CN117456388B - Geological measurement and remote sensing data fusion management system based on unmanned aerial vehicle - Google Patents
Geological measurement and remote sensing data fusion management system based on unmanned aerial vehicle Download PDFInfo
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Abstract
本发明涉及地质测量和遥感技术领域，用于解决现有的地质测量中，无法将无人机和遥感技术相结合，导致地质测量方式缺乏完整的数据处理流程和综合分析能力，且传统的数据处理方法往往依赖于人工判断和处理，容易出现主观性和不一致性的问题，具体为基于无人机的地质测量与遥感数据融合管理系统，包括数据采集单元、云数据库、无人机地质测量分析单元、遥感地质测量分析单元、数据融合单元和显示终端。本发明，将无人机和遥感技术所获取的地质特征信息进行融合，充分利用无人机和遥感两种技术的优势，实现对地表地貌的全面分析，做到地质测量工作的高效、精确和可持续发展，为资源勘探、环境监测提供更好的支持和服务。
The present invention relates to the field of geological survey and remote sensing technology, and is used to solve the problem that in the existing geological survey, unmanned aerial vehicles and remote sensing technologies cannot be combined, resulting in the lack of complete data processing flow and comprehensive analysis capabilities in the geological survey method, and the traditional data processing method often relies on manual judgment and processing, which is prone to subjectivity and inconsistency. Specifically, it is a geological survey and remote sensing data fusion management system based on unmanned aerial vehicles, including a data acquisition unit, a cloud database, an unmanned aerial vehicle geological survey analysis unit, a remote sensing geological survey analysis unit, a data fusion unit and a display terminal. The present invention integrates the geological feature information obtained by unmanned aerial vehicles and remote sensing technology, fully utilizes the advantages of the two technologies of unmanned aerial vehicles and remote sensing, realizes a comprehensive analysis of the surface landform, and achieves efficient, accurate and sustainable development of geological survey work, and provides better support and services for resource exploration and environmental monitoring.
Description
Technical Field
The invention relates to the technical field of geological measurement and remote sensing, in particular to a geological measurement and remote sensing data fusion management system based on an unmanned aerial vehicle.
Background
In the fields of geological measurement and remote sensing, obtaining accurate surface and landform characteristic information is important to aspects such as environment monitoring, resource exploration, geological disaster early warning and the like. Conventional geological survey methods typically consume a significant amount of time and effort, and are difficult to obtain comprehensive surface information. While the rapid development of unmanned aerial vehicles and remote sensing technology has brought new opportunities and challenges for geologic measurements.
The unmanned aerial vehicle is used as a flexible and efficient data acquisition tool, and can rapidly acquire ground surface images and data. The remote sensing technology can acquire more detail and characteristic information through devices such as a thermal infrared sensor, a radar and the like. However, a problem exists at present how to combine unmanned aerial vehicles with remote sensing technology to achieve efficient fusion and analysis of geologic survey data. Most of the existing geological measurement and remote sensing data fusion management systems lack complete data processing flow and comprehensive analysis capability. Traditional data processing methods often rely on manual judgment and processing, and subjectivity and inconsistency are prone to occur.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
The invention aims to provide a geological measurement and remote sensing data fusion management system based on an unmanned aerial vehicle.
The aim of the invention can be achieved by the following technical scheme: the unmanned aerial vehicle-based geological measurement and remote sensing data fusion management system comprises a data acquisition unit, a cloud database, an unmanned aerial vehicle geological measurement analysis unit, a remote sensing geological measurement analysis unit, a data fusion unit and a display terminal;
The data acquisition unit is used for acquiring image information of the ground surface through the unmanned aerial vehicle and a remote sensing technology and sending the image information to the cloud database for storage;
The cloud database is also used for storing a texture feature comparison table, a first surface relief feature comparison table, a second surface relief feature comparison table, a third surface relief feature comparison table and a fourth surface relief feature comparison table;
The unmanned aerial vehicle geological survey analysis unit shoots earth surface images acquired by earth surface based on cameras carried by the unmanned aerial vehicle, performs geological data feature extraction operation, performs image preprocessing on the earth surface images, performs landform feature data extraction processing on the preprocessed images after the earth surface images are preprocessed, and obtains earth surface texture feature information, earth surface edge information, earth surface object identification information, segmentation information, relief degree, slope data and gradient of topography corresponding to the unmanned aerial vehicle shooting;
The remote sensing geological measurement analysis unit acquires an earth surface thermal infrared image based on a thermal infrared sensor carried by the unmanned aerial vehicle, and accordingly judges and analyzes an underground path and an underground hot spot on the earth surface, and accordingly outputs the flowing direction and path of underground water and the position and size of the underground hot spot;
the remote sensing geological measurement analysis unit is also used for acquiring radar images of the earth surface through a remote sensing technology, and performing geological remote sensing data characteristic extraction operation according to the radar images, so as to output earth surface texture information corresponding to the radar technology;
the data fusion unit is used for carrying out data fusion on various geological feature information corresponding to the shooting of the unmanned aerial vehicle and various geological feature information corresponding to the remote sensing technology, so that final landform feature information of the ground surface is output, and display description is carried out through the display terminal.
