Disaster risk detection method and system based on active and passive imaging of unmanned aerial vehicle-mounted platform

Abstract

The invention discloses a disaster risk detection method and system based on active and passive imaging of an unmanned aerial vehicle platform, and belongs to the technical field of unmanned aerial vehicle application and disaster risk detection. According to the method, real-time position calculation output of a GNSS integrated navigation system of an unmanned aerial vehicle-mounted platform is taken as a position and attitude data basis, and point cloud and spectral images acquired by a laser radar scanner and a multispectral camera on the same platform are processed; the calculation of five elements including the gradient, the slope direction, the slope curvature, the plane curvature and the vegetation coverage of the region of interest is realized on the unmanned aerial vehicle-mounted platform, disaster risk influence factor analysis is carried out through link return, and finally, the rapid detection and identification of the disaster risk based on active and passive imaging are realized. According to the invention, real-time acquisition of the space and spectral information of the region of interest and flexible and rapid identification of the earth surface elements can be realized on the unmanned aerial vehicle-mounted platform, and important technical support is provided for current disaster risk detection, monitoring and early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle application and disaster risk detection, and in particular relates to a disaster risk detection method and system based on active and passive imaging of an unmanned aerial vehicle platform. Background Art

[0002] The UAV platform has the advantages of flexibility, rapid response, portability, good adaptability to various terrains and high cost performance, and is a powerful supplement to traditional air-to-ground observation. As new remote sensing detection methods that have flourished in recent years, active detection by lidar and multispectral passive imaging technology have unique technical characteristics in terms of target geometric space and texture spectral information acquisition. Among them, lidar uses active emission of laser pulses to detect targets. It is currently the fastest and most accurate means of obtaining surface elevation information, but the laser point cloud data it obtains lacks spectral information that describes the attributes of the objects. Multispectral imaging technology can simultaneously obtain the spatial and spectral information of the target based on traditional passive optics. The spectral data of specific bands obtained by it has outstanding advantages in identifying the attributes of objects.

[0003] In the existing fields of disaster risk detection, post-disaster emergency response and post-disaster rescue, the efficiency of comprehensive remote sensing information detection and ground target identification in disaster-stricken areas is often low due to problems such as heavy detection payloads, short effective flight time of the platform, single LiDAR and multispectral camera, no real-time fusion of geometric / spectral information, poor timeliness of post-GPS differential processing of position and attitude information, and inability to calculate surface elements in real time. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a disaster risk detection method and system based on active and passive imaging of an unmanned aerial vehicle platform. The point cloud and spectral images obtained by a lidar scanner and a multispectral camera on the same platform are used as data basis. The five elements of slope, slope aspect, slope curvature, plane curvature and vegetation coverage of the area of ​​interest are calculated on the unmanned aerial vehicle platform, and the disaster risk influencing factors are analyzed through link feedback, ultimately realizing rapid detection and identification of disaster risks based on active and passive imaging.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: On the one hand, the present invention provides a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform, comprising the following steps: Step 1: Time-synchronize the LiDAR scanner and the multispectral camera; Step 2: Storing active and passive imaging data and position and attitude data acquired by the UAV platform; Step 3: Calculate the spatial coordinates of point cloud and multispectral image data based on active and passive imaging data and position and posture data; Step 4: Obtain five disaster risk factors, namely slope, aspect, slope curvature, plane curvature and vegetation cover, based on point cloud data with spatial coordinates and multispectral image data; Step 5: Input the calculated five disaster risk factors into the drone platform data processing algorithm for evaluation, and transmit the evaluation results back to the ground control end through the link carried by the drone.

[0006] On the other hand, the present invention provides a disaster risk detection system based on active and passive imaging of an unmanned aerial vehicle platform, comprising: A time synchronization module, used to synchronize the time between the lidar scanner and the multispectral camera; A data storage module, used to store active and passive imaging data and position and attitude data acquired by the UAV platform; The spatial coordinate settlement module is used to solve the spatial coordinates of point cloud and multispectral image data based on active and passive imaging data and position and posture data; The element calculation module is used to obtain five disaster risk elements, namely slope, aspect, slope curvature, plane curvature and vegetation cover, based on point cloud data with spatial coordinates and multispectral image data; The factor assessment module is used to input the calculated five disaster risk factors into the drone platform data processing algorithm for assessment, and transmit the assessment results back to the ground control end through the link carried by the drone.

[0007] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; A memory for storing one or more programs; Among them, when one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform.

[0009] The beneficial effects of the present invention are: The present invention proposes a disaster risk detection method based on spatial three-dimensional information and ground object spectral information obtained by the low-power active and passive imaging system of an unmanned aerial vehicle platform. This method can achieve real-time acquisition of spatial and spectral information of the area of ​​interest and flexible and rapid identification of surface elements on the unmanned aerial vehicle platform, providing important technical support for the current difficulties in disaster risk detection, monitoring and early warning, such as terrain survey, geological judgment, and personnel rescue. It solves the problems of short effective flight time of the platform due to heavy detection load, single laser radar and multi-spectral camera, no real-time fusion of geometric / spectral information, poor timeliness of post-GPS differential processing of position and attitude information, and inability to calculate surface elements in real time, and provides a new method with high convenience and high timeliness for rapid detection and identification of comprehensive remote sensing information in disaster-stricken areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flow chart of a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform according to the present invention. DETAILED DESCRIPTION

