Geographic surveying and mapping data acquisition and processing system based on image analysis

Abstract

The invention relates to the technical field of geographic image analysis, and discloses a geographic surveying and mapping data acquisition and processing system based on image analysis. According to the system, geographic image data are acquired by applying an unmanned aerial vehicle, a satellite and ground sensor equipment through a data acquisition module, image denoising, geometric correction, radiation correction and image enhancement are performed on an acquired image through an algorithm formula by a data preprocessing module, and geographic features are extracted from the preprocessed image by a feature extraction module; comprising surface feature classification, edge detection and texture analysis, a data analysis module analyzes extracted features and calculates a geographic image area and a geographic image perimeter, and a data visualization module displays an analysis result in a map superposition, chart image and 3D visualization mode. The geographic image is analyzed, the geographic information is displayed in a charting form, the latest geographic change is reflected, and the feedback accuracy of the geographic information is improved.

Description

Technical Field

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

[0002] Geography is the science that studies the earth's surface and its environment, involving the interaction between the natural environment and human activities. Geographic information can help manage natural resources such as water, minerals, forests, etc. to ensure sustainable use. Through geographic mapping, environmental changes can be monitored, ecosystem health can be assessed, and protection measures can be formulated. Through mapping, spatial analysis can be carried out to reveal the spatial distribution and relationships of geographical phenomena. With the development of technologies such as remote sensing, drones and GIS, the accuracy and efficiency of geographic mapping are constantly improving.

[0003] Currently, the dynamic nature of geographic information requires that data be updated in a timely manner, but the data update frequency in many areas is low and cannot reflect the latest geographic changes, thus reducing the accuracy of geographic information feedback. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides a geographic surveying and mapping data acquisition and processing system based on image analysis, which has the advantages of using unmanned aerial vehicles, satellites and ground sensor equipment to collect geographic image data, including high-resolution geographic images, hyperspectral geographic images, lidar data, geographic image temperature and humidity data, a data preprocessing module to perform image denoising, geometric correction, radiation correction and image enhancement on the collected images through an algorithm formula, a feature extraction module to extract geographic features from the preprocessed images, including ground object classification, edge detection and texture analysis, a data analysis module to analyze the extracted features, calculate the geographic image area Txmj and the geographic image perimeter Txzc, a data visualization module to display the analysis results in the form of map overlays, chart images and 3D visualization, and continuously collects geographic information image data, analyzes the geographic images, displays the geographic information in a graphical form, reflects the latest geographic changes, improves the feedback accuracy of the geographic information, and solves the above problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a geographic surveying and mapping data acquisition and processing system based on image analysis, comprising a data acquisition module, a data preprocessing module, a feature extraction module, a data analysis module and a data visualization module;

[0008] The data acquisition module collects geographic image data, including high-resolution geographic images, hyperspectral geographic images, lidar data, geographic image temperature and humidity data, through unmanned aerial vehicle equipment, satellites and ground sensor equipment, and transmits the collected data to the data preprocessing module;

[0009] The data preprocessing module performs image denoising, geometric correction, radiation correction and image enhancement on the collected images through algorithm formulas, and transmits the processed images to the feature extraction module;

[0010] The feature extraction module extracts geographic features from the preprocessed image, including object classification, edge detection and texture analysis, and sends the extracted feature data to the data analysis module for calculation;

[0011] The data analysis module analyzes the extracted features and calculates the geographic image area Txmj and the geographic image perimeter Txzc. The above analysis and calculation results are transmitted to the data visualization module through the network;

[0012] The calculation formula of geographic image area Txmj is as follows:

[0013]

[0014] In the formula, Txmj represents the area of ​​the geographic image, (x a ,y a ) represents the coordinates of the polygon vertices, a ranges from 1 to d, d represents the number of vertices of the polygon, (x a+1 ,y a+1 ) is defined as the starting point (x1, y1) for closing the polygon;

[0015] The calculation formula of geographic image perimeter Txzc is as follows:

[0016]

[0017] In the formula, Txzc represents the perimeter of the geographic image, dt q represents the length of the qth edge, q=1 represents the starting index of the summation, starting from the first edge, and r represents the number of edges of the polygon;

[0018] The data visualization module displays the analysis results in the form of map overlays, chart images, and 3D visualization.

