A method and system for acquiring vector geographic information based on remote sensing imagery

Abstract

This invention discloses a vector geographic information acquisition method and system based on remote sensing imagery, relating to the technical field of remote sensing imagery. The system employs UAV remote sensing technology to continuously capture geographic image information of high mountains, achieving efficient coverage of complex terrain and improving the efficiency of geographic information acquisition. It also fully considers the real-time acquisition of relevant environmental and atmospheric situation data around the UAV as its location changes, providing multifaceted background information for subsequent processing. Considering various environmental factors during geographic image acquisition, the system calculates the visibility coefficient Njdx to further accurately assess the feasibility and usability of geographic image information acquisition. Furthermore, by comparing a preset visibility threshold Q with the visibility coefficient Njdx, the system automatically makes corresponding adjustment strategies to ensure data accuracy and stability during geographic image acquisition.

Description

Technical Field

[0001] This invention relates to the technical field of remote sensing imagery, specifically to a method and system for acquiring vector geographic information based on remote sensing imagery. Background Technology

[0002] Remote sensing technology, as a means of acquiring information about the Earth's surface, is widely used in agriculture, urban planning, and resource management. In geographic information science and technology, vector data is an important form for describing geographic phenomena and geospatial relationships. Therefore, vector geographic information acquisition systems based on remote sensing imagery have wide applications in spatial information processing and geographic data analysis. With the continuous development of unmanned aerial vehicle (UAV) technology, UAV remote sensing has become one of the effective means of acquiring high-resolution geographic information.

[0003] However, when capturing geographic images of high mountains, the surrounding environment changes as the drone continuously changes its altitude (Gdz) position. In practical applications, the special terrain and climate conditions of high-altitude areas pose challenges to drone remote sensing. When the drone continuously adjusts its altitude (Gdz) position and tilt, it faces variable environmental factors such as atmospheric conditions, lighting conditions, and terrain slope, which can easily lead to deviations and affect the accurate acquisition of the final geographic image information. This results in a certain error between the captured geographic image and the actual target, and may also cause fluctuations in image quality, affecting the accurate acquisition of geographic information. This problem is often overlooked in traditional drone remote sensing, resulting in unsatisfactory accuracy and reliability of image acquisition. Summary of the Invention

[0004] ( 一 Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a vector geographic information acquisition method and system based on remote sensing imagery, thus resolving the problems mentioned in the background section.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a vector geographic information acquisition system based on remote sensing imagery, comprising a route planning module, a data acquisition module, a processing module, a condition analysis module, a vectorization module, and a deviation comparison module;

[0008] The flight path planning module is used to continuously capture geographic image information of mountains using UAV remote sensing technology, pre-determine the flight path of the UAV to identify the geographic area of ​​the mountains to be captured, and adjust the altitude Gdz and tilt Qxd of the UAV in real time according to the slope trend of the mountains when capturing geographic image information.

[0009] The data acquisition module is used to collect and record relevant environmental data around the UAV and relevant atmospheric state data in the atmosphere when the UAV is capturing geographic image information, and to generate a capture status dataset.

[0010] The processing module is used to upload relevant environmental data and atmospheric situation data to the capture status dataset, and perform data preprocessing and feature extraction on them; it also preprocesses continuous image frames in the geographic image information to remove noise, smooth the image and perform geometric correction, and identifies and extracts effective features in the continuous image frames, and improves the image quality based on image enhancement technology to generate a geographic image dataset.

[0011] The condition analysis module is used to perform machine analysis on relevant data information in the captured state dataset after feature extraction, obtain atmospheric condition factor Dqyz and environmental condition factor Hjyz, correlate the atmospheric condition factor Dqyz and the environmental condition factor Hjyz, and after dimensionless processing, obtain the visibility coefficient Njdx. The visibility coefficient Njdx is obtained by the following formula:

[0012]

[0013] In the formula, F1 represents the proportionality coefficient of atmospheric condition factor Dqyz, F2 represents the proportionality coefficient of environmental condition factor Hjyz, F3 represents the proportionality coefficient of tilt Qxd, 0.06≤F1≤0.26, 0.10≤F2≤0.33, 0.20≤F3≤0.41, and 0.45≤F1+F2+F3≤1.0, and P represents the first correction constant;

[0014] The vectorization module is used to convert captured geographic image information into vector data and generate vector layers to represent the geometric shapes and spatial relationships of different land features;

[0015] The deviation comparison module is used to obtain a preset visibility threshold Q based on the determined high-altitude geographical area to be captured, and to compare and analyze the preset visibility threshold Q with the visibility coefficient Njdx to obtain the analysis results of the vector layer, and to make corresponding adjustment strategies based on the analysis results.

[0016] Preferably, the route planning module includes a predetermined route unit and a real-time positioning unit;

[0017] The predetermined flight path unit is used to acquire and plan flight paths for UAV remote sensing based on the geographical image information of the mountain body to be acquired and in combination with the geographical location in the map.

[0018] The real-time positioning unit is used to continuously capture geographical image information of the side of the mountain based on the planned UAV flight path and the slope trend of the mountain, and to locate the UAV's position on the mountain in real time, so as to grasp the relevant environmental data and atmospheric situation data of the UAV at different altitudes (Gdz) and tilt angles (Qxd).