[0011] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0012] Aiming at the application requirements of disaster risk detection and other comprehensive remote sensing information acquisition with high convenience and high timeliness, the present invention provides a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform, based on an in-depth study of the principles of an unmanned aerial vehicle platform system, a GNSS integrated navigation system, a lidar scanner, a multispectral camera and a platform processing system. The method comprises: the GNSS integrated navigation carried by the unmanned aerial vehicle platform realizes time synchronization of the lidar scanner and the multispectral camera through two methods, GPRMC and PPS second pulse; the data processing system carried by the unmanned aerial vehicle platform realizes storage of lidar ranging data, multispectral image data, GNSS integrated navigation position and attitude data; and the method comprises: The platform data processing system solves the lidar ranging data and multispectral image data to generate point cloud data and multispectral image data with spatial coordinates, respectively. Then, for the solved point cloud data, the slope, aspect, slope curvature, and plane curvature parameters are calculated from the perspective of geometric information. For the multispectral image data with spatial coordinates, the vegetation coverage parameters are calculated from the perspective of radiation information. Finally, the five disaster risk factors calculated in the previous step are input into the UAV platform data processing algorithm for disaster risk assessment, and the assessment results are transmitted back to the ground station through the link carried by the UAV, so as to realize the rapid detection and identification of disaster risks and surface elements by active and passive imaging.

[0013] like Figure 1 As shown in FIG. 1 , a flow chart of a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform is provided in the present invention, and the specific steps are as follows: Step 1: Time synchronization between the lidar scanner and the multispectral camera; The GNSS integrated navigation carried by the UAV platform achieves time synchronization through the GPRMC signal in the satellite navigation data. The GPRMC signal mainly includes: UTC time, positioning status, latitude, latitude hemisphere, longitude, longitude hemisphere, ground speed, ground heading, UTC time, magnetic declination, magnetic declination direction and mode guidance. The time synchronization module mainly achieves time synchronization of the lidar scanner and the multispectral camera by combining the UTC time information in the GPRMC signal with the PPS pulse signal, so that the coordinate solution of the subsequent point cloud and multispectral image data can be performed based on the synchronized time information.

[0014] Step 2: storing active and passive imaging data and position and posture data; The data processing system carried by the UAV platform stores the ranging data obtained by the lidar scanner, the image data obtained by the multispectral camera, and the instantaneous position and attitude data obtained by the GNSS integrated navigation system.

[0015] Step 3: coordinate solution of point cloud and multispectral image data; Step 3.1, point cloud coordinate solution; The time-synchronized LiDAR scanner ranging data and the instantaneous position and attitude data obtained by the GNSS integrated navigation system are aligned through time information, and the absolute spatial coordinates of the point cloud data are solved.

[0016] , In the formula, is the coordinate of the LiDAR point cloud in the WGS-84 space rectangular coordinate system; is the coordinate of the phase center of the GPS antenna of the GNSS integrated navigation system in the WGS-84 spatial rectangular coordinate system; is the instantaneous azimuth attitude rotation matrix of the inertial measurement unit; is the instantaneous pitch attitude angle rotation matrix of the inertial measurement unit; is the instantaneous roll attitude angle rotation matrix of the inertial measurement unit; is the linear offset between the laser radar transmitting reference center and the inertial unit reference center, where The distance between the laser radar transmitting reference center and the inertial unit reference center along the inertial unit coordinate system The offset of the axis, where The distance between the laser radar transmitting reference center and the inertial unit reference center along the inertial unit coordinate system The offset of the axis, where The distance between the laser radar transmitting reference center and the inertial unit reference center along the inertial unit coordinate system The offset of the axis, is the placement angle rotation matrix of the laser radar transmitting reference center and the inertial unit reference center; is the coordinate of the instantaneous laser radar point in the laser radar body coordinate system.

[0017] Step 3.2: Coordinate calculation of multispectral image data; The instantaneous position and attitude data obtained by the GNSS integrated navigation system are aligned with the multispectral image based on the timestamp. The instantaneous position and attitude information at the time of shooting are used as the exterior orientation elements of the image. The image is subjected to collinear equation calculation to obtain multispectral image data with spatial coordinate information.

[0018] , In the formula, is the object coordinate of the multispectral image in the WGS-84 rectangular coordinate system; is the proportionality coefficient; (i=1,2,3) are the cosine values ​​of the nine directions composed of the exterior azimuth elements obtained by the INS system in the GNSS instantaneous position and attitude measurement system; is the image space coordinate; It is the instantaneous exterior orientation line element obtained by GPS in the GNSS instantaneous position and attitude measurement system, that is, the spatial coordinates of the multispectral camera.

[0019] Step 4: Generate disaster risk factors; Step 4.1 Slope element calculation; The slope factor is mainly obtained through the point cloud data calculated in step 3.1. The smaller the slope value, the flatter the terrain; the larger the slope value, the steeper the terrain. The slope percentage can be calculated by the elevation change and the horizontal distance change. According to the size of the area to be calculated, select from the point cloud data Two points, then The coordinates of the two points are , , the calculation formula of slope factor is as follows: , In the formula, for The slope feature value within two points.

[0020] Step 4.2 Calculate the aspect factor; The aspect factor mainly determines the azimuth facing the downhill slope. The aspect factor is mainly obtained through the point cloud data calculated in step 3.1. The point coordinates are , the calculation formula of the aspect factor is as follows: , In the formula, for The aspect feature value of the point, The rate of change of the point in the east direction is , The rate of change of the point along the north direction is .

[0021] Step 4.3 Calculate the slope curvature elements; The slope curvature element mainly calculates the second-order derivative of the surface of the area of ​​interest, that is, the slope of the slope. The slope curvature element calculates the curvature along the direction of the maximum slope. If the slope curvature calculation result is positive, it means that the area protrudes upward, and if it is positive, it means that the area protrudes downward. A high slope curvature value indicates that the water flow will accelerate when it flows through this area, and a low value indicates that the water flow will slow down when it flows through this area. The calculation method can be calculated using a fourth-order polynomial with a certