuracy rate but less than a preset second accuracy rate, it can be preliminarily determined that the completeness of the terrain image acquisition does not meet the requirements. Next, it is necessary to make a final determination on whether the completeness of the terrain image acquisition meets the requirements based on the building shadow coverage rate of the terrain image, that is, to determine whether the reason for the unsatisfactory mapping accuracy of the urban terrain is the unsatisfactory completeness of the terrain image acquisition.

[0089] It is understandable that the preset first accuracy rate is lower than the preset second accuracy rate. The three intervals divided by the preset first accuracy rate and the preset second accuracy rate correspond to three different scenarios:

[0090] The first interval is when the extraction accuracy of multi-dimensional terrain features is less than or equal to the preset first accuracy. The corresponding situation is: when TIN modeling, large-sized triangular units are generated first, and the height of ground points is indirectly compressed, which makes it impossible to effectively distinguish between ground points and non-ground points of micro-terrain such as the edge of small depressions and steep slope inflection points. At this time, it is necessary to adjust the classification height threshold of ground points in the terrain image.

[0091] The second interval is where the accuracy of multi-dimensional terrain feature extraction is greater than the first preset accuracy but less than the second preset accuracy. The corresponding situation is as follows: due to the high density of high-rise buildings in the urban core area, the shadows of multiple buildings are easy to overlap to form a large area of ​​continuous shadow. This results in low gray value of overlapping shadows and the masking of texture details, which is confused with the features of abnormal terrain areas such as potholes and cracks. The success rate of terrain feature point extraction in the shadow-covered area is low. At this time, it is necessary to further judge whether the completeness of the terrain image acquisition meets the requirements.

[0092] The third interval is when the accuracy of multi-dimensional terrain feature extraction is greater than or equal to the preset second accuracy rate. The corresponding situation is that the accuracy of urban terrain mapping meets the requirements, and no adjustment is needed.

[0093] Understandably, in the process of verifying the accuracy of urban terrain mapping, the use of preset first accuracy and preset second accuracy to characterize the degree of accuracy attainment is based on the core logic of transforming mapping accuracy into a quantifiable feature point matching rate range judgment. The preset first accuracy serves as the boundary between the ground point classification height threshold that needs adjustment and the completeness of data collection that needs to be judged. The preset second accuracy serves as the critical threshold for determining whether mapping accuracy meets the standards, providing a quantitative basis for targeted optimization of the urban terrain mapping process. The preset first accuracy and preset second accuracy can be set according to actual working conditions. The setting of the preset first accuracy and preset second accuracy aims to ensure the accuracy and practicality of urban terrain mapping. Optionally, the preset first accuracy and preset second accuracy are determined through a limited number of experiments by evaluating the mapping effect of different matching rate thresholds on urban terrain. The determined preset first accuracy and preset second accuracy should be neither too low nor cause excessive interference to the urban terrain mapping process. For example, the preset first accuracy is generally selected within the range of [85%, 89%], and the preset second accuracy is generally selected within the range of [90%, 94%].

[0094] Preferably, the first accuracy rate is 87% in the preferred embodiment, and the second accuracy rate is 92% in the preferred embodiment.

[0095] Specifically, the increase in the ground point classification height threshold in the terrain image is determined by the difference between a preset first accuracy rate and the extraction accuracy rate of multi-dimensional terrain features.

[0096] Specifically, when the difference between the preset first accuracy rate and the extraction accuracy rate of multi-dimensional terrain features is within 2%, the ground point classification height threshold in the terrain image is increased to 1.1 times the original value. When the difference between the preset first accuracy rate and the extraction accuracy rate of multi-dimensional terrain features exceeds 2%, in addition to increasing to 1.1 times the original value, for every 1% exceeding 1%, the ground point classification height threshold in the terrain image is increased by 0.2 meters. For example, if the difference between the preset first accuracy rate and the extraction accuracy rate of multi-dimensional terrain features is 3%, and the current ground point classification height threshold in the terrain image is 0.5 meters, the increased ground point classification height threshold in the terrain image will be 0.5 × 1.1 + 1 × 0.2 = 0.75 meters.

[0097] In practice, the method of this invention adjusts the classification height threshold of ground points in terrain images by setting a preset first accuracy rate and a preset second accuracy rate. Since large-sized triangular units are preferentially generated during TIN modeling, the height of ground points is indirectly compressed, making it impossible to effectively distinguish between ground points and non-ground points in micro-topography such as the edges of small depressions and steep slope inflections. By increasing the classification height threshold of ground points in the image, the attribution of ground points in micro-topographic areas can be defined, avoiding the incorrect classification of key feature points at the bottom of small depressions and the slope surface of steep slopes as non-ground points and their removal. This increases the number of effective ground points participating in TIN modeling, thereby improving multi-dimensional terrain features and allowing the reproduction of micro-topographic details such as depressions and steep slope undulations, further improving the accuracy of urban terrain mapping.

[0098] Please continue reading. Figure 3 As shown, it is a logical flowchart of the process of determining the multi-dimensional terrain feature texture roughness coefficient by the urban terrain mapping method based on image analysis according to an embodiment of the present invention.

[0099] Specifically, the building shadow coverage of terrain images is used to determine whether the integrity of the terrain image acquisition meets the requirements, including:

[0100] Compare the building shadow coverage of the terrain image with a preset first coverage rate;

[0101] If the building shadow coverage of the terrain image is less than or equal to the preset first coverage, then it is determined that the acquisition integrity of the terrain image meets the requirements, and it is determined whether the classification height threshold of the ground points in the terrain image meets the requirements.

[0102] If the building shadow coverage of the terrain image is greater than the preset first coverage rate, then the integrity of the terrain image acquisition is determined to be unsatisfactory.

[0103] If the building shadow coverage of the terrain image is less than or equal to the preset first coverage, it is determined that the integrity of the terrain image acquisition meets the requirements. However, if the accuracy of the urban terrain mapping has been determined to be unacceptable, it is necessary to further determine whether the ground point classification height threshold in the terrain image meets the requirements.

[0104] In practice, the classification height threshold of ground points in the actual terrain image is compared with the predetermined height threshold to determine whether the classification height threshold of ground points in the terrain image meets the requirements. If the classification height threshold of ground points in the actual terrain image is less than the predetermined height threshold, it is determined that the classification height threshold of ground points in the terrain image does not meet the requirements. The predetermined height threshold is the average value of the classification height threshold of ground points monitored in the same type of surveying area of ​​the city in the previous three months of the historical period.

[0105] If the ground point classification height threshold in the terrain image does not meet the requirements, then increase the ground point classification height threshold in the terrain image; if the ground point classification height threshold in the terrain image meets the requirements, then re-collect the extraction accuracy of multi-dimensional terrain features and re-determine whether the mapping accuracy of the urban terrain meets the requirements.

[0106] When the building shadow coverage of a terrain image exceeds a preset first coverage rate, the reason for the unsatisfactory mapping accuracy of the urban terrain can be identified as insufficient terrain image acquisition integrity. This incomplete acquisition integrity could be due to either an insufficient multi-dimensional terrain feature texture roughness coefficient or insufficient anti-interference capability during terrain image transmission. The next step is to determine the specific cause, which is essentially the process of deciding whether to increase the multi-dimensional terrain feature texture roughness coefficient.

[0107] Specifically, determining whether it is necessary to increase the roughness coefficient of multi-dimensional terrain feature texture includes:

[0108] The building shadow coverage of the terrain image is compared with the preset first coverage and the preset second coverage, respectively;

[0109] If the building shadow coverage of the terrain image is greater than the preset first coverage and less than the preset second coverage, then it is determined that the multi-dimensional terrain feature texture roughness coefficient needs to be increased.

[0110] If the building shadow coverage of the terrain image is greater than or equal to the preset second coverage, then it is determined that there is no need to increase the multi-dimensional terrain feature texture roughness coefficient.

[0111] Specifically, when the building shadow coverage of the terrain image is greater than a preset first coverage rate but less than a preset second coverage rate, the reason for the failure to meet the requirements for terrain image acquisition integrity is determined to be that the multi-dimensional terrain feature texture roughness coefficient does not meet the requirements. Therefore, it is necessary to increase the multi-dimensional terrain feature texture roughness coefficient. When the building shadow coverage of the terrain image is greater than or equal to the preset second coverage rate, it can be preliminarily determined that the terrain image transmission anti-interference capability does not meet the requirements. Next, it is necessary to make a final determination on whether the terrain image transmission anti-interference capability meets the requirements based on the image frame loss rate of the terrain image, that is, to determine whether the reason for the failure to meet the requirements for terrain image acquisition integrity is that the terrain image transmission anti-interference capability does not meet the requirements.