[0019] Preferably, the data acquisition module includes a first data acquisition unit, a second data acquisition unit, and a geographic image acquisition unit;

[0020] The first data acquisition unit is used to collect relevant atmospheric situation data information around the UAV in real time according to the UAV at different altitudes Gdz and tilt angles Qxd. The relevant atmospheric situation data information includes atmospheric pressure state, atmospheric transparency Tmdz, water vapor concentration Szqn, atmospheric turbulence state, aerosol concentration, and temperature difference Wdc and humidity difference generated by the UAV at different altitudes Gdz.

[0021] The second data acquisition unit is used to collect relevant environmental data information around the UAV in real time according to the UAV at different altitudes Gdz and tilt angles Qxd. The relevant environmental data information includes wind speed Fsz, wind direction, particulate matter volume Klwt, light intensity Gxqd, and weather conditions.

[0022] The geographic image acquisition unit is used to acquire continuous image frames of the side of the mountain using UAV remote sensing to generate geographic image information. The geographic image information includes the slope, aspect, soil condition, geological structure, human activity area, water body distribution, and vegetation type and distribution data of the mountain section to be acquired.

[0023] Preferably, the processing module includes a data preprocessing unit and an image enhancement unit;

[0024] The data preprocessing unit is used to detect and delete repeatedly collected environmental data and atmospheric situation data to avoid data duplication causing bias in the analysis, and to normalize the units using dimensionless processing technology.

[0025] The image enhancement unit is used to extract key feature information from consecutive image frames through image enhancement technology, identify and analyze defective parts in the image, and select and adjust the degree of difference between different regions in the image frame according to their characteristics.

[0026] Preferably, the high mountain section to be acquired is imaged and a three-dimensional regional model is constructed based on UAV remote sensing technology. Spatial analysis technology is used in the three-dimensional regional model to distinguish the relevant environmental data and atmospheric situation data acquired based on the different altitudes (Gdz) of the UAV.

[0027] Preferably, the atmospheric transparency Tmdz and water vapor concentration Szqn are correlated, and after dimensionless processing, the atmospheric condition factor Dqyz is obtained. The atmospheric condition factor Dqyz is obtained by the following formula:

[0028]

[0029] In the formula, α and β are both proportionality coefficients, Wdc is the temperature difference generated by the UAV at different altitudes Gdz, Yqz is the pressure coefficient, and C is the second correction constant.

[0030] A pre-set condition threshold Z is used, and the condition threshold Z is compared and analyzed with the atmospheric condition factor Dqyz to determine whether the UAV needs to use multiple time series to capture the geographic image information of the mountain section to be acquired multiple times. The analysis results are as follows:

[0031] If the atmospheric condition factor Dqyz is greater than or equal to the condition threshold Z, that is, when Dqyz≥Z, it will be determined that the UAV needs to use multiple time series to capture the geographical image information of the mountain section to be acquired multiple times.

[0032] If the atmospheric condition factor Dqyz is less than the condition threshold Z, i.e., Dqyz < Z, then it will be determined that there is no need to use multiple time series for the UAV.

[0033] Preferably, the wind speed Fsz is correlated with the light intensity Gxqd, and after dimensionless processing, the environmental condition factor Hjyz is obtained. The environmental condition factor Hjyz is obtained by the following formula:

[0034]

[0035] In the formula, Klwt represents the particulate volume, b1 and b2 are both proportionality coefficients, and g is the third correction constant.

[0036] Preferably, the vectorization module includes a target recognition unit and a vectorization processing unit;

[0037] The target recognition unit is used to identify the types of different land features in the captured geographic image information, wherein the types of land features include buildings, roads, vegetation and pedestrian walkways;

[0038] The vectorization processing unit is used to convert the geographic image information in the target recognition unit into vector data and generate high mountain vector geometry to represent the shape and location of the high mountain surface.

[0039] Preferably, the preset visibility threshold Q is compared and analyzed with the visibility coefficient Njdx to obtain the analysis results of the vector layer:

[0040] If the visibility coefficient Njdx is greater than the preset visibility threshold Q, it indicates that the high mountain geographical area captured by the UAV remote sensing deviates from the mountain body to be acquired, and there is a phenomenon of false capture in the captured geographical image information. At this time, a red warning notification will be triggered in time, the altitude Gdz and tilt Qxd of the UAV will be adjusted, and continuous image capture will be carried out at multiple time points.

[0041] If the visibility coefficient Njdx is equal to the preset visibility threshold Q, it means that the high mountain geographical area captured by the UAV remote sensing is in the position of the high mountain body to be acquired and no shift has occurred. At this time, the capture operation continues.

[0042] If the visibility coefficient Njdx is less than the preset visibility threshold Q, it means that the high mountain geographical area captured by the UAV remote sensing is in the position of the high mountain body to be acquired. However, in the continuous capture of geographical image information, there is duplicate image information. At this time, the processing module will be used to perform deduplication of the duplicate image information.

[0043] Preferably, a vector geographic information acquisition method based on remote sensing imagery includes the following steps:

[0044] Step 1: Using the flight path planning module, the drone remote sensing technology will be used to capture images of the mountain to be acquired in order to obtain geographic image information. When capturing geographic image information, the drone's altitude Gdz and tilt Qxd will be adjusted in real time according to the slope trend of the mountain.

[0045] Step 2: The data acquisition module will collect relevant environmental data around the UAV and atmospheric situation data in the atmosphere based on the UAV's altitude (Gdz), and generate a capture status dataset.

[0046] Step 3: The processing module will perform data preprocessing and feature extraction on the relevant data information in the captured state dataset, preprocess the continuous image frames in the geographic image information, identify and extract effective features in the continuous image frames, and improve the image quality based on image enhancement technology to generate a geographic image dataset.

[0047] Step 4: The condition analysis module performs machine analysis on the relevant data information in the captured state dataset after feature extraction to obtain the atmospheric condition factor Dqyz and the environmental condition factor Hjyz. The atmospheric condition factor Dqyz and the environmental condition factor Hjyz are correlated and then processed without dimension to obtain the visibility coefficient Njdx.

[0048] Step 5: Convert the captured geographic image information into vector data using the vectorization module, and identify the types of different land features;

[0049] Step 6: Compare and analyze the preset visibility threshold Q with the visibility coefficient Njdx using the deviation comparison module to obtain the analysis results of the vector layer, and make corresponding adjustment strategies based on the analysis results.

[0050] (III) Beneficial Effects

[0051] This invention provides a method and system for acquiring vector geographic information based on remote sensing imagery, which has the following beneficial effects:

[0052] (1) This system uses UAV remote sensing technology to continuously capture geographic image information of high mountains, achieving efficient coverage of complex terrain and improving the efficiency of geographic information acquisition. It also fully considers the real-time acquisition of relevant environmental and atmospheric situation data around the UAV as its location changes, providing multifaceted background information for subsequent processing. Considering various environmental factors during geographic image acquisition, the system calculates the visibility coefficient Njdx to further accurately assess the feasibility and usability of geographic image information acquisition. Furthermore, by comparing the preset visibility threshold Q with the visibility coefficient Njdx, the system automatically makes corresponding adjustment strategies to ensure the accuracy and stability of data during geographic image acquisition. In summary, compared with existing technologies, this system fully considers the dynamic changes in environmental conditions and performs real-time acquisition, thereby further accurately capturing geographic image information.

[0053] (2) In the existing technology, by using spatial analysis technology in the three-dimensional regional model, the system can acquire relevant environmental data and atmospheric situation data based on the different altitudes (Gdz) of the UAV and distinguish them. This facilitates the detection of whether the UAV is affected by external factors at different altitudes (Gdz), which may cause the high-altitude geographical area captured by the UAV remote sensing to deviate from the mountain body to be acquired, and the captured geographical image information may contain false images. This provides the system with the ability to perform in-depth analysis of geographical images. By setting a condition threshold Z in advance and comparing it with the atmospheric condition factor Dqyz, the system can determine whether to use multiple time series based on the analysis results, that is, whether the UAV needs to capture images of the mountain body to be acquired multiple times, so that it can respond more flexibly to different environmental conditions. By judging whether the atmospheric condition factor Dqyz reaches the set condition threshold Z, the system can decide whether to use multiple time series. If the atmospheric conditions are good, multiple captures are not required, thereby improving the efficiency of image capture and further reducing the waste of resources and time. Attached Figure Description

[0054] Figure 1 This is a schematic flowchart of a vector geographic information acquisition system based on remote sensing images according to the present invention.

[0055] Figure 2 This is a schematic diagram illustrating the steps of a vector geographic information acquisition method based on remote sensing imagery according to the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Remote sensing technology, as a means of acquiring information about the Earth's surface, is widely used in agriculture, urban planning, and resource management. In geographic information science and technology, vector data is an important form for describing geographic phenomena and geospatial relationships. Therefore, vector geographic information acquisition systems based on remote sensing imagery have wide applications in spatial information processing and geographic data analysis. With the continuous development of unmanned aerial vehicle (UAV) technology, UAV remote sensing has become one of the effective means of acquiring high-resolution geographic information.

[0058] However, when capturing geographic images of high mountains, the surrounding environment changes as the drone continuously changes its altitude (Gdz) position. In practical applications, the special terrain and climate conditions of high-altitude areas pose challenges to drone remote sensing. When the drone continuously adjusts its altitude (Gdz) position and tilt, it faces variable environmental factors such as atmospheric conditions, lighting conditions, and terrain slope, which can easily cause deviations. This results in a certain error between the captured geographic image and the actual target, and may also lead to fluctuations in image quality, affecting the accurate acquisition of geographic information. This problem is often overlooked in traditional drone remote sensing, resulting in unsatisfactory accuracy and reliability of image acquisition.

[0059] Example 1

[0060] Please see Figure 1 This invention provides a vector geographic information acquisition system based on remote sensing imagery, including a route planning module, a data acquisition module, a processing module, a condition analysis module, a vectorization module, and a deviation comparison module;

[0061] The flight path planning module is used to continuously capture geographic image information of mountains using UAV remote sensing technology, pre-determine the flight path of the UAV to identify the geographic area of ​​the mountains to be captured, and adjust the altitude Gdz and tilt Qxd of the UAV in real time according to the slope trend of the mountains when capturing geographic image information.