Preferably, the geological data feature extraction operation comprises the following specific analysis process:
Shooting the earth surface through a camera mounted on the unmanned aerial vehicle, thereby obtaining an earth surface image of the target object, and performing image preprocessing on the earth surface image, thereby completing noise removal, geometric distortion correction and image brightness and contrast adjustment of the earth surface image;
After the surface image is preprocessed, performing landform feature data extraction processing on the preprocessed image, and obtaining surface texture feature information, surface edge information, surface object identification information and segmentation information corresponding to the shooting of the unmanned aerial vehicle, thereby completing texture feature extraction, edge detection, object identification and segmentation in feature extraction of the surface image corresponding to the shooting of the unmanned aerial vehicle;
calculating elevation information of the earth surface according to the earth surface texture characteristic information, the earth surface edge information, the earth surface object identification information and the segmentation information, and registering the earth surface image with corresponding elevation data to obtain the earth surface elevation information of each pixel point;
According to the ground elevation information, calculating the standard deviation of elevation values in the field of each pixel point, taking the standard deviation as the fluctuation degree of the terrain, calculating the elevation change rate of each pixel point in the horizontal and vertical directions, taking the elevation change rate as the gradient of the terrain, calculating the elevation gradient of each pixel point in the horizontal and vertical directions, converting the elevation gradient into azimuth angle, and taking the azimuth angle as slope data for judging the terrain.
Preferably, the image preprocessing is performed on the surface image, and the specific analysis process is as follows:
Dividing the ground surface image into a plurality of sub-image pictures according to time sequence and other frames;
Calculating the median value of the neighborhood around the pixel of each sub-image picture by using a median filter, thereby smoothing each sub-image picture and completing the noise removal of the surface image;
Obtaining a calibration plate of a camera mounted on the unmanned aerial vehicle, obtaining a camera model and distortion parameters through the calibration plate, calculating the distortion corrected coordinates of each pixel point, carrying out interpolation processing on each corresponding sub-image picture according to the distortion corrected coordinates, thereby obtaining corrected pixel values, and assigning the corrected pixel values to a new image, thereby completing distortion correction of a plurality of sub-image pictures, namely restoring an object in an earth surface image to a real geometric shape;
And (3) redistributing the gray value of each sub-image picture pixel by using histogram equalization, thereby enhancing the contrast and brightness of each sub-image picture, namely finishing the brightness and contrast adjustment of the surface image.
Preferably, the extracting process of the geomorphic feature data is performed on the preprocessed image, and the specific analysis process is as follows:
Calculating and analyzing gray level co-occurrence matrixes among pixels of the preprocessed image in different directions by using a gray level co-occurrence matrix method, so as to obtain a statistical feature parameter set of the preprocessed image, wherein the statistical feature parameter set comprises contrast, energy and entropy, and the statistical feature parameter set is subjected to comparison matching analysis with a texture feature comparison table stored in a cloud database, so that texture feature information of the ground surface image is output, the texture feature information comprises texture roughness and texture directions, and each of the output statistical feature parameter sets is provided with one texture feature information corresponding to the texture feature information;
Calculating the gradient amplitude and the gradient direction of each pixel point in the preprocessed image, determining the edge position in the preprocessed image, comparing two adjacent pixels in the gradient direction of each pixel point, reserving the pixel with the largest gradient value, obtaining a thinner edge, setting a low threshold value and a high threshold value, and considering the pixel point as a strong edge when the gradient value of the pixel point is larger than the high threshold value; when the gradient value of the pixel point is between the low threshold value and the high threshold value, the pixel point is regarded as a weak edge; when the gradient value of the pixel point is smaller than the low threshold value, the pixel point is regarded as a non-edge, the strong edge is reserved, and the weak edge is connected, so that the final edge information of the earth surface is obtained;
And dividing and identifying different objects or landform units in the preprocessed image through an image dividing and target identifying algorithm, so as to obtain object identifying information and dividing information of the ground surface.