[0112] It is understandable that the preset first coverage rate is less than the preset second coverage rate. The three intervals divided by the preset first and preset second coverage rates correspond to three different scenarios:

[0113] The first interval is when the building shadow coverage of the terrain image is less than or equal to the preset first coverage rate. The corresponding situation is: the integrity of the terrain image acquisition meets the requirements. At this time, it is necessary to further determine whether the classification height threshold of the ground points in the terrain image meets the requirements.

[0114] The second interval is when the building shadow coverage of the terrain image is greater than the preset first coverage and less than the preset second coverage. The corresponding situation is: due to the high density of high-rise buildings in the core urban area, the shadows of multiple buildings are easy to overlap to form a large continuous shadow area, resulting in low gray value of overlapping shadows and the masking of texture details. This leads to confusion with the features of abnormal terrain areas such as potholes and cracks, resulting in a low success rate of terrain feature point extraction in the shadow-covered area. At this time, it is necessary to adjust the multi-dimensional terrain feature texture roughness coefficient.

[0115] The third interval is when the building shadow coverage of the terrain image is greater than or equal to the preset second coverage rate. The corresponding situation is: due to the presence of electromagnetic radiation sources such as high-voltage lines and industrial equipment in the urban core area, the drone image transmission signal is attenuated, which increases the probability of data packet loss during image transmission. At this time, it is necessary to further determine whether the anti-interference of the terrain image transmission meets the requirements.

[0116] Understandably, in the process of verifying the accuracy of urban topographic mapping, the introduction of preset first and second coverage rates to characterize the completeness of topographic image acquisition is crucial. The core logic is to transform acquisition completeness into a quantifiable judgment of the building shadow coverage range of the topographic image. The preset first coverage rate serves as the boundary between the threshold for classifying ground points requiring confirmation and the adjustment of the multi-dimensional topographic feature texture roughness coefficient. The preset second coverage rate serves as the critical point for determining whether the multi-dimensional topographic feature texture roughness coefficient needs adjustment and whether the image transmission anti-interference capability needs to be assessed. This provides a quantitative basis for targeted optimization of the urban topographic mapping data processing workflow. The preset first and second coverage rates can be set according to actual working conditions. Their setting aims to ensure the accuracy and practicality of urban topographic mapping. Optionally, the preset first and second coverage rates are determined through a limited number of experiments by evaluating the effect of different topographic images' building shadow coverage on urban topographic mapping. The determined preset first and second coverage rates should be neither too small nor cause excessive interference to the mapping process. For example, the preset first coverage rate is generally selected in the range of [8%, 10%], and the preset second coverage rate is generally selected in the range of [11%, 14%].

[0117] Preferably, the first coverage rate is 9% in the preferred embodiment, and the second coverage rate is 12% in the preferred embodiment.

[0118] Specifically, the increase in the multi-dimensional terrain feature texture roughness coefficient is determined by the difference between the building shadow coverage of the terrain image and a preset first coverage.

[0119] Specifically, when the difference between the building shadow coverage of the terrain image and the preset first coverage is within 2%, the multi-dimensional terrain feature texture roughness coefficient increases to 1.05 times the original value. When the difference between the building shadow coverage of the terrain image and the preset first coverage exceeds 2%, in addition to increasing to 1.05 times the original value, for every 1% exceeding 1%, the multi-dimensional terrain feature texture roughness coefficient increases by 0.1. For example, when the difference between the building shadow coverage of the terrain image and the preset first coverage is 3%, the current multi-dimensional terrain feature texture roughness coefficient is 2.0, and the increased multi-dimensional terrain feature texture roughness coefficient is 2.0×1.05+0.1×1=2.2.