[0019] Preferably, the data preprocessing module performs image denoising on the collected image through an algorithm formula, and the denoising formula is as follows:

[0020]

[0021] In the formula, Ts(x,y) represents the pixel value of the denoised image at the (x,y) coordinate, T(x,y) represents the pixel value of the original image at the (x,y) coordinate, i represents the row index of the image pixel, j represents the column index of the image pixel, G(i,j) represents the Gaussian kernel, which defines the weight of each neighborhood pixel and is a two-dimensional Gaussian function, and k is the normalization constant.

[0022] Preferably, the data preprocessing module performs geometric correction on the collected image through an algorithm formula, and the correction formula is as follows:

[0023]

[0024] In the formula, (x, y) represents the original image coordinates, (x ′ ,y ′ ) represents the corrected image coordinates, a, b, c, d represent the parameters of the transformation matrix, which control the scaling, rotation, and shearing of the image, and (tx, ty) is the translation, which represents the movement of the image in the x and y directions.

[0025] Preferably, the data preprocessing module performs radiation correction on the collected image through an algorithm formula, and the radiation correction formula is as follows:

[0026] Fs(x,y)=α*T(x,y)+β

[0027] In the formula, Fs(x,y) represents the image after radiation correction, α represents the gain factor, β represents the offset, and T(x,y) represents the original image.

[0028] Preferably, the data preprocessing module performs image enhancement on the collected image through an algorithm formula, and the image enhancement formula is as follows:

[0029] Zq(x,y)=CDF(T(x,y))*(L-1)

[0030] In the formula, Zq(x,y) represents the enhanced image, CDF represents the cumulative distribution function, L represents the image grayscale level, and T(x,y) represents the original image.

[0031] Preferably, the feature extraction module extracts geographic features from the preprocessed image, and the classification feature extraction formula is as follows:

[0032]

[0033] In the formula, f(a) represents the output of the classification function, w is the weight vector set by the system, indicating the importance of each feature. represents the feature mapping function, which maps the input features to a high-dimensional space, and b represents the bias term.

[0034] Preferably, the feature extraction module extracts geographic features from the preprocessed image, and the edge detection feature extraction formula is as follows:

[0035]

[0036] In the formula, Ga represents the gradient amplitude of the image, which reflects the edge strength of the image, and G x Represents the gradient of the image in the x direction, G y Represents the gradient of the image in the y direction.

[0037] Preferably, the feature extraction module extracts geographic features from the preprocessed image, and the texture analysis feature extraction formula is as follows:

[0038]

[0039] In the formula, P(m,n) represents the joint probability of gray levels m and n in the gray-level co-occurrence matrix, δ(T(x,y),m,n) represents the indicator function, which returns 1 when the gray value of pixel T(x,y) is m and the gray value of the adjacent pixel is n, otherwise it returns 0, and Ω represents the set of all pixels in the image.

[0040] Compared with the prior art, the present invention provides a geographical surveying and mapping data acquisition and processing system based on image analysis, which has the following beneficial effects:

[0041] The present invention uses a data acquisition module to apply unmanned aerial vehicles, satellites and ground sensor equipment to collect geographic image data, including high-resolution geographic images, hyperspectral geographic images, laser radar data, geographic image temperature and humidity data. The data preprocessing module performs image denoising, geometric correction, radiation correction and image enhancement on the collected images through an algorithm formula. The feature extraction module extracts geographic features from the preprocessed images, including ground object classification, edge detection and texture analysis. The data analysis module analyzes the extracted features and calculates the geographic image area Txmj and the geographic image perimeter Txzc. The data visualization module displays the analysis results in the form of map overlays, chart images and 3D visualization. By continuously collecting geographic information image data and analyzing geographic images, the geographic information is displayed in a graphical form to reflect the latest geographic changes and improve the feedback accuracy of the geographic information. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The current dynamic nature of geographic information requires timely data updates, but the data update frequency in many areas is low and cannot reflect the latest geographic changes, thus reducing the accuracy of geographic information feedback. Therefore, a geographic surveying and mapping data acquisition and processing system based on image analysis is proposed. Figure 1 ,The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a data analysis module, and a data visualization module;

[0045] The data acquisition module plays a vital role in modern geographic information systems. It obtains high-quality geographic image data through a variety of advanced technical means, including:

[0046] Drones are equipped with high-resolution cameras that can obtain detailed geographic images at lower flight altitudes. These images can be used for applications such as urban planning, agricultural monitoring and environmental assessment. Drones transmit the collected data to ground control stations in real time via wireless networks to ensure the timeliness and effectiveness of the data.

