Geographic information surveying and mapping data processing method based on image analysis

Abstract

The invention discloses a geographic information surveying and mapping data processing method based on image analysis, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting a multi-angle satellite remote sensing image, and dividing the multi-angle satellite remote sensing image into a plurality of image blocks according to a geographic coordinate system; extracting topographic feature parameters of the image blocks, and establishing feature vectors of the topographic feature parameters; according to the feature vectors, establishing a ground feature classification model to perform ground feature classification on the image blocks to obtain ground feature classification labels; constructing a three-dimensional geographic information model according to the ground feature classification labels and the topographic feature parameters, and marking ground feature boundary points; and generating surveying and mapping data based on the three-dimensional geographic information model and the surface feature boundary points. According to the method, the improved three-dimensional geographic information modeling algorithm is adopted, the ground feature boundary points are precisely marked, and the problems of high ground feature classification difficulty, fuzzy boundary recognition, poor model training stability and the like in the complex terrain are effectively solved.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing image processing, in particular to a geographic information surveying and mapping data processing method based on image analysis. Background Art

[0002] With the rapid development of geographic information technology and remote sensing image processing technology, geographic information mapping based on image analysis has become an important research direction in the field of modern surveying and mapping. Traditional geographic information mapping methods mainly rely on ground surveying equipment to obtain data. Although these methods can provide high-precision measurement results, they are limited by terrain conditions, surveying scope, and the on-site work efficiency of operators. There are significant limitations in dealing with large-scale complex terrain or high-resolution requirements. In recent years, with the increasing maturity of satellite remote sensing technology, the acquisition of high-resolution multi-angle satellite image data has provided a new data source for geographic information mapping. Using multi-angle satellite remote sensing images for mapping can not only cover a large area of ​​terrain, but also provide rich three-dimensional geographic information through multi-view images. However, the processing of multi-angle satellite image data usually involves complex image preprocessing, feature extraction, and object classification algorithms, which puts higher requirements on algorithm efficiency and accuracy. At the same time, the rapid growth of data scale also poses a challenge to data storage and processing capabilities.

[0003] Although the existing geographic information mapping methods based on remote sensing images have made some progress in terrain feature extraction and three-dimensional model construction, there are still some shortcomings. First, in the process of terrain feature extraction, most of the existing technologies rely on traditional rule matching or algorithms based on shallow features, resulting in limited accuracy when processing complex terrain or detailed features. Secondly, the existing object classification models are usually based on single-view image data, ignoring the perspective compensation information provided by multi-angle images, and cannot fully reflect the three-dimensional spatial characteristics of the objects. Thirdly, the construction of three-dimensional geographic information models and the accurate marking of object boundaries are still difficult points. Existing methods often lack the effective use of the correlation between terrain features and object classification, resulting in insufficient accuracy in the generation of mapping data. In addition, since the multiple steps in the processing flow are independent of each other and fail to achieve efficient collaboration, the data processing efficiency is low, and it is difficult to meet the needs of efficient mapping. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention proposes a geographic information surveying and mapping data processing method based on image analysis.

[0005] Therefore, the present invention provides a geographic information surveying and mapping data processing method based on image analysis, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for processing geographic information surveying and mapping data based on image analysis, which comprises: Collecting multi-angle satellite remote sensing images, and dividing the multi-angle satellite remote sensing images into a plurality of image blocks according to a geographic coordinate system; Extracting terrain characteristic parameters of the image block and establishing a characteristic vector of the terrain characteristic parameters; According to the feature vector, a ground object classification model is established to classify the ground objects in the image block to obtain a ground object classification label; Constructing a three-dimensional geographic information model based on the object classification labels and the terrain feature parameters, and marking the object boundary points; Generate surveying and mapping data based on the three-dimensional geographic information model and the boundary points of the land objects.

[0007] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis of the present invention, wherein: the terrain characteristic parameters include elevation value, slope value and texture characteristic value; Combining the elevation value, the slope value and the texture feature value to construct the feature vector; The calculation of the elevation value is shown in the following formula: , in, Represents the coordinate points in the image The gray value at is the adaptive kernel function, is the calculation window, is the smoothing coefficient, represents the Laplace operator, is the coordinate point The elevation value at .

[0008] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis of the present invention, the slope value is calculated according to the spatial change rate of the elevation value, as shown in the following formula: , in, and They are Direction and The elevation gradient of the direction, is the slope correction factor, is the elevation influencing factor, Indicates coordinate points The slope value at ; The texture feature value is extracted using the gray level co-occurrence matrix, as shown in the following formula: , in, is the position in the gray-level co-occurrence matrix The probability value of is the pixel pitch, is the direction angle, To avoid small constants whose logarithms are zero, is the distance weight function, is the direction weight function, Indicates a specific distance and direction The texture feature values ​​under .

[0009] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis of the present invention, obtaining the ground feature classification label includes: Calculate the weights of features at different scales through the attention mechanism; The features of different branches of the object classification model are weighted and fused with the weights of the features of different scales obtained by the attention mechanism to obtain a comprehensive feature vector; The object classification label is output according to the comprehensive feature vector, and the object classification model is optimized through a loss function.

[0010] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis of the present invention, the classification confidence of the ground object classification label is calculated by the following formula: , in, Indicates the current feature category. The current feature category The classification weights, The current feature category The bias term, is the comprehensive feature vector, For the The bias term of each category, is the weight matrix of the i-th category; The loss function is expressed by the following formula: , in, is the true label, is the predicted probability, is the sample difficulty coefficient, is the balance factor, Represents the final loss function value.

[0011] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis described in the present invention, the construction of the three-dimensional geographic information model includes: Extracting the boundary features of the object through a bidirectional recursive neural network according to the object classification label and the terrain feature parameters; Differentiated boundary transition zones are set for different types of land objects, and a three-dimensional grid model based on boundary constraints is constructed based on the boundary characteristics of the land objects; In the transition zone area, the elevation value is smoothly transitioned by using a cubic spline interpolation algorithm, and the vertex positions of the three-dimensional model are optimized by an iterative least squares method to obtain the three-dimensional geographic information model; Obtaining the boundary points of the ground feature includes: The region segmentation algorithm and the adaptive boundary point extraction algorithm are used to calculate the coordinates of the initial boundary points; Optimizing and screening the coordinates of the initial boundary points; The retained boundary points are interpolated to obtain the boundary points of the feature and generate a continuous boundary curve.

[0012] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis of the present invention, the extraction of the boundary features of the land object is expressed by the following formula: , in, and are the forward and backward LSTM units, respectively. For location The classification characteristics of is the feature fusion operator, is the class adaptive weight, For location The feature value of the boundary of the object at ; The three-dimensional grid model is expressed by the following formula: , in, are vertex coordinates, is the elevation gradient, is the boundary response function, is the edge smoothing factor, is the boundary feature of the vertex coordinates, It is a three-dimensional mesh model.

[0013] As a preferred solution of the geographic information surveying and mapping data processing method based on image analysis of the present invention, the calculation of the coordinates of the initial boundary points is as follows: , in, is the boundary saliency measure, is the local structure weight, is the classification confidence, is the balance coefficient, is the three-dimensional coordinate of the boundary point; The interpolation of the reserved boundary points is expressed by the following formula: , in, is the cubic B-spline basis function, is the tension control factor, is the continuous boundary curve equation, For the The coordinates of the boundary points, is a parameter variable.

[0014] In a second aspect, an embodiment of the present invention provides a geographic information surveying and mapping data processing system based on image analysis, which includes: An acquisition module is used to acquire multi-angle satellite remote sensing images and divide the multi-angle satellite remote sensing images into a plurality of image blocks according to a geographic coordinate system; A feature extraction module, used to extract terrain feature parameters of the image block and establish a feature vector of the terrain feature parameters; A ground object classification module is used to establish a ground object classification model according to the feature vector to classify the image block and obtain a ground object classification label; A boundary point marking module is used to establish a ground object classification model according to the feature vector to classify the ground objects in the image block and obtain a ground object classification label; A surveying and mapping data generation module is used to generate surveying and mapping data based on the three-dimensional geographic information model and the boundary points of the land objects.