Preferably, the determination analysis of the underground path and the underground hot spot is performed on the surface, and the specific analysis process is as follows:
acquiring surface temperature distribution data through a thermal infrared sensor carried by an unmanned aerial vehicle, thereby obtaining a thermal infrared image of the surface;
Analyzing the groundwater flow path, selecting a section of longer area from the thermal infrared image, calculating the temperature value of each pixel point in the area and the temperature values of surrounding pixel points, respectively marking the temperature value as a first temperature value and a second temperature value, if the first temperature value of the pixel point is higher than the second temperature value of the surrounding pixel point, indicating that the pixel point is positioned on the groundwater flow path, marking the pixel point as a groundwater flow path mark, and thus obtaining the direction and the path of groundwater flow;
Extracting underground hot spot information, selecting a region with the area smaller than V1 from the thermal infrared image, calculating the average temperature value of pixel points in each region, if the average temperature value of the region is higher than the average temperature value of surrounding regions, indicating that the region is an underground hot spot, marking the region as an underground hot spot mark, extracting the position and the size of the underground hot spot, and obtaining the position and the size of the underground hot spot.
Preferably, the geological remote sensing data feature extraction operation comprises the following specific analysis process:
Acquiring information of the earth surface through a remote sensing technology, thereby obtaining a radar image;
According to the radar image, drawing a corresponding histogram, obtaining gray values corresponding to different pixels according to the histogram, calculating average gray values of all pixels according to the gray values, taking the average gray values as average reflection intensity of the measured radar image, and marking the average reflection intensity as fs;
Obtaining the distribution state of the pixel quantity of different gray levels according to the histogram, counting the pixel quantity corresponding to each gray level, taking the pixel quantity as the comprehensive reflection intensity of the measuring radar image, and marking the comprehensive reflection intensity as zrs;
calculating and analyzing the average reflection intensity and the comprehensive reflection intensity of the radar image, and according to a set data model: ry=1/2× (λ1×fs+λ2× zrs), thereby obtaining the surface reflection intensity ry corresponding to the radar technology, wherein λ1 and λ2 are both natural numbers greater than 0;
Dividing the radar image into a plurality of areas, and comparing the gray level changes of different areas in the radar image, thereby obtaining the surface texture information corresponding to the radar technology.
Preferably, the data fusion is performed on each item of geological feature information corresponding to the shooting of the unmanned aerial vehicle and each item of geological feature information corresponding to the remote sensing technology, and the specific process is as follows:
matching and analyzing the surface texture feature information, the surface edge information, the surface object identification information and the segmentation information corresponding to the unmanned aerial vehicle shooting with a first surface landform feature comparison table corresponding to the unmanned aerial vehicle data stored in a cloud database, thereby outputting first landform feature information in geological measurement;
matching and analyzing the relief degree, slope data and gradient of the topography corresponding to the unmanned aerial vehicle shooting with a second surface topography characteristic comparison table corresponding to the unmanned aerial vehicle data stored in the cloud database, thereby outputting second topography characteristic information in geological measurement;
Carrying out matching analysis on the direction and path of groundwater flowing, the position and size of an underground hot spot corresponding to the infrared sensor carried by the unmanned aerial vehicle and a third surface landform characteristic comparison table corresponding to radar technical data stored in a cloud database, thereby outputting third landform characteristic information in geological measurement;
Performing matching analysis on the surface texture information and the surface reflection intensity corresponding to the radar technology and a fourth surface landform feature comparison table corresponding to the radar technology data stored in the cloud database, thereby outputting fourth landform feature information in geological measurement;
The first, second, third and fourth topographical feature information are respectively given the weights of ρ1, ρ2, ρ3 and ρ4, and are weighted and averaged, so that the final topographical feature information of the earth surface is output;
wherein ρ1, ρ2, ρ3 and ρ4 are natural numbers greater than 0.
The invention has the beneficial effects that:
According to the invention, the unmanned aerial vehicle and the remote sensing technology are combined to realize rapid acquisition of surface images and data, the surface images are shot by using a camera carried by the unmanned aerial vehicle, and the image preprocessing and the landform characteristic data extraction processing are carried out to obtain accurate surface texture characteristic information, edge information, object identification information and landform data; acquiring an earth surface thermal infrared image by using a thermal infrared sensor, and judging and analyzing the underground path and the hot spot position; and (3) fusing various geological feature information corresponding to the unmanned aerial vehicle and the remote sensing technology, and outputting final surface landform feature information, so that comprehensive analysis of the surface landform is realized.
The geological feature information acquired by the unmanned aerial vehicle and the remote sensing technology is fused, the advantages of the unmanned aerial vehicle and the remote sensing technology are fully utilized, the accuracy of the surface and landform feature information is improved, the situation of the surface and landform can be more comprehensively known through comprehensively analyzing the information of different data sources, and more accurate data support is provided for the aspects of environment monitoring, resource exploration, geological disaster early warning and the like.
And displaying and explaining the final surface topography characteristic information through a display terminal, so that a user can intuitively know the condition of the surface topography. This provides convenient reference and analysis tools for decision makers, researchers, and related industries.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses a geological measurement and remote sensing data fusion management system based on an unmanned aerial vehicle, which comprises a data acquisition unit, a cloud database, an unmanned aerial vehicle geological measurement analysis unit, a remote sensing geological measurement analysis unit, a data fusion unit and a display terminal.
The data acquisition unit is used for acquiring the image information of the ground surface through the unmanned aerial vehicle and the remote sensing technology, and sending the image information to the cloud database for storage.
The cloud database is also used for storing a texture feature comparison table, a first surface relief feature comparison table, a second surface relief feature comparison table, a third surface relief feature comparison table and a fourth surface relief feature comparison table.
The unmanned aerial vehicle geological survey analysis unit shoots earth surface images acquired by earth surface based on cameras carried by the unmanned aerial vehicle, and performs geological data feature extraction operation, and the specific analysis process is as follows:
The ground surface is shot by a camera carried by the unmanned aerial vehicle, so that a ground surface image of a target object is obtained, and the ground surface image is subjected to image preprocessing, wherein the specific analysis process is as follows:
Dividing the ground surface image into a plurality of sub-image pictures according to time sequence and other frames;
Calculating the median value of the neighborhood around the pixel of each sub-image picture by using a median filter, thereby smoothing each sub-image picture and completing the noise removal of the surface image;
Obtaining a calibration plate of a camera mounted on the unmanned aerial vehicle, obtaining a camera model and distortion parameters through the calibration plate, calculating the distortion corrected coordinates of each pixel point, carrying out interpolation processing on each corresponding sub-image picture according to the distortion corrected coordinates, thereby obtaining corrected pixel values, and assigning the corrected pixel values to a new image, thereby completing distortion correction of a plurality of sub-image pictures, namely restoring an object in an earth surface image to a real geometric shape;
the gray value of each sub-image picture pixel is redistributed by utilizing histogram equalization, so that the contrast and the brightness of each sub-image picture are enhanced, and the brightness and the contrast adjustment of the surface image are completed;
thus, the noise removal, geometric distortion correction and image brightness and contrast adjustment of the ground surface image are completed;
after the surface image is preprocessed, the preprocessed image is subjected to the extraction processing of the geomorphic feature data, and the specific analysis process is as follows:
Calculating and analyzing gray level co-occurrence matrixes among pixels of the preprocessed image in different directions by using a gray level co-occurrence matrix method, so as to obtain a statistical feature parameter set of the preprocessed image, wherein the statistical feature parameter set comprises contrast, energy and entropy, and the statistical feature parameter set is subjected to comparison matching analysis with a texture feature comparison table stored in a cloud database, so that texture feature information of the ground surface image is output, the texture feature information comprises texture roughness and texture directions, and each of the output statistical feature parameter sets is provided with one texture feature information corresponding to the texture feature information;
It should be noted that the contrast reflects the degree of gray scale difference between adjacent pixels in the image, and the larger the contrast, the more pronounced the texture change in the image, and the higher the roughness of the image. Therefore, the roughness of an image can be determined by the magnitude of the contrast, and in general, the larger the contrast, the higher the roughness of the image. The energy is the sum of squares of the pixel gray level co-occurrence matrixes in different directions in the image, and the larger the energy is, the more uniform the texture of the image is, and the lower the roughness of the image is. Therefore, the roughness of the image can be determined by the amount of energy, and in general, the smaller the energy, the higher the roughness of the image. The entropy refers to the information entropy of the pixel gray level co-occurrence matrix in different directions in the image, and the larger the entropy is, the more complex the texture of the image is represented, and the stronger the directivity of the image is represented. Therefore, the directionality of an image can be determined by the magnitude of entropy, and in general, the larger the entropy is, the stronger the directionality of the image is. For example, if the contrast is large, the energy is small, the entropy is large, the texture of the image is rough, and the image has certain directivity;
Calculating the gradient amplitude and the gradient direction of each pixel point in the preprocessed image, determining the edge position in the preprocessed image, comparing two adjacent pixels in the gradient direction of each pixel point, reserving the pixel with the largest gradient value, obtaining a thinner edge, setting a low threshold value and a high threshold value, and considering the pixel point as a strong edge when the gradient value of the pixel point is larger than the high threshold value; when the gradient value of the pixel point is between the low threshold value and the high threshold value, the pixel point is regarded as a weak edge; when the gradient value of the pixel point is smaller than the low threshold value, the pixel point is regarded as a non-edge, the strong edge is reserved, and the weak edge is connected, so that the final edge information of the earth surface is obtained;
Dividing and identifying different objects or landform units in the preprocessed image through an image dividing and target identifying algorithm, so as to obtain object identifying information and dividing information of the ground surface;
In conclusion, the surface texture feature information, the surface edge information, the surface object identification information and the segmentation information corresponding to the unmanned aerial vehicle shooting are obtained, and therefore texture feature extraction, edge detection, object identification and segmentation in feature extraction of the surface image corresponding to the unmanned aerial vehicle shooting are completed;
calculating elevation information of the earth surface according to the earth surface texture characteristic information, the earth surface edge information, the earth surface object identification information and the segmentation information, and registering the earth surface image with corresponding elevation data to obtain the earth surface elevation information of each pixel point;
According to the ground elevation information, calculating the standard deviation of elevation values in the field of each pixel point, taking the standard deviation as the fluctuation degree of the terrain, calculating the elevation change rate of each pixel point in the horizontal and vertical directions, taking the elevation change rate as the gradient of the terrain, calculating the elevation gradient of each pixel point in the horizontal and vertical directions, converting the elevation gradient into azimuth angles, and taking the azimuth angles as slope data for judging the terrain;
According to the method, the surface texture characteristic information, the surface edge information, the surface object identification information, the segmentation information, the relief degree of the terrain, the slope data and the slope corresponding to the unmanned aerial vehicle shooting are obtained.
The remote sensing geological measurement analysis unit acquires an earth surface thermal infrared image based on a thermal infrared sensor carried by the unmanned aerial vehicle, and judges and analyzes an underground path and an underground hot spot on the earth surface, and the specific analysis process is as follows:
acquiring surface temperature distribution data through a thermal infrared sensor carried by an unmanned aerial vehicle, thereby obtaining a thermal infrared image of the surface;
Analyzing the groundwater flow path, selecting a section of longer area from the thermal infrared image, calculating the temperature value of each pixel point in the area and the temperature values of surrounding pixel points, respectively marking the temperature value as a first temperature value and a second temperature value, if the first temperature value of the pixel point is higher than the second temperature value of the surrounding pixel point, indicating that the pixel point is positioned on the groundwater flow path, marking the pixel point as a groundwater flow path mark, and thus obtaining the direction and the path of groundwater flow;
Extracting underground hot spot information, selecting a region with the area smaller than V1 from a thermal infrared image, calculating the average temperature value of pixel points in each region, if the average temperature value of the region is higher than that of surrounding regions, indicating that the region is an underground hot spot, marking the region as an underground hot spot mark, extracting the position and the size of the underground hot spot, and obtaining the position and the size of the underground hot spot;
and outputting the flowing direction and path of the underground water and the position and size of the underground hot spot.
The remote sensing geological measurement analysis unit is also used for acquiring radar images of the earth surface through a remote sensing technology, and extracting characteristics of geological remote sensing data, and the specific analysis process is as follows:
Acquiring information of the earth surface through a remote sensing technology, thereby obtaining a radar image;
According to the radar image, drawing a corresponding histogram, obtaining gray values corresponding to different pixels according to the histogram, calculating average gray values of all pixels according to the gray values, taking the average gray values as average reflection intensity of the measured radar image, and marking the average reflection intensity as fs;
Obtaining the distribution state of the pixel quantity of different gray levels according to the histogram, counting the pixel quantity corresponding to each gray level, taking the pixel quantity as the comprehensive reflection intensity of the measuring radar image, and marking the comprehensive reflection intensity as zrs;
Calculating and analyzing the average reflection intensity and the comprehensive reflection intensity of the radar image, and according to a set data model: ry=1/2× (λ1×fs+λ2× zrs), thereby obtaining the surface reflection intensity ry corresponding to the radar technology, wherein λ1 and λ2 are preset weight factor coefficients, λ1 and λ2 are natural numbers larger than 0, and the weight factor coefficients are used for balancing the duty ratio weight of each item of data in formula calculation, so that the accuracy of a calculation result is promoted;
Dividing the radar image into a plurality of areas, and comparing the gray level changes of different areas in the radar image, thereby obtaining the surface texture information corresponding to the radar technology.
The data fusion unit is used for carrying out data fusion on each item of geological feature information corresponding to the shooting of the unmanned aerial vehicle and each item of geological feature information corresponding to the remote sensing technology, and the specific process is as follows:
matching and analyzing the surface texture feature information, the surface edge information, the surface object identification information and the segmentation information corresponding to the unmanned aerial vehicle shooting with a first surface landform feature comparison table corresponding to the unmanned aerial vehicle data stored in a cloud database, thereby outputting first landform feature information in geological measurement;
matching and analyzing the relief degree, slope data and gradient of the topography corresponding to the unmanned aerial vehicle shooting with a second surface topography characteristic comparison table corresponding to the unmanned aerial vehicle data stored in the cloud database, thereby outputting second topography characteristic information in geological measurement;
Carrying out matching analysis on the direction and path of groundwater flowing, the position and size of an underground hot spot corresponding to the infrared sensor carried by the unmanned aerial vehicle and a third surface landform characteristic comparison table corresponding to radar technical data stored in a cloud database, thereby outputting third landform characteristic information in geological measurement;
Performing matching analysis on the surface texture information and the surface reflection intensity corresponding to the radar technology and a fourth surface landform feature comparison table corresponding to the radar technology data stored in the cloud database, thereby outputting fourth landform feature information in geological measurement;
The first, second, third and fourth topographical feature information are respectively given the weights of ρ1, ρ2, ρ3 and ρ4, and are weighted and averaged, so that the final topographical feature information of the earth surface is output;
Wherein ρ1, ρ2, ρ3 and ρ4 are natural numbers greater than 0;
and outputting the final landform characteristic information of the earth surface, and displaying and explaining through a display terminal.
When the method is used, the unmanned aerial vehicle and the remote sensing technology are combined to realize rapid acquisition of the ground surface image and data, the ground surface image is shot by using a camera carried by the unmanned aerial vehicle, and the ground surface texture characteristic information, the edge information, the object identification information and the topographic data are obtained accurately through image preprocessing and landform characteristic data extraction; acquiring an earth surface thermal infrared image by using a thermal infrared sensor, and judging and analyzing the underground path and the hot spot position; and (3) fusing various geological feature information corresponding to the unmanned aerial vehicle and the remote sensing technology, and outputting final surface landform feature information, so that comprehensive analysis of the surface landform is realized.
The geological feature information acquired by the unmanned aerial vehicle and the remote sensing technology is fused, the advantages of the unmanned aerial vehicle and the remote sensing technology are fully utilized, the accuracy of the surface and landform feature information is improved, the situation of the surface and landform can be more comprehensively known through comprehensively analyzing the information of different data sources, and more accurate data support is provided for the aspects of environment monitoring, resource exploration, geological disaster early warning and the like.