[0120] In practice, the method of the present invention adjusts the texture roughness coefficient of multi-dimensional terrain features by setting a preset first coverage rate and a preset second coverage rate. Due to the high density of high-rise buildings in the core urban area, the shadows of multiple buildings are prone to overlap to form large continuous shadow areas, resulting in low gray values ​​of overlapping shadows and the obscuring of texture details. This leads to confusion with the features of terrain anomalies such as potholes and cracks, resulting in a low success rate of terrain feature point extraction in the shadow-covered area. By increasing the texture roughness coefficient of multi-dimensional terrain features, the texture convex and concave features of the terrain in the shadow area can be enhanced, the texture gray value difference of the edges of road cracks and the undulating contours of depressions can be amplified, the originally blurred texture details can be improved, the distinction between terrain anomalies in the shadow area and the shadow background can be increased, the proportion of effective shape information in the image can be increased, and the accuracy of urban terrain mapping can be further improved.

[0121] Please continue reading. Figure 4 As shown, it is a logical flowchart of the process of determining the number of edges and associated redundancy rate of TIN modeling triangular mesh using the image analysis-based urban terrain mapping method of the present invention.

[0122] Specifically, the redundancy rate of the TIN-modeled triangulated mesh edge count is determined based on the image frame loss rate of the terrain image, including:

[0123] Compare the image frame loss rate of the terrain image with the preset loss rate;

[0124] If the image frame loss rate of the terrain image is less than or equal to the preset loss rate, then the anti-interference capability of the terrain image transmission is determined to meet the requirements, and there is no need to increase the redundancy rate of the edge number association of the TIN modeling triangular mesh. It is also determined whether the texture roughness coefficient of the multi-dimensional terrain feature meets the requirements.

[0125] If the image frame loss rate of the terrain image is greater than the preset loss rate, it is determined that the anti-interference capability of the terrain image transmission does not meet the requirements, and the redundancy rate of the TIN modeling triangulation edge number needs to be increased.

[0126] Specifically, the image frame loss rate of the terrain image is the ratio of the number of lost terrain image frames to the total number of terrain image frames.

[0127] When the image frame loss rate of the terrain image is less than or equal to the preset loss rate, it is determined that the anti-interference capability of the terrain image transmission meets the requirements. However, if it has been previously determined that the integrity of the terrain image acquisition does not meet the requirements, then it is necessary to further determine whether the texture roughness coefficient of the multi-dimensional terrain features meets the requirements.

[0128] In practice, the texture roughness coefficient of the multidimensional terrain features is compared with the predetermined texture roughness coefficient to determine whether the texture roughness coefficient of the multidimensional terrain features meets the requirements. If the texture roughness coefficient of the actual multidimensional terrain features is less than the predetermined texture roughness coefficient, it is determined that the texture roughness coefficient of the multidimensional terrain features does not meet the requirements. The predetermined texture roughness coefficient is the average value of the texture roughness coefficient of the multidimensional terrain features monitored in the same type of surveying area of ​​the city in the previous three months of the historical period.

[0129] If the multi-dimensional terrain feature texture roughness coefficient does not meet the requirements, then increase the multi-dimensional terrain feature texture roughness coefficient; if the multi-dimensional terrain feature texture roughness coefficient meets the requirements, then re-acquire the building shadow coverage of the terrain image and re-determine whether the acquisition integrity of the terrain image meets the requirements.

[0130] When the image frame loss rate of the terrain image is greater than the preset loss rate, it can be determined that the reason for the failure of the terrain image acquisition integrity is that the anti-interference capability of the terrain image transmission is not up to standard. Therefore, it is necessary to increase the redundancy rate of the edge number association of the TIN modeling triangular mesh.

[0131] It is understandable that the two intervals defined by the preset loss rate correspond to two different scenarios:

[0132] The first interval is when the image frame loss rate of the terrain image is less than or equal to the preset loss rate. The corresponding situation is that the anti-interference of the communication data acquisition process meets the requirements. At this time, it is necessary to further determine whether the texture roughness coefficient of the multi-dimensional terrain features meets the requirements.

[0133] The second interval is when the image frame loss rate of the terrain image is greater than the preset loss rate. The corresponding situation is: due to the presence of electromagnetic radiation sources such as high-voltage lines and industrial equipment in the urban core area, the image transmission signal of the UAV is attenuated, which leads to an increased probability of data packet loss during image transmission. At this time, it is necessary to increase the redundancy rate of the edge number association of the TIN modeling triangular network.

[0134] Understandably, in the process of optimizing the accuracy of TIN modeling in urban topographic mapping, using a preset loss rate to characterize the reliability of topographic image transmission is based on the core logic of transforming transmission reliability into a quantifiable judgment of image frame loss rate. The preset loss rate is the critical threshold that distinguishes between the need to adjust the redundancy rate of the TIN modeling triangular mesh edge count and the need to confirm the texture roughness coefficient of multi-dimensional topographic features. It clarifies the targeted processing direction under different image frame loss rate scenarios, providing a quantitative basis for ensuring the accuracy of topographic contour restoration in TIN modeling. The preset loss rate can be set according to actual working conditions. The setting of the preset loss rate aims to ensure the accuracy and practicality of urban topographic mapping. Optionally, the preset loss rate is determined through a limited number of experiments by evaluating the mapping effect of different image frame loss rates on urban topography. The determined preset loss rate should be neither too small nor cause excessive interference to the mapping process. For example, the preset loss rate is generally selected in the range of [1.5%, 3.0%].

[0135] Preferably, the preset loss rate is 2.0% in this embodiment.

[0136] Specifically, the increase in the redundancy rate associated with the number of edges in the TIN modeling triangular mesh is determined by the difference between the image frame loss rate of the terrain image and the preset loss rate.

[0137] Specifically, when the difference between the image frame loss rate of the terrain image and the preset loss rate is within 0.5%, the edge number association redundancy rate of the TIN modeling triangulation increases to 1.1 times the original value. When the difference between the image frame loss rate of the terrain image and the preset loss rate exceeds 0.5%, in addition to increasing to 1.1 times the original value, for every 0.2% exceeding the original value, the edge number association redundancy rate of the TIN modeling triangulation increases by 0.05. For example, when the difference between the image frame loss rate of the terrain image and the preset loss rate is 0.9%, and the current edge number association redundancy rate of the TIN modeling triangulation is 15%, the increased edge number association redundancy rate of the TIN modeling triangulation is 15%×1.1+0.05×2=16.6%.

[0138] In practice, the method described in this invention adjusts the redundancy rate of the edge number association of the TIN modeling triangulation network by setting a preset loss rate. Due to the presence of electromagnetic radiation sources such as high-voltage lines and industrial equipment in the urban core area, the image transmission signal of the UAV is attenuated, which increases the probability of data packet loss during image transmission. By increasing the redundancy rate of the edge number association of the TIN modeling triangulation network, the core frames of redundant resources are allocated according to the proportion of the number of edges of the triangulation network in a single frame to the total number of edges in the region. Redundancy backup is allocated, so even if the original data packet is damaged due to interference, the complete frame data can be quickly restored through the redundant packets. The recovery rate of key data packets for modeling in the image transmission link is improved, the image frame loss rate is reduced, and the accuracy of urban terrain mapping is further improved.

[0139] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for urban topographic mapping based on image analysis, characterized in that, include: The terrain images and point cloud data collected by the UAV are acquired, and the terrain images are subjected to radiometric correction, geometric correction and feature extraction in sequence to obtain multi-dimensional terrain features. The point cloud data is subjected to cleaning, noise reduction and standardization processing in sequence to obtain standardized point cloud data. A three-dimensional network framework is constructed based on the multi-dimensional terrain features and the standardized point cloud data, combined with TIN. The image texture in the multi-dimensional terrain features is mapped to the three-dimensional network framework for optimization to obtain a three-dimensional urban terrain model. The urban terrain is then surveyed based on the three-dimensional urban terrain model to obtain a surveyed image. The extraction accuracy of multi-dimensional terrain features is obtained, and the accuracy of urban terrain mapping is determined based on the extraction accuracy of the multi-dimensional terrain features. If the accuracy of the urban terrain mapping does not meet the requirements, it is determined whether it is necessary to increase the classification height threshold of ground points in the terrain image; If it is not necessary to increase the classification height threshold of ground points in the terrain image, then obtain the building shadow coverage of the terrain image to determine whether the acquisition integrity of the terrain image meets the requirements; If the completeness of the acquired terrain image does not meet the requirements, then determine whether it is necessary to increase the multi-dimensional terrain feature texture roughness coefficient. If it is not necessary to increase the texture roughness coefficient of multi-dimensional terrain features, the edge number and associated redundancy rate of TIN modeling triangular mesh are determined based on the image frame loss rate of the terrain image.