[0015] The beneficial effects of the present invention are the collection and refinement of multi-angle satellite remote sensing images, combined with high-precision terrain feature extraction technology, to achieve a comprehensive analysis of land feature features, and the introduction of multi-scale convolutional neural network (MSCNN) for land object classification, which greatly improves the classification accuracy and boundary recognition accuracy. The improved three-dimensional geographic information modeling algorithm not only accurately marks the boundary points of land objects, but also generates high-quality topographic maps, contour maps and land object distribution maps through optimization processing, effectively solving the problems of difficulty in land object classification in complex terrain, fuzzy boundary recognition and poor model training stability. In addition, through hierarchical quality control and hierarchical structure storage, it is ensured that the surveying and mapping data has high precision, high integrity and good compatibility, meets the needs of engineering applications, and shows significant technical advantages in the field of geographic information surveying and mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 The present invention is a flowchart of a geographic information surveying and mapping data processing method based on image analysis. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0020] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0021] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0022] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] Example 1 Reference Figure 1 , which is the first embodiment of the present invention, provides a geographic information surveying and mapping data processing method based on image analysis, comprising: S1: Collect multi-angle satellite remote sensing images, and divide the multi-angle satellite remote sensing images into a plurality of image blocks according to a geographic coordinate system; Multi-angle remote sensing images of the study area are collected at different time periods by multiple low-orbit remote sensing satellites. The multi-angle remote sensing images include vertical viewing angle, 45-degree oblique viewing angle in the east-west direction, and 45-degree oblique viewing angle in the north-south direction. The multi-angle remote sensing images are divided into 500-meter × 500-meter square image blocks according to the WGS84 geographic coordinate system, wherein an overlapping area of ​​100 meters is set between adjacent image blocks for subsequent image stitching.

[0024] S2: extracting terrain characteristic parameters of the image block, including elevation value, slope value and texture characteristic value, and establishing a characteristic vector of the terrain characteristic parameters; Feature parameters are extracted from the image block, and the digital elevation model of each image block is calculated by the pixel gray value to obtain the elevation value. More specifically, feature parameters are extracted from the image block, and the elevation value of the digital elevation model DEM is first calculated based on the pixel gray gradient operator. : , in, Represents pixel The gray value of is the adaptive kernel function, is the calculation window, is the smoothing coefficient, represents the Laplace operator.

[0025] The slope value is then calculated using the spatial rate of change of the elevation value. : , in, and They are Direction and The elevation gradient of the direction, is the slope correction factor, is the elevation influencing factor, Indicates coordinate points The slope value at .

[0026] The gray-level co-occurrence matrix is ​​further used to extract texture feature values ​​including energy, contrast, and entropy, as shown in the following formula: , in, is the position in the gray-level co-occurrence matrix The probability value of is the pixel pitch, is the direction angle, To avoid small constants whose logarithms are zero, is the distance weight function, is the direction weight function, Indicates a specific distance and direction The texture feature values ​​under .

[0027] The elevation value, the slope value and the texture feature value are combined to construct a 15-dimensional feature vector , as shown below: , in, , , are the normalized eigenvalues, , , is the corresponding feature weight coefficient, where the elevation value occupies 3 dimensions, the slope value occupies 2 dimensions, and the texture feature value occupies 10 dimensions.

[0028] S3: According to the feature vector, a ground object classification model is established to classify the ground objects in the image block to obtain a ground object classification label; According to the 15-dimensional feature vector, a multi-scale convolutional neural network MSCNN is constructed for object classification. The MSCNN includes three parallel convolution branches. The convolution kernel sizes of each branch are 3×3, 5×5 and 7×7 respectively. Each branch adopts four convolution layers, where the first layer adopts 32 feature maps, the second layer adopts 64 feature maps, the third layer adopts 128 feature maps, and the fourth layer adopts 256 feature maps. Batch normalization layers and ReLU activation functions are set between the convolution layers, and the following attention mechanism is used. Calculate the weights of features at different scales: , in, and is the learnable weight matrix, for Feature map at scale, and is the bias term.

[0029] The features of the three branches are fused through attention weighting to obtain the comprehensive feature vector Fc: , in, represents the convolution operation, is a scale-adaptive function.

[0030] The final output includes the classification labels of objects including buildings, roads, vegetation, and water bodies. The classification confidence is calculated by the Softmax function: , in, Indicates the type of feature. is the corresponding classification weight, is the bias term.

[0031] The improved cross entropy loss function L is used to optimize the model training: , in, is the true label, is the predicted probability, is the sample difficulty coefficient, is the balancing factor.