And displaying and explaining the final surface topography characteristic information through a display terminal, so that a user can intuitively know the condition of the surface topography. This provides convenient reference and analysis tools for decision makers, researchers, and related industries.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (6)
1. The unmanned aerial vehicle-based geological measurement and remote sensing data fusion management system is characterized by comprising a data acquisition unit, a cloud database, an unmanned aerial vehicle geological measurement analysis unit, a remote sensing geological measurement analysis unit, a data fusion unit and a display terminal;
The data acquisition unit is used for acquiring image information of the ground surface through the unmanned aerial vehicle and a remote sensing technology and sending the image information to the cloud database for storage;
The cloud database is also used for storing a texture feature comparison table, a first surface relief feature comparison table, a second surface relief feature comparison table, a third surface relief feature comparison table and a fourth surface relief feature comparison table;
The unmanned aerial vehicle geological survey analysis unit shoots earth surface images acquired by earth surface based on cameras carried by the unmanned aerial vehicle, performs geological data feature extraction operation, performs image preprocessing on the earth surface images, performs landform feature data extraction processing on the preprocessed images after the earth surface images are preprocessed, and obtains earth surface texture feature information, earth surface edge information, earth surface object identification information, segmentation information, relief degree, slope data and gradient of topography corresponding to the unmanned aerial vehicle shooting;
The remote sensing geological measurement analysis unit acquires an earth surface thermal infrared image based on a thermal infrared sensor carried by the unmanned aerial vehicle, and accordingly judges and analyzes an underground path and an underground hot spot on the earth surface, and accordingly outputs the flowing direction and path of underground water and the position and size of the underground hot spot;
the remote sensing geological measurement analysis unit is also used for acquiring radar images of the earth surface through a remote sensing technology, and performing geological remote sensing data characteristic extraction operation according to the radar images, so as to output earth surface texture information corresponding to the radar technology;
The data fusion unit is used for carrying out data fusion on various geological feature information corresponding to the shooting of the unmanned aerial vehicle and various geological feature information corresponding to the remote sensing technology, so that final landform feature information of the ground surface is output, and display description is carried out through the display terminal;
the geological remote sensing data feature extraction operation comprises the following specific analysis processes:
Acquiring information of the earth surface through a remote sensing technology, thereby obtaining a radar image;
According to the radar image, drawing a corresponding histogram, obtaining gray values corresponding to different pixels according to the histogram, calculating average gray values of all pixels according to the gray values, taking the average gray values as average reflection intensity of the measured radar image, and marking the average reflection intensity as fs;
Obtaining the distribution state of the pixel quantity of different gray levels according to the histogram, counting the pixel quantity corresponding to each gray level, taking the pixel quantity as the comprehensive reflection intensity of the measuring radar image, and marking the comprehensive reflection intensity as zrs;
calculating and analyzing the average reflection intensity and the comprehensive reflection intensity of the radar image, and according to a set data model: ry=1/2× (λ1×fs+λ2× zrs), thereby obtaining the surface reflection intensity ry corresponding to the radar technology, wherein λ1 and λ2 are both natural numbers greater than 0;
Dividing the radar image into a plurality of areas, and comparing the gray level changes of different areas in the radar image, thereby obtaining the surface texture information corresponding to the radar technology.
2. The unmanned aerial vehicle-based geological survey and remote sensing data fusion management system according to claim 1, wherein the geological data feature extraction operation comprises the following specific analysis processes:
Shooting the earth surface through a camera mounted on the unmanned aerial vehicle, thereby obtaining an earth surface image of the target object, and performing image preprocessing on the earth surface image, thereby completing noise removal, geometric distortion correction and image brightness and contrast adjustment of the earth surface image;
After the surface image is preprocessed, performing landform feature data extraction processing on the preprocessed image, and obtaining surface texture feature information, surface edge information, surface object identification information and segmentation information corresponding to the shooting of the unmanned aerial vehicle, thereby completing texture feature extraction, edge detection, object identification and segmentation in feature extraction of the surface image corresponding to the shooting of the unmanned aerial vehicle;
calculating elevation information of the earth surface according to the earth surface texture characteristic information, the earth surface edge information, the earth surface object identification information and the segmentation information, and registering the earth surface image with corresponding elevation data to obtain the earth surface elevation information of each pixel point;
According to the ground elevation information, calculating the standard deviation of elevation values in the field of each pixel point, taking the standard deviation as the fluctuation degree of the terrain, calculating the elevation change rate of each pixel point in the horizontal and vertical directions, taking the elevation change rate as the gradient of the terrain, calculating the elevation gradient of each pixel point in the horizontal and vertical directions, converting the elevation gradient into azimuth angle, and taking the azimuth angle as slope data for judging the terrain.
3. The unmanned aerial vehicle-based geological survey and remote sensing data fusion management system according to claim 2, wherein the image preprocessing is performed on the ground surface image, and the specific analysis process is as follows:
Dividing the ground surface image into a plurality of sub-image pictures according to time sequence and other frames;
Calculating the median value of the neighborhood around the pixel of each sub-image picture by using a median filter, thereby smoothing each sub-image picture and completing the noise removal of the surface image;
Obtaining a calibration plate of a camera mounted on the unmanned aerial vehicle, obtaining a camera model and distortion parameters through the calibration plate, calculating the distortion corrected coordinates of each pixel point, carrying out interpolation processing on each corresponding sub-image picture according to the distortion corrected coordinates, thereby obtaining corrected pixel values, and assigning the corrected pixel values to a new image, thereby completing distortion correction of a plurality of sub-image pictures, namely restoring an object in an earth surface image to a real geometric shape;
And (3) redistributing the gray value of each sub-image picture pixel by using histogram equalization, thereby enhancing the contrast and brightness of each sub-image picture, namely finishing the brightness and contrast adjustment of the surface image.
4. The unmanned aerial vehicle-based geological survey and remote sensing data fusion management system according to claim 2, wherein the preprocessing image is subjected to the extraction processing of the geomorphic feature data, and the specific analysis process is as follows:
Calculating and analyzing gray level co-occurrence matrixes among pixels of the preprocessed image in different directions by using a gray level co-occurrence matrix method, so as to obtain a statistical feature parameter set of the preprocessed image, wherein the statistical feature parameter set comprises contrast, energy and entropy, and the statistical feature parameter set is subjected to comparison matching analysis with a texture feature comparison table stored in a cloud database, so that texture feature information of the ground surface image is output, the texture feature information comprises texture roughness and texture directions, and each of the output statistical feature parameter sets is provided with one texture feature information corresponding to the texture feature information;
Calculating the gradient amplitude and the gradient direction of each pixel point in the preprocessed image, determining the edge position in the preprocessed image, comparing two adjacent pixels in the gradient direction of each pixel point, reserving the pixel with the largest gradient value, obtaining a thinner edge, setting a low threshold value and a high threshold value, and considering the pixel point as a strong edge when the gradient value of the pixel point is larger than the high threshold value; when the gradient value of the pixel point is between the low threshold value and the high threshold value, the pixel point is regarded as a weak edge; when the gradient value of the pixel point is smaller than the low threshold value, the pixel point is regarded as a non-edge, the strong edge is reserved, and the weak edge is connected, so that the final edge information of the earth surface is obtained;
And dividing and identifying different objects or landform units in the preprocessed image through an image dividing and target identifying algorithm, so as to obtain object identifying information and dividing information of the ground surface.
5. The unmanned aerial vehicle-based geological measurement and remote sensing data fusion management system according to claim 1, wherein the determination analysis of the underground path and the underground hot spot is performed on the earth surface, and the specific analysis process is as follows:
acquiring surface temperature distribution data through a thermal infrared sensor carried by an unmanned aerial vehicle, thereby obtaining a thermal infrared image of the surface;
Analyzing the groundwater flow path, selecting a section of longer area from the thermal infrared image, calculating the temperature value of each pixel point in the area and the temperature values of surrounding pixel points, respectively marking the temperature value as a first temperature value and a second temperature value, if the first temperature value of the pixel point is higher than the second temperature value of the surrounding pixel point, indicating that the pixel point is positioned on the groundwater flow path, marking the pixel point as a groundwater flow path mark, and thus obtaining the direction and the path of groundwater flow;
Extracting underground hot spot information, selecting a region with the area smaller than V1 from the thermal infrared image, calculating the average temperature value of pixel points in each region, if the average temperature value of the region is higher than the average temperature value of surrounding regions, indicating that the region is an underground hot spot, marking the region as an underground hot spot mark, extracting the position and the size of the underground hot spot, and obtaining the position and the size of the underground hot spot.
6. The unmanned aerial vehicle-based geological measurement and remote sensing data fusion management system according to claim 1, wherein the data fusion is performed on each item of geological feature information corresponding to the unmanned aerial vehicle shooting and each item of geological feature information corresponding to the remote sensing technology, and the specific process is as follows:
matching and analyzing the surface texture feature information, the surface edge information, the surface object identification information and the segmentation information corresponding to the unmanned aerial vehicle shooting with a first surface landform feature comparison table corresponding to the unmanned aerial vehicle data stored in a cloud database, thereby outputting first landform feature information in geological measurement;
matching and analyzing the relief degree, slope data and gradient of the topography corresponding to the unmanned aerial vehicle shooting with a second surface topography characteristic comparison table corresponding to the unmanned aerial vehicle data stored in the cloud database, thereby outputting second topography characteristic information in geological measurement;
Carrying out matching analysis on the direction and path of groundwater flowing, the position and size of an underground hot spot corresponding to the infrared sensor carried by the unmanned aerial vehicle and a third surface landform characteristic comparison table corresponding to radar technical data stored in a cloud database, thereby outputting third landform characteristic information in geological measurement;
Performing matching analysis on the surface texture information and the surface reflection intensity corresponding to the radar technology and a fourth surface landform feature comparison table corresponding to the radar technology data stored in the cloud database, thereby outputting fourth landform feature information in geological measurement;
the first and second landform characteristic information the third and fourth topographical information are weighted by ρ1, ρ2, ρ3 and ρ4 respectively, and then the final geomorphic characteristic information of the earth surface is output by carrying out weighted average on the final geomorphic characteristic information.
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