2. The urban topographic mapping method based on image analysis according to claim 1, characterized in that, Determining whether the accuracy of urban terrain mapping meets the requirements based on the extraction accuracy of the multi-dimensional terrain features includes: The accuracy of multi-dimensional terrain feature extraction is compared with the preset second accuracy. If the extraction accuracy of the multi-dimensional terrain features is greater than or equal to the preset second accuracy, then the accuracy of the urban terrain mapping is determined to meet the requirements. If the extraction accuracy of the multi-dimensional terrain features is less than the preset second accuracy, then the accuracy of the urban terrain mapping is determined to be unsatisfactory.

3. The urban topographic mapping method based on image analysis according to claim 2, characterized in that, Determine whether the classification height threshold for ground points in terrain images needs to be increased, including: The extraction accuracy of the multi-dimensional terrain features is compared with the preset first accuracy and the preset second accuracy, respectively. If the extraction accuracy of the multi-dimensional terrain features is less than or equal to the preset first accuracy, then it is determined that the classification height threshold of ground points in the terrain image needs to be increased. If the extraction accuracy of the multi-dimensional terrain features is greater than the preset first accuracy and less than the preset second accuracy, then it is determined that there is no need to increase the classification height threshold of ground points in the terrain image.

4. The urban topographic mapping method based on image analysis according to claim 3, characterized in that, The increase in the ground point classification height threshold in the terrain image is determined by the difference between the preset first accuracy and the extraction accuracy of multi-dimensional terrain features.

5. The urban topographic mapping method based on image analysis according to claim 4, characterized in that, Building shadow coverage in terrain images is used to determine whether the integrity of terrain image acquisition meets requirements, including: Compare the building shadow coverage of the terrain image with a preset first coverage rate; If the building shadow coverage of the terrain image is less than or equal to the preset first coverage, then it is determined that the acquisition integrity of the terrain image meets the requirements, and it is determined whether the classification height threshold of the ground points in the terrain image meets the requirements. If the building shadow coverage of the terrain image is greater than the preset first coverage rate, then the integrity of the terrain image acquisition is determined to be unsatisfactory.

6. The urban topographic mapping method based on image analysis according to claim 5, characterized in that, Determine whether it is necessary to increase the texture roughness coefficient of multi-dimensional terrain features, including: The building shadow coverage of the terrain image is compared with the preset first coverage and the preset second coverage, respectively; If the building shadow coverage of the terrain image is greater than the preset first coverage and less than the preset second coverage, then it is determined that the multi-dimensional terrain feature texture roughness coefficient needs to be increased. If the building shadow coverage of the terrain image is greater than or equal to the preset second coverage, then it is determined that there is no need to increase the multi-dimensional terrain feature texture roughness coefficient.

7. The urban topographic mapping method based on image analysis according to claim 6, characterized in that, The increase in the texture roughness coefficient of the multi-dimensional terrain feature is determined by the difference between the building shadow coverage of the terrain image and the preset first coverage.

8. The urban topographic mapping method based on image analysis according to claim 7, characterized in that, Determining the edge number association redundancy rate of TIN-modeled triangulated mesh based on the image frame loss rate of terrain images includes: Compare the image frame loss rate of the terrain image with the preset loss rate; If the image frame loss rate of the terrain image is less than or equal to the preset loss rate, then the anti-interference capability of the terrain image transmission is determined to meet the requirements, and there is no need to increase the redundancy rate of the edge number association of the TIN modeling triangular mesh. It is also determined whether the texture roughness coefficient of the multi-dimensional terrain feature meets the requirements. If the image frame loss rate of the terrain image is greater than the preset loss rate, it is determined that the anti-interference capability of the terrain image transmission does not meet the requirements, and the redundancy rate of the TIN modeling triangulation edge number needs to be increased.

9. The urban topographic mapping method based on image analysis according to claim 8, characterized in that, The image frame loss rate of the terrain image is the ratio of the number of lost terrain image frames to the total number of terrain image frames.

10. The urban topographic mapping method based on image analysis according to claim 9, characterized in that, The increase in the redundancy rate associated with the edge number of the TIN modeling triangular mesh is determined by the difference between the image frame loss rate of the terrain image and the preset loss rate.

Images