[0062] The data acquisition module is used to collect and record relevant environmental data around the UAV and relevant atmospheric state data in the atmosphere when the UAV is capturing geographic image information, and to generate a capture status dataset.

[0063] The processing module is used to upload relevant environmental data and atmospheric situation data to the capture status dataset, and perform data preprocessing and feature extraction on them; it also preprocesses continuous image frames in the geographic image information to remove noise, smooth the image and perform geometric correction, and identifies and extracts effective features in the continuous image frames, and improves the image quality based on image enhancement technology to generate a geographic image dataset.

[0064] The condition analysis module is used to perform machine analysis on relevant data information in the captured state dataset after feature extraction, obtain atmospheric condition factor Dqyz and environmental condition factor Hjyz, correlate the atmospheric condition factor Dqyz and the environmental condition factor Hjyz, and after dimensionless processing, obtain the visibility coefficient Njdx. The visibility coefficient Njdx is obtained by the following formula:

[0065]

[0066] In the formula, F1 represents the proportionality coefficient of atmospheric condition factor Dqyz, F2 represents the proportionality coefficient of environmental condition factor Hjyz, F3 represents the proportionality coefficient of tilt Qxd, 0.06≤F1≤0.26, 0.10≤F2≤0.33, 0.20≤F3≤0.41, and 0.45≤F1+F2+F3≤1.0, and P represents the first correction constant;

[0067] The vectorization module is used to convert captured geographic image information into vector data and generate vector layers to represent the geometric shapes and spatial relationships of different land features;

[0068] The deviation comparison module is used to obtain a preset visibility threshold Q based on the determined high-altitude geographical area to be captured, and to compare and analyze the preset visibility threshold Q with the visibility coefficient Njdx to obtain the analysis results of the vector layer, and to make corresponding adjustment strategies based on the analysis results.

[0069] During operation, the system utilizes the flight path planning module to continuously capture geographic image information of high mountains using UAV remote sensing technology. The real-time control mechanism within the flight path planning module dynamically adjusts the UAV's altitude (Gdz) and tilt (Qxd) based on the mountain's slope, ensuring accurate geographic image information acquisition under varying terrain conditions. The data acquisition module collects relevant environmental and atmospheric data around the UAV, providing comprehensive background information for subsequent processing and considering various environmental factors during geographic image acquisition. Calculating the visibility coefficient (Njdx) helps to further accurately assess the feasibility and usability of geographic image information acquisition. By comparing the preset visibility threshold (Q) with the visibility coefficient (Njdx), the system automatically makes corresponding adjustment strategies to ensure data accuracy and stability during geographic image acquisition.

[0070] Example 2

[0071] Please refer to Figure 1 Specifically: the route planning module includes a predetermined route unit and a real-time positioning unit;

[0072] The predetermined flight path unit is used to acquire and plan flight paths for UAV remote sensing based on the geographical image information of the mountain body to be acquired and in combination with the geographical location in the map.

[0073] The real-time positioning unit is used to continuously capture geographical image information of the side of the mountain based on the planned UAV flight path and the slope trend of the mountain, and to locate the UAV's position on the mountain in real time, so as to grasp the relevant environmental data and atmospheric situation data of the UAV at different altitudes (Gdz) and tilt angles (Qxd).

[0074] The data acquisition module includes a first data acquisition unit, a second data acquisition unit, and a geographic image acquisition unit;

[0075] The first data acquisition unit is used to collect relevant atmospheric situation data information around the UAV in real time according to the UAV at different altitudes Gdz and tilt angles Qxd. The relevant atmospheric situation data information includes atmospheric pressure state, atmospheric transparency Tmdz, water vapor concentration Szqn, atmospheric turbulence state, aerosol concentration, and temperature difference Wdc and humidity difference generated by the UAV at different altitudes Gdz.

[0076] The second data acquisition unit is used to collect relevant environmental data information around the UAV in real time according to the UAV at different altitudes Gdz and tilt angles Qxd. The relevant environmental data information includes wind speed Fsz, wind direction, particulate matter volume Klwt, light intensity Gxqd, and weather conditions.

[0077] The geographic image acquisition unit is used to acquire continuous image frames of the side of the mountain using UAV remote sensing to generate geographic image information. The geographic image information includes the slope, aspect, soil condition, geological structure, human activity area, water body distribution, and vegetation type and distribution data of the mountain section to be acquired.

[0078] In this embodiment, the real-time positioning unit continuously captures geographical image information of the mountain's side profile based on the planned flight path and the slope trend of the mountain. Simultaneously, it locates the UAV's position on the mountain in real time, providing accurate location information for subsequent data collection. Furthermore, it acquires relevant environmental data and atmospheric situation data based on different location information. Additionally, the first data acquisition unit, the second data acquisition unit, and the geographical image acquisition unit respectively collect relevant atmospheric situation data, environmental data, and geographical image information around the UAV in real time, providing multi-dimensional and high-quality geographical information for subsequent processing. This allows for subsequent determination of whether the mountain geographical area captured by the UAV remote sensing deviates from the mountain profile to be acquired.