[0032] To be noted, the sample difficulty coefficient The calculation is done through an adaptive dynamic weighting mechanism. The specific calculation process is as follows: First, calculate the historical prediction error Ei of the sample: , in, is the number of historical predictions, For the The true label of the iteration, For the The predicted probability for the iteration.

[0033] Then, the relative entropy of the sample is calculated based on the prediction error : , in, is the maximum entropy value in the current batch; Next, the local spatial consistency index is introduced : , in, and is the feature vector of adjacent samples, is the spatial distance weight, is the normalization factor.

[0034] Finally, the sample difficulty coefficient It is obtained through the weighted combination of the above three indicators.

[0035] Better, sample difficulty coefficient By integrating historical prediction errors , relative entropy and spatial consistency index The three-dimensional evaluation indicators and the dynamic weight adaptive adjustment mechanism have significantly improved the limitations of the traditional single error evaluation method. In the experiment, the classification accuracy was improved by 8-12%, the training convergence speed was improved by 25%, and the accuracy of object boundary recognition was improved by more than 15%. At the same time, key technical problems such as complex object boundary recognition, small sample classification and model training stability were solved. It has strong adaptability, robustness and generalization ability, and shows obvious technical advantages in the actual application of complex terrain object classification.

[0036] S4: constructing a three-dimensional geographic information model based on the feature classification label and the terrain feature parameter, and marking the feature boundary points; According to the object classification label and the terrain feature parameters, the improved 3D reconstruction algorithm is used to construct the geographic information model, and the reference plane grid is set with a grid spacing of 10 meters. The grid nodes contain elevation attributes and object type attributes. First, the object boundary feature B(x,y) is extracted through a bidirectional recurrent neural network BRNN: , in, and are the forward and backward LSTM units, respectively. For location The classification characteristics of is the feature fusion operator, is the class adaptive weight, For location The characteristic value of the boundary of the object at .

[0037] Different boundary transition zones are set for different types of land objects, where the width of the building boundary transition zone is 2 meters, the width of the road boundary transition zone is 5 meters, and the width of the water body and vegetation boundary transition zone is 3 meters. A three-dimensional grid model based on boundary constraints is constructed: , in, are vertex coordinates, is the elevation gradient, is the boundary response function, is the edge smoothing factor, is the boundary feature of the vertex coordinates, It is a three-dimensional mesh model; In the transition zone, the elevation value is smoothly processed by cubic spline interpolation algorithm, and the vertex position of the three-dimensional model is optimized by iterative least squares method: , in, is the energy function, is the learning rate, is the regularization coefficient, is the local curvature.

[0038] The region segmentation algorithm and the adaptive boundary point extraction algorithm are used to calculate the coordinates of the key boundary points P (x, y, z), where the area with an elevation change rate greater than 0.3 and across different land object categories is marked as a boundary candidate area: , in, is the boundary saliency measure, is the local structure weight, is the classification confidence, is the balance coefficient; a boundary point is sampled every 1 meter in the boundary candidate area; the extracted boundary points are optimized and screened, and redundant points with a distance between adjacent boundary points less than 0.5 meters and an elevation difference less than 0.3 meters are removed. Finally, a continuous boundary curve L(t) is generated by interpolation of boundary points: , in, is the cubic B-spline basis function, is the tension control factor. Finally, the optimized boundary points are grouped and stored according to the feature categories. Each boundary point contains information such as spatial coordinates, feature attributes, and adjacent feature types.

[0039] S5: Generate surveying and mapping data based on the three-dimensional geographic information model and the boundary points of the land objects, including a topographic map, a contour map and a land object distribution map.

[0040] Based on the three-dimensional geographic information model and the boundary points of the land features, surveying and mapping data are generated. First, topographic map data is generated, and the three-dimensional model is projected onto a two-dimensional plane at a scale of 1:500. The terrain features are expressed using a layered coloring method, in which the color difference value of adjacent elevation levels is set to 20%, and the topographic map resolution is set to 0.5 m / pixel. At the same time, absolute elevation values ​​are marked at key landform points, and field survey standards are used to perform vector processing on artificial features such as buildings and roads.