[0047] Satellite remote sensing technology can obtain hyperspectral images covering multiple bands from visible light to near infrared. These data can be used for land cover classification, vegetation health monitoring and water quality assessment. Satellites can cover a wide geographical area and are suitable for large-scale environmental monitoring and disaster assessment.

[0048] LiDAR technology can generate high-precision three-dimensional terrain models by emitting laser pulses and measuring their return time, which is of great significance in fields such as forest resource surveys, urban modeling and archaeological research;

[0049] Ground sensors can monitor temperature and humidity data in real time in geographic images, which are essential for climate change research, agricultural irrigation management, and ecosystem monitoring;

[0050] The collected data is transmitted to the data preprocessing module through a secure communication protocol, where the data will be processed by denoising, correction and format conversion to ensure the quality and consistency of the data;

[0051] The data preprocessing module performs image denoising, geometric correction, radiation correction and image enhancement on the collected images through algorithm formulas, where:

[0052] The image denoising formula is as follows:

[0053]

[0054] Denoising can eliminate random noise in an image, making the image clearer and the details more obvious, which is crucial for subsequent analysis and processing. In the formula, Ts(x,y) represents the pixel value of the denoised image at the (x,y) coordinate, T(x,y) represents the pixel value of the original image at the (x,y) coordinate, i represents the row index of the image pixel, j represents the column index of the image pixel, G(i,j) represents the Gaussian kernel, which defines the weight of each neighborhood pixel and is a two-dimensional Gaussian function. k is a normalization constant. In geographic surveying and mapping, feature extraction is an important analysis step. Denoising can improve the effect of the feature extraction algorithm, making the boundary of the object more obvious, and facilitating identification and classification.

[0055] The geometric correction formula is as follows:

[0056]

[0057] Geometric correction can eliminate geometric distortion caused by shooting angle, terrain undulations, and sensor characteristics, ensuring that the actual position of the object in the image is consistent with its position on the earth's surface. In the formula, (x, y) represents the original image coordinates, (x ′ ,y ′ ) represents the coordinates of the corrected image, a, b, c, d represent the parameters of the transformation matrix, which control the scaling, rotation and shearing of the image, (tx, ty) is the translation, which represents the movement of the image in the x and y directions. The corrected image can be aligned with other geographic data in the same coordinate system, which is convenient for spatial analysis and data fusion;

[0058] The radiation correction formula is as follows:

[0059] Fs(x,y)=α*T(x,y)+β

[0060] Radiation correction can eliminate radiation differences caused by sensor characteristics and shooting conditions such as lighting and weather, so that images acquired at different times and under different conditions have consistent radiation characteristics. In the formula, Fs(x,y) represents the image after radiation correction, α represents the gain factor, β represents the offset, and T(x,y) represents the original image. The corrected image can be effectively compared with data from different sensors or platforms to ensure the reliability of the analysis results.

[0061] The image enhancement formula is as follows:

[0062] Zq(x,y)=CDF(T(x,y))*(L-1)

[0063] Image enhancement can improve the performance of feature extraction algorithms, making features such as edges, textures, and shapes clearer, thereby improving the accuracy of object classification and recognition. In the formula, Zq(x, y) represents the enhanced image, CDF represents the cumulative distribution function, L represents the image grayscale, and T(x, y) represents the original image. By removing background noise and unnecessary details, the enhanced image can better highlight the target features and reduce the false detection rate.

[0064] The feature extraction module extracts geographic features from the preprocessed image, including object classification, edge detection and texture analysis, where:

[0065] The classification feature extraction formula is as follows:

[0066]

[0067] Classification feature extraction can effectively integrate data from different sensors or platforms to provide more comprehensive geographic information. In the formula, f(a) represents the output of the classification function, and w is the weight vector set by the system, indicating the importance of each feature. represents the feature mapping function, which maps the input features to a high-dimensional space, and b represents the bias term. By extracting unified features, it can ensure the consistency of data from different sources in the analysis and enhance the comparability of the data;

[0068] The edge detection feature extraction formula is as follows:

[0069]

[0070] Edge detection can clearly define the boundaries of objects, making the boundaries between different types of objects such as buildings, roads, and vegetation more obvious, thereby improving the accuracy of recognition. In the formula, Ga represents the gradient amplitude of the image, reflecting the edge strength of the image, and G x Represents the gradient of the image in the x direction, G y Represents the gradient of the image in the y direction. The clear edge information provides a basis for subsequent image analysis such as shape analysis and region segmentation, enhancing the depth and breadth of the analysis.

[0071] The texture analysis feature extraction formula is as follows:

[0072]

[0073] Texture analysis can capture subtle differences in the surface of objects, such as different types of vegetation, soil, and building materials, thereby improving the accuracy of classification. In the formula, P(m,n) represents the joint probability of gray levels m and n in the gray level co-occurrence matrix, δ(T(x,y),m,n) represents the indicator function, which returns 1 when the gray value of pixel T(x,y) is m and the gray value of the adjacent pixel is n, otherwise it returns 0, and Ω represents the set of all pixels in the image. The extraction of texture features provides basic data for subsequent analysis, simplifies the analysis process, and saves time and resources;

[0074] The data analysis module analyzes the extracted features and calculates the geographic image area Txmj and the geographic image perimeter Txzc, where:

[0075] The formula for calculating the area of ​​geographic images is as follows:

[0076]

[0077] By calculating the area of ​​geographic images, we can accurately quantify the coverage of land features such as water bodies, forests, and farmland, providing a reliable data basis for subsequent analysis. In the formula, Txmj represents the area of ​​the geographic image, (x a ,y a ) represents the coordinates of the polygon vertices, a ranges from 1 to d, d represents the number of vertices of the polygon, (x a+1 ,y a+1 ) is defined as the starting point (x1, y1) and is used to close the polygon. Accurate area calculation helps to rationally manage and allocate resources and ensure sustainable use;

[0078] The formula for calculating the perimeter of a geographic image is as follows:

[0079]

[0080] The perimeter is related to the shape characteristics of the land object and can help analyze the efficiency and rationality of land use. In the formula, Txzc represents the perimeter of the geographic image, dt q represents the length of the qth edge, q=1 represents the starting index of the summation, starting from the first edge, and r represents the number of edges of the polygon. By calculating the perimeter of different landforms, the form and distribution of land use can be analyzed, providing a basis for urban planning and land management;

[0081] The data visualization module plays a vital role in the geographic surveying and mapping data acquisition and processing system. It displays the analysis results in a variety of ways, such as map overlay, chart images and 3D visualization. Map overlay technology can effectively overlay different types of spatial data on the same map, such as land use, terrain changes, ecological environment data, etc. Through layer management, users can selectively display different data sets to facilitate spatial analysis and decision-making;

[0082] Through chart display, in-depth statistical analysis of data information can be performed, such as the distribution of land feature categories, time series changes, etc., to help users identify potential patterns and problems;

[0083] Using three-dimensional visualization technology, the module can vividly display geographic spatial features, such as the ups and downs of terrain and the three-dimensional structure of buildings, so that users can have a more intuitive understanding of spatial layout and structure.

[0084] The geographic surveying and mapping data acquisition and processing system based on image analysis, which is constructed through the combination of the above modules, continuously collects geographic information image data, analyzes the geographic images, and displays the geographic information in a graphical form to reflect the latest geographic changes and improve the feedback accuracy of geographic information.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A geographic surveying and mapping data acquisition and processing system based on image analysis, characterized in that: It includes data acquisition module, data preprocessing module, feature extraction module, data analysis module and data visualization module; The data acquisition module collects geographic image data, including high-resolution geographic images, hyperspectral geographic images, lidar data, geographic image temperature and humidity data, through unmanned aerial vehicle equipment, satellites and ground sensor equipment, and transmits the collected data to the data preprocessing module; The data preprocessing module performs image denoising, geometric correction, radiation correction and image enhancement on the collected images through algorithm formulas, and transmits the processed images to the feature extraction module; The feature extraction module extracts geographic features from the preprocessed image, including object classification, edge detection and texture analysis, and sends the extracted feature data to the data analysis module for calculation; The data analysis module analyzes the extracted features and calculates the geographic image area Txmj and the geographic image perimeter Txzc. The above analysis and calculation results are transmitted to the data visualization module through the network; The calculation formula of geographic image area Txmj is as follows: In the formula, Txmj represents the area of ​​the geographic image, (x a ,y a ) represents the coordinates of the polygon vertices, a ranges from 1 to d, d represents the number of vertices of the polygon, (x a+1 ,y a+1 ) is defined as the starting point (x1, y1) for closing the polygon; The calculation formula of geographic image perimeter Txzc is as follows: In the formula, Txzc represents the perimeter of the geographic image, dt q represents the length of the qth edge, q=1 represents the starting index of the summation, starting from the first edge, and r represents the number of edges of the polygon; The data visualization module displays the analysis results in the form of map overlays, chart images, and 3D visualization.

2. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 1, characterized in that: The data preprocessing module performs image denoising on the collected image through an algorithm formula, and the denoising formula is as follows: In the formula, Ts(x, y) represents the pixel value of the denoised image at the (x, y) coordinate, T(x, y) represents the pixel value of the original image at the (x, y) coordinate, i represents the row index of the image pixel, j represents the column index of the image pixel, G(i, j) represents the Gaussian kernel, defines the weight of each neighborhood pixel, is a two-dimensional Gaussian function, and k is the normalization constant.

3. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 2 is characterized in that: The data preprocessing module performs geometric correction on the collected image through an algorithm formula, and the correction formula is as follows: In the formula, (x, y) represents the original image coordinates, (x′, y′) represents the corrected image coordinates, a, b, c, d represent the parameters of the transformation matrix, which control the scaling, rotation, and shearing of the image, and (tx, ty) is the translation, which represents the movement of the image in the x and y directions.

4. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 3 is characterized by: The data preprocessing module performs radiation correction on the collected image through an algorithm formula. The radiation correction formula is as follows: Fs(x,y)=α*T(x,y)+β In the formula, Fs(x, y) represents the image after radiation correction, α represents the gain factor, β represents the offset, and T(x, y) represents the original image.

5. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 4 is characterized in that: The data preprocessing module performs image enhancement on the collected image through an algorithm formula, and the image enhancement formula is as follows: Zq(x,y)=CDF(T(x,y))*(L-1) In the formula, Zq(x, y) represents the enhanced image, CDF represents the cumulative distribution function, L represents the image grayscale level, and T(x, y) represents the original image.

6. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 5 is characterized by: The feature extraction module extracts geographic features from the preprocessed image. The classification feature extraction formula is as follows: In the formula, f(a) represents the output of the classification function, w is the weight vector set by the system, indicating the importance of each feature. represents the feature mapping function, which maps the input features to a high-dimensional space, and b represents the bias term.

7. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 6 is characterized by: The feature extraction module extracts geographic features from the preprocessed image. The edge detection feature extraction formula is as follows: In the formula, Ga represents the gradient amplitude of the image, which reflects the edge strength of the image, and G x Represents the gradient of the image in the x direction, G y Represents the gradient of the image in the y direction.

8. The geographic surveying and mapping data acquisition and processing system based on image analysis according to claim 7 is characterized by: The feature extraction module extracts geographic features from the preprocessed image. The texture analysis feature extraction formula is as follows: In the formula, P(m, n) represents the joint probability of gray levels m and n in the gray level co-occurrence matrix, δ(T(x, y), m, n) represents the indicator function, which returns 1 when the gray value of pixel T(x, y) is m and the gray value of the adjacent pixel is n, otherwise it returns 0, and Ω represents the set of all pixels in the image.

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