[0079] Example 3

[0080] Please refer to Figure 1 Specifically: the processing module includes a data preprocessing unit and an image enhancement unit;

[0081] The data preprocessing unit is used to detect and delete repeatedly collected environmental data and atmospheric situation data to avoid data duplication causing bias in the analysis, and to normalize the units using dimensionless processing technology.

[0082] The image enhancement unit is used to extract key feature information from consecutive image frames through image enhancement technology, identify and analyze defective parts in the image, and select and adjust the degree of difference between different regions in the image frame according to their characteristics. This helps to eliminate noise in the image, improve the image clarity and contrast, make the geographic image data clearer and easier to analyze, and thus improve the accuracy of geographic information extraction.

[0083] In this embodiment, the data preprocessing unit further reduces the potential bias caused by data duplication in analysis by detecting and deleting duplicate environmental and atmospheric situation data, thereby improving the accuracy and consistency of the data. At the same time, the use of dimensionless processing technology for unit normalization helps to eliminate interference from different dimensions, improving the comparability and consistency of the data. After preprocessing and image enhancement, the data is more analyzable and interpretable, which helps the subsequent condition analysis module to more accurately obtain atmospheric condition factor Dqyz and environmental condition factor Hjyz, thereby improving the accurate calculation of visibility coefficient Njdx and providing a more reliable data foundation for geographic information collection.

[0084] Example 4

[0085] Please refer to Figure 1 Specifically: Based on UAV remote sensing technology, the high mountain section to be acquired is imaged and a three-dimensional regional model is constructed. Spatial analysis technology is used in the three-dimensional regional model to distinguish the relevant environmental data and atmospheric situation data acquired based on the different altitudes (Gdz) of the UAV.

[0086] Among them, spatial analysis technology is used to process relevant environmental data and atmospheric situation data acquired by UAVs at different altitudes (Gdz) to distinguish and build three-dimensional regional models. It also includes geographic information systems to process geographic data, identify land cover types, and analyze terrain, so as to more comprehensively utilize and understand the collected geographic image information.

[0087] The atmospheric transparency Tmdz and water vapor concentration Szqn are correlated, and after dimensionless processing, the atmospheric condition factor Dqyz is obtained. The atmospheric condition factor Dqyz is obtained by the following formula:

[0088]

[0089] In the formula, α and β are both proportionality coefficients, Wdc is the temperature difference generated by the UAV at different altitudes Gdz, Yqz is the pressure coefficient, and C is the second correction constant.

[0090] The atmospheric transparency Tmdz mentioned above is measured using a transilluminator or an optical transducer. The transilluminator can measure the degree of attenuation of light as it passes through the atmosphere, thus indirectly reflecting the transparency. A decrease in transparency may lead to a decrease in the signal strength received by the UAV remote sensing, thereby affecting the image quality.

[0091] The water vapor concentration Szqn is measured by a water vapor detector or water vapor measuring instrument;

[0092] The temperature difference (Wdc) generated by the drone at different altitudes (Gdz) is collected by a temperature sensor.

[0093] The pressure coefficient Yqz is acquired through a pressure sensor;

[0094] A pre-set condition threshold Z is used, and the condition threshold Z is compared and analyzed with the atmospheric condition factor Dqyz to determine whether the UAV needs to use multiple time series to capture the geographic image information of the mountain section to be acquired multiple times. The analysis results are as follows:

[0095] If the atmospheric condition factor Dqyz is greater than or equal to the condition threshold Z, that is, when Dqyz≥Z, it indicates that the current external environment may affect the captured geographic image. In this case, it will be determined that the UAV needs to use multiple time series to capture the geographic image information of the mountain section to be acquired multiple times.

[0096] If the atmospheric condition factor Dqyz is less than the condition threshold Z, i.e., Dqyz < Z, then it will be determined that there is no need to use multiple time series for the UAV.

[0097] In this embodiment, by pre-setting a condition threshold Z and comparing it with the atmospheric condition factor Dqyz, the system can determine whether to use multiple time series based on the analysis results, that is, whether the UAV needs to capture multiple images of the mountain body to be acquired, so that it can more flexibly cope with different environmental conditions; by determining whether the atmospheric condition factor Dqyz reaches the set condition threshold Z, the system can decide whether to use multiple time series.

[0098] Example 5

[0099] Please refer to Figure 1 Specifically: the wind speed Fsz is correlated with the light intensity Gxqd, and after dimensionless processing, the environmental condition factor Hjyz is obtained. The environmental condition factor Hjyz is obtained by the following formula:

[0100]

[0101] In the formula, Klwt represents the particulate volume, b1 and b2 are both proportionality coefficients, and g is the third correction constant.

[0102] The wind speed Fsz mentioned above is collected by wind speed sensors, hot-wire wind speed sensors or ultrasonic wind speed sensors, and provides information on the direction and intensity of the wind.

[0103] The light intensity Gxqd is acquired by a light intensity sensor.