[0041] Then the contour map data is generated, with the basic contour interval set to 1 meter, the main curve interval to 5 meters, and the counting curve interval to 25 meters. The contour interval is increased to 0.5 meters in steep areas with slopes greater than 30 degrees. For flat areas with slopes less than 5 degrees, additional elevation annotation points are used for supplementary expression. All contour lines are fitted with smooth spline curves to ensure the continuity and smoothness of the curves. At the same time, elevation annotations are added at the turning points of the contour lines.

[0042] Then, a feature distribution map was constructed, and a classified coloring scheme was used to express different types of features, where buildings were represented by red, roads by gray, vegetation by green, and water bodies by blue. A 0.5-meter-wide transition buffer zone was set at the boundaries of the features, and the boundary recognition effect was enhanced through gradient tones and texture filling. At the same time, the attribute information of the main features was superimposed and displayed, including the area occupied by buildings, the width level of roads, the coverage of vegetation, and the depth level of water bodies.

[0043] During the generation process, the surveying and mapping data are subject to graded quality control. First, the geometric accuracy is checked to control the plane position error within 0.2 meters and the elevation error within 0.1 meters. Then the attribute integrity check is carried out to ensure that all land features have complete spatial information and attribute information. Finally, the map symbol standardization is checked to strictly implement the diagrammatic regulations in the 1:500 topographic map surveying and mapping specifications. The generated surveying and mapping data is stored in a hierarchical structure, with each layer containing corresponding spatial data and attribute data, supporting bidirectional conversion between vector and raster formats, and establishing a data update mechanism to record the generation time, update history, and quality assessment results of surveying and mapping data. The surveying and mapping results meet the requirements of surveying and mapping industry standards and can be directly applied to engineering planning, design, and construction.

[0044] Furthermore, this embodiment also provides a geographic information surveying and mapping data processing system based on image analysis, comprising: An acquisition module is used to acquire multi-angle satellite remote sensing images and divide the multi-angle satellite remote sensing images into a plurality of image blocks according to a geographic coordinate system; A feature extraction module, used to extract terrain feature parameters of the image block and establish a feature vector of the terrain feature parameters; A ground object classification module is used to establish a ground object classification model according to the feature vector to classify the image block and obtain a ground object classification label; A boundary point marking module is used to establish a ground object classification model according to the feature vector to classify the ground objects in the image block and obtain a ground object classification label; A surveying and mapping data generation module is used to generate surveying and mapping data based on the three-dimensional geographic information model and the boundary points of the land objects.

[0045] This embodiment also provides a computer device, which is suitable for the case of a geographic information surveying and mapping data processing method based on image analysis, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the geographic information surveying and mapping data processing method based on image analysis proposed in the above embodiment.

[0046] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0047] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for processing geographic information surveying and mapping data based on image analysis as proposed in the above embodiment is implemented.

[0048] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0049] Example 2 This is the second embodiment of the present invention. This embodiment provides a geographic information surveying and mapping data processing method based on image analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0050] A 3 square kilometer area in the central area of ​​a city was selected as the study area, and three low-orbit remote sensing satellites (satellite numbers are RS-101, RS-102, and RS-103) were used to collect image data from September 15 to October 15, 2023. The satellite orbit altitude is 480 kilometers, the spatial resolution is better than 0.5 meters, and the sampling period is 3 days. The acquisition period is selected in the time window with clear weather and good atmospheric visibility to ensure image quality. The study area includes typical urban buildings, road networks, park green spaces, and river systems. The terrain undulations are obvious, and the maximum height difference reaches 76 meters.

[0051] First, according to step S1 of the invention, remote sensing images are collected at vertical viewing angles, 45-degree oblique viewing angles in the east-west direction, and 45-degree oblique viewing angles in the north-south direction. Customized image processing software is used to divide the study area into 12 standard image blocks of 500 meters by 500 meters, and a 100-meter overlap zone is set between adjacent image blocks. In the image preprocessing stage, atmospheric correction, geometric correction, and radiation calibration are performed to ensure that the registration accuracy of multi-phase and multi-angle images is better than 0.3 pixels.

[0052] Then, according to step S2, the elevation value, slope value and texture feature value of each image block are calculated respectively using the improved feature extraction algorithm. In the feature extraction process, an adaptive calculation window of 7×7 pixels is used, the smoothing coefficient is set to 0.15, and the elevation influence factor is 0.08. When extracting texture features, the pixel spacing is set to 4, and the direction angles include four directions: 0°, 45°, 90°, and 135°. Finally, a 15-dimensional feature vector is obtained, in which the elevation feature weight coefficient is 0.4, the slope feature weight coefficient is 0.3, and the texture feature weight coefficient is 0.3.

[0053] Next, according to step S3, a multi-scale convolutional neural network is constructed and trained. The network training adopts a batch size of 64, an initial learning rate of 0.001, an Adam optimizer, and 200 training rounds. During the model training process, the sample difficulty coefficient is dynamically adjusted through cross-validation, the spatial distance weight is set to 0.6, and the normalization factor is taken as 0.1.

[0054] In step S4, when constructing the three-dimensional geographic information model, the grid reference elevation is set to 50 meters above sea level, and an adaptive grid generation strategy is adopted to encrypt grid nodes in areas with drastic terrain changes. The threshold of the boundary response function is set to 0.25, the edge smoothing factor is set to 0.18, and the convergence threshold of the iterative least squares method is set to 0.001.

[0055] Finally, in step S5, the surveying and mapping data with a scale of 1:500 is generated, using 24-bit true color mode, a resolution of 300dpi, and a hierarchical structure for data output, supporting mainstream formats such as DWG and SHP. The experimental data shown in Table 1 are obtained: Table 1 Experimental data table Sample area Plane position accuracy (m) Elevation accuracy (m) Boundary recognition accuracy (%) Overall classification accuracy (%) Processing time (min) Data storage capacity (GB) Traditional method A 0.45 0.32 82.5 85.7 156 2.8 Traditional method B 0.38 0.28 84.3 87.2 142 2.5 Method 1 of the present invention 0.21 0.15 95.8 94.3 98 1.9 Method 2 of the present invention 0.19 0.13 96.2 95.1 95 1.8 Method 3 of the present invention 0.18 0.12 96.5 95.4 92 1.7 Method 4 of the present invention 0.17 0.11 97.1 95.8 90 1.7 By comparing the test data, it can be found that the method of the present invention has significant advantages over the traditional method in multiple key indicators: The plane position accuracy is improved from 0.38-0.45 meters of the traditional method to 0.17-0.21 meters, an increase of 55%; The elevation accuracy has been improved from 0.28-0.32 meters of the traditional method to 0.11-0.15 meters, an increase of 60%; The accuracy of boundary recognition increased from 82.5-84.3% to 95.8-97.1%, an increase of more than 13 percentage points; The overall classification accuracy increased from 85.7-87.2% to 94.3-95.8%, an increase of 9 percentage points.

[0056] The processing time is reduced from 142-156 minutes of the traditional method to 90-98 minutes, and the efficiency is improved by about 37%; The data storage capacity is reduced from 2.5-2.8GB to 1.7-1.9GB, and the storage efficiency is improved by about 32%; It is particularly noteworthy that the method of the present invention has the most significant improvement in boundary recognition accuracy, which solves the key technical problem of traditional methods in complex terrain boundary recognition. At the same time, the improvement in processing efficiency and the reduction in storage volume also reflect the engineering value of the present invention in practical applications.

[0057] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A geographic information surveying and mapping data processing method based on image analysis, characterized in that: include: Collecting multi-angle satellite remote sensing images, and dividing the multi-angle satellite remote sensing images into a plurality of image blocks according to a geographic coordinate system; Extracting terrain characteristic parameters of the image block and establishing a characteristic vector of the terrain characteristic parameters; According to the feature vector, a ground object classification model is established to classify the ground objects in the image block to obtain a ground object classification label; Constructing a three-dimensional geographic information model based on the object classification labels and the terrain feature parameters, and marking the object boundary points; Generate surveying and mapping data based on the three-dimensional geographic information model and the boundary points of the land objects.

2. The method for processing geographic information surveying and mapping data based on image analysis according to claim 1, characterized in that: The terrain characteristic parameters include elevation value, slope value and texture characteristic value; Combining the elevation value, the slope value and the texture feature value to construct the feature vector; The calculation of the elevation value is shown in the following formula: ， in, Represents the coordinate points in the image The gray value at is the adaptive kernel function, is the calculation window, is the smoothing coefficient, represents the Laplace operator, is the coordinate point The elevation value at .

3. The method for processing geographic information surveying and mapping data based on image analysis according to claim 2, characterized in that: The slope value is calculated based on the spatial change rate of the elevation value, as shown in the following formula: ， in, and They are Direction and The elevation gradient of the direction, is the slope correction factor, is the elevation influencing factor, Indicates coordinate points The slope value at ; The texture feature value is extracted using the gray level co-occurrence matrix, as shown in the following formula: ， in, is the position in the gray-level co-occurrence matrix The probability value of is the pixel pitch, is the direction angle, To avoid small constants whose logarithms are zero, is the distance weight function, is the direction weight function, Indicates a specific distance and direction The texture feature values ​​under .

4. The method for processing geographic information surveying and mapping data based on image analysis according to claim 1, characterized in that: Obtaining the object classification label includes: Calculate the weights of features at different scales through the attention mechanism; The features of different branches of the object classification model are weighted and fused with the weights of the features of different scales obtained by the attention mechanism to obtain a comprehensive feature vector; The object classification label is output according to the comprehensive feature vector, and the object classification model is optimized through a loss function.

5. The method for processing geographic information surveying and mapping data based on image analysis according to claim 4, characterized in that: The classification confidence of the object classification label is calculated by the following formula: ， in, Indicates the current feature category. The current feature category The classification weights, The current feature category The bias term, is the comprehensive feature vector, For the The bias term of each category, is the weight matrix of the i-th category; The loss function is expressed by the following formula: ， in, is the true label, is the predicted probability, is the sample difficulty coefficient, is the balance factor, Represents the final loss function value.

6. The method for processing geographic information surveying and mapping data based on image analysis according to claim 5, characterized in that: The construction of the three-dimensional geographic information model includes: Extracting the boundary features of the object through a bidirectional recursive neural network according to the object classification label and the terrain feature parameters; Differentiated boundary transition zones are set for different types of land objects, and a three-dimensional grid model based on boundary constraints is constructed based on the boundary characteristics of the land objects; In the transition zone area, the elevation value is smoothly transitioned by using a cubic spline interpolation algorithm, and the vertex positions of the three-dimensional model are optimized by an iterative least squares method to obtain the three-dimensional geographic information model; Obtaining the boundary points of the ground feature includes: The region segmentation algorithm and the adaptive boundary point extraction algorithm are used to calculate the initial boundary point coordinates; Optimizing and screening the coordinates of the initial boundary points; The retained boundary points are interpolated to obtain the boundary points of the feature and generate a continuous boundary curve.

7. The method for processing geographic information surveying and mapping data based on image analysis according to claim 6, characterized in that: The extraction of the boundary features of the ground object is expressed by the following formula: ， in, and are the forward and backward LSTM units, respectively. For location The classification characteristics of is the feature fusion operator, is the class adaptive weight, For location The feature value of the boundary of the object at ; The three-dimensional grid model is expressed by the following formula: ， in, are vertex coordinates, is the elevation gradient, is the boundary response function, is the edge smoothing factor, is the boundary feature of the vertex coordinates, It is a three-dimensional mesh model.

8. The method for processing geographic information surveying and mapping data based on image analysis according to claim 7, characterized in that: The calculation of the initial boundary point coordinates is shown in the following formula: ， in, is the boundary saliency measure, is the local structure weight, is the classification confidence, is the balance coefficient, is the three-dimensional coordinate of the boundary point; The interpolation of the reserved boundary points is expressed by the following formula: ， in, is the cubic B-spline basis function, is the tension control factor, is the continuous boundary curve equation, For the The coordinates of the boundary points, is a parameter variable.

9. A geographic information surveying and mapping data processing system based on image analysis, based on the geographic information surveying and mapping data processing method based on image analysis according to any one of claims 1 to 8, characterized in that: include: An acquisition module is used to acquire multi-angle satellite remote sensing images and divide the multi-angle satellite remote sensing images into a plurality of image blocks according to a geographic coordinate system; A feature extraction module, used to extract terrain feature parameters of the image block and establish a feature vector of the terrain feature parameters; A ground object classification module is used to establish a ground object classification model according to the feature vector to classify the image block and obtain a ground object classification label; A boundary point marking module is used to establish a ground object classification model according to the feature vector to classify the ground objects in the image block and obtain a ground object classification label; A surveying and mapping data generation module is used to generate surveying and mapping data based on the three-dimensional geographic information model and the boundary points of the land objects.

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