[0104] The particulate matter volume Klwt is measured using lidar or a laser scattering instrument. A laser beam is emitted into the atmosphere, and the concentration and size of the particulate matter are estimated by measuring the scattered light.

[0105] The vectorization module includes a target recognition unit and a vectorization processing unit;

[0106] The target recognition unit is used to identify the types of different land features in the captured geographic image information, wherein the types of land features include buildings, roads, vegetation and pedestrian walkways;

[0107] The vectorization processing unit is used to convert the geographic image information in the target recognition unit into vector data, generate high mountain vector geometry to represent the shape and location of the high mountain surface, and provide high-precision spatial data for geographic information.

[0108] The preset visibility threshold Q is compared and analyzed with the visibility coefficient Njdx to obtain the analysis results of the vector layer:

[0109] If the visibility coefficient Njdx is greater than the preset visibility threshold Q, it indicates that the mountainous geographical area captured by the UAV remote sensing deviates from the mountain section to be acquired, and the captured geographical image information contains false images. In this case, a red warning notification will be triggered in a timely manner to indicate that the captured geographical image information may have offset and false images. The altitude Gdz and tilt Qxd of the UAV are adjusted, and continuous image capture is performed at multiple time points to obtain more comprehensive and clearer data.

[0110] If the visibility coefficient Njdx is equal to the preset visibility threshold Q, it means that the high mountain geographical area captured by the UAV remote sensing is in the position of the high mountain body to be acquired and no shift has occurred. At this time, the capture operation continues.

[0111] If the visibility coefficient Njdx is less than the preset visibility threshold Q, it indicates that the high mountain geographical area captured by the UAV remote sensing is in the position of the high mountain body to be acquired. However, in the continuous capture of geographical image information, there is duplicate image information. At this time, the processing module will be used to perform deduplication of the duplicate image information, which helps to optimize the geographical image information, reduce the interference of duplicate information on subsequent analysis, and improve data utilization efficiency.

[0112] In this embodiment, the target recognition unit further realizes intelligent classification of land features by identifying the types of different land features in the captured geographic image, such as buildings, roads, and vegetation; the vectorization processing unit converts the information of these land features into vector data to generate high mountain vector geometry, thereby better representing the shape and location of the high mountain surface; by comparing and analyzing the preset visibility threshold Q with the visibility coefficient Njdx, the system can determine whether the currently captured high mountain geographic area deviates from the location of the high mountain body to be acquired, and obtain the analysis results. Under different circumstances, the system adopts different strategies, such as red warning notification, continuous image capture, and deduplication operations, to further ensure the accuracy and integrity of the geographic image.

[0113] Example 6

[0114] Please refer to Figure 1 and Figure 2 Specifically: A method for acquiring vector geographic information based on remote sensing imagery, comprising the following steps:

[0115] Step 1: Using the flight path planning module, the drone remote sensing technology will be used to capture images of the mountain to be acquired in order to obtain geographic image information. When capturing geographic image information, the drone's altitude Gdz and tilt Qxd will be adjusted in real time according to the slope trend of the mountain.

[0116] Step 2: The data acquisition module will collect relevant environmental data around the UAV and atmospheric situation data in the atmosphere based on the UAV's altitude (Gdz), and generate a capture status dataset.

[0117] Step 3: The processing module will perform data preprocessing and feature extraction on the relevant data information in the captured state dataset, preprocess the continuous image frames in the geographic image information, identify and extract effective features in the continuous image frames, and improve the image quality based on image enhancement technology to generate a geographic image dataset.

[0118] Step 4: The condition analysis module performs machine analysis on the relevant data information in the captured state dataset after feature extraction to obtain the atmospheric condition factor Dqyz and the environmental condition factor Hjyz. The atmospheric condition factor Dqyz and the environmental condition factor Hjyz are correlated and then processed without dimension to obtain the visibility coefficient Njdx.

[0119] Step 5: Convert the captured geographic image information into vector data using the vectorization module, and identify the types of different land features;

[0120] Step 6: Compare and analyze the preset visibility threshold Q with the visibility coefficient Njdx using the deviation comparison module to obtain the analysis results of the vector layer, and make corresponding adjustment strategies based on the analysis results.

[0121] In this embodiment, combining the contents of steps one to six, the flight path of the UAV is first planned to determine the location of the mountain to be acquired. Then, during the image capture process of the UAV, relevant environmental data and atmospheric situation data are collected. The visibility coefficient Njdx is obtained through the condition analysis module. Finally, the strategy needs to be adjusted through comparative analysis.

[0122] Example:

[0123] Data Acquisition: Atmospheric transparency (Tmdz) was 95%; water vapor concentration (Szqn) was 68%; temperature difference (Wdc) generated by the UAV at different altitudes (Gdz) was 12; pressure coefficient (Yqz) was 6.2; α was 0.25; β was 0.37; C was 0.5; wind speed (Fsz) was 16; light intensity (Gxqd) was 26; particulate matter volume (Klwt) was 2.3; b1 was 0.15; b2 was 0.49; g was 0.35; tilt (Qxd) was 64%; F1 was 0.20; F2 was 0.30; F3 was 0.21; P was 0.89.

[0124] Based on the above data, the following calculations can be performed:

[0125] Atmospheric condition factors

[0126] Environmental factors

[0127] Visibility coefficient

[0128] If the preset visibility threshold Q is 10, then the visibility coefficient Njdx is greater than the preset visibility threshold Q, indicating that the high mountain geographical area captured by the UAV remote sensing deviates from the mountain body to be acquired, and there is a phenomenon of false capture in the captured geographical image information. At this time, a red warning notification will be triggered in time, the altitude Gdz and tilt Qxd of the UAV will be adjusted, and continuous image capture will be carried out at multiple time points.

[0129] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vector geographic information acquisition system based on remote sensing imagery, characterized in that: It includes a route planning module, a data acquisition module, a processing module, a condition analysis module, a vectorization module, and a deviation comparison module; The flight path planning module is used to continuously capture geographic image information of mountains using UAV remote sensing technology, pre-determine the flight path of the UAV to identify the geographic area of ​​the mountains to be captured, and adjust the altitude Gdz and tilt Qxd of the UAV in real time according to the slope trend of the mountains when capturing geographic image information. The data acquisition module is used to collect and record relevant environmental data around the UAV and relevant atmospheric state data in the atmosphere when the UAV is capturing geographic image information, and to generate a capture status dataset. The processing module is used to upload relevant environmental data and atmospheric situation data to the capture status dataset, and to perform data preprocessing and feature extraction on them. The system preprocesses consecutive image frames in the geographic image information to remove noise, smooth the image, and perform geometric correction. It also identifies and extracts effective features from consecutive image frames and improves the image quality based on image enhancement techniques to generate a geographic image dataset. The condition analysis module is used to perform machine analysis on relevant data information in the captured state dataset after feature extraction, obtain atmospheric condition factor Dqyz and environmental condition factor Hjyz, correlate the atmospheric condition factor Dqyz and the environmental condition factor Hjyz, and after dimensionless processing, obtain the visibility coefficient Njdx. The visibility coefficient Njdx is obtained by the following formula: In the formula, F1 represents the proportionality coefficient of atmospheric condition factor Dqyz, F2 represents the proportionality coefficient of environmental condition factor Hjyz, F3 represents the proportionality coefficient of tilt Qxd, 0.06≤F1≤0.26, 0.10≤F2≤0.33, 0.20≤F3≤0.41, and 0.45≤F1+F2+F3≤1.0, and P represents the first correction constant; The vectorization module is used to convert captured geographic image information into vector data and generate vector layers to represent the geometric shapes and spatial relationships of different land features; The deviation comparison module is used to obtain a preset visibility threshold Q based on the determined high-altitude geographical area to be captured, and to compare and analyze the preset visibility threshold Q with the visibility coefficient Njdx to obtain the analysis results of the vector layer, and to make corresponding adjustment strategies based on the analysis results.

2. The vector geographic information acquisition system based on remote sensing imagery according to claim 1, characterized in that: The route planning module includes a pre-defined route unit and a real-time positioning unit; The predetermined flight path unit is used to acquire and plan flight paths for UAV remote sensing based on the geographical image information of the mountain body to be acquired and in combination with the geographical location in the map. The real-time positioning unit is used to continuously capture geographical image information of the side of the mountain based on the planned UAV flight path and the slope trend of the mountain, and to locate the UAV's position on the mountain in real time, so as to grasp the relevant environmental data and atmospheric situation data of the UAV at different altitudes (Gdz) and tilt angles (Qxd).

3. The vector geographic information acquisition system based on remote sensing imagery according to claim 2, characterized in that: The data acquisition module includes a first data acquisition unit, a second data acquisition unit, and a geographic image acquisition unit; The first data acquisition unit is used to collect relevant atmospheric situation data information around the UAV in real time according to the UAV at different altitudes Gdz and tilt angles Qxd. The relevant atmospheric situation data information includes atmospheric pressure state, atmospheric transparency Tmdz, water vapor concentration Szqn, atmospheric turbulence state, aerosol concentration, and temperature difference Wdc and humidity difference generated by the UAV at different altitudes Gdz. The second data acquisition unit is used to collect relevant environmental data information around the UAV in real time according to the UAV at different altitudes Gdz and tilt angles Qxd. The relevant environmental data information includes wind speed Fsz, wind direction, particulate matter volume Klwt, light intensity Gxqd, and weather conditions. The geographic image acquisition unit is used to acquire continuous image frames of the side of the mountain using UAV remote sensing to generate geographic image information. The geographic image information includes the slope, aspect, soil condition, geological structure, human activity area, water body distribution, and vegetation type and distribution data of the mountain section to be acquired.

4. A vector geographic information acquisition system based on remote sensing imagery according to claim 3, characterized in that: The processing module includes a data preprocessing unit and an image enhancement unit; The data preprocessing unit is used to detect and delete repeatedly collected environmental data and atmospheric situation data to avoid data duplication causing bias in the analysis, and to normalize the units using dimensionless processing technology. The image enhancement unit is used to extract key feature information from consecutive image frames through image enhancement technology, identify and analyze defective parts in the image, and select and adjust the degree of difference between different regions in the image frame according to their characteristics.

5. A vector geographic information acquisition system based on remote sensing imagery according to claim 4, characterized in that: Based on UAV remote sensing technology, the high mountain section to be acquired is imaged and a three-dimensional regional model is constructed. Spatial analysis technology is used in the three-dimensional regional model to distinguish the relevant environmental data and atmospheric situation data acquired based on the different altitudes (Gdz) of the UAV.

6. A vector geographic information acquisition system based on remote sensing imagery according to claim 5, characterized in that: The atmospheric transparency Tmdz and water vapor concentration Szqn are correlated, and after dimensionless processing, the atmospheric condition factor Dqyz is obtained. The atmospheric condition factor Dqyz is obtained by the following formula: In the formula, α and β are both proportionality coefficients, Wdc is the temperature difference generated by the UAV at different altitudes Gdz, Yqz is the pressure coefficient, and C is the second correction constant. A pre-set condition threshold Z is used, and the condition threshold Z is compared and analyzed with the atmospheric condition factor Dqyz to determine whether the UAV needs to use multiple time series to capture the geographic image information of the mountain section to be acquired multiple times. The analysis results are as follows: If the atmospheric condition factor Dqyz is greater than or equal to the condition threshold Z, that is, when Dqyz≥Z, it will be determined that the UAV needs to use multiple time series to capture the geographical image information of the mountain section to be acquired multiple times. If the atmospheric condition factor Dqyz is less than the condition threshold Z, i.e., Dqyz < Z, then it will be determined that there is no need to use multiple time series for the UAV.

7. A vector geographic information acquisition system based on remote sensing imagery according to claim 6, characterized in that: The wind speed Fsz is correlated with the light intensity Gxqd, and after dimensionless processing, the environmental condition factor Hjyz is obtained. The environmental condition factor Hjyz is obtained by the following formula: In the formula, Klwt represents the particulate volume, b1 and b2 are both proportionality coefficients, and g is the third correction constant.

8. A vector geographic information acquisition system based on remote sensing imagery according to claim 7, characterized in that: The vectorization module includes a target recognition unit and a vectorization processing unit; The target recognition unit is used to identify the types of different land features in the captured geographic image information, wherein the types of land features include buildings, roads, vegetation and pedestrian walkways; The vectorization processing unit is used to convert the geographic image information in the target recognition unit into vector data and generate high mountain vector geometry to represent the shape and location of the high mountain surface.

9. A vector geographic information acquisition system based on remote sensing imagery according to claim 8, characterized in that: The preset visibility threshold Q is compared and analyzed with the visibility coefficient Njdx to obtain the analysis results of the vector layer: If the visibility coefficient Njdx is greater than the preset visibility threshold Q, it indicates that the high mountain geographical area captured by the UAV remote sensing deviates from the mountain body to be acquired, and there is a phenomenon of false capture in the captured geographical image information. At this time, a red warning notification will be triggered in time, the altitude Gdz and tilt Qxd of the UAV will be adjusted, and continuous image capture will be carried out at multiple time points. If the visibility coefficient Njdx is equal to the preset visibility threshold Q, it means that the high mountain geographical area captured by the UAV remote sensing is in the position of the high mountain body to be acquired and no shift has occurred. At this time, the capture operation continues. If the visibility coefficient Njdx is less than the preset visibility threshold Q, it means that the high mountain geographical area captured by the UAV remote sensing is in the position of the high mountain body to be acquired. However, in the continuous capture of geographical image information, there is duplicate image information. At this time, the processing module will be used to perform deduplication of the duplicate image information.

10. A method for acquiring vector geographic information based on remote sensing imagery, comprising the vector geographic information acquisition system based on remote sensing imagery as described in any one of claims 1 to 9, characterized in that: Includes the following steps, Step 1: Using the flight path planning module, the drone remote sensing technology will be used to capture images of the mountain to be acquired in order to obtain geographic image information. When capturing geographic image information, the drone's altitude Gdz and tilt Qxd will be adjusted in real time according to the slope trend of the mountain. Step 2: The data acquisition module will collect relevant environmental data around the UAV and atmospheric situation data in the atmosphere based on the UAV's altitude (Gdz), and generate a capture status dataset. Step 3: The processing module will perform data preprocessing and feature extraction on the relevant data information in the captured state dataset, preprocess the continuous image frames in the geographic image information, identify and extract effective features in the continuous image frames, and improve the image quality based on image enhancement technology to generate a geographic image dataset. Step 4: The condition analysis module performs machine analysis on the relevant data information in the captured state dataset after feature extraction to obtain the atmospheric condition factor Dqyz and the environmental condition factor Hjyz. The atmospheric condition factor Dqyz and the environmental condition factor Hjyz are correlated and then processed without dimension to obtain the visibility coefficient Njdx. Step 5: Convert the captured geographic image information into vector data using the vectorization module, and identify the types of different land features; Step 6: Compare and analyze the preset visibility threshold Q with the visibility coefficient Njdx using the deviation comparison module to obtain the analysis results of the vector layer, and make corresponding adjustment strategies based on the analysis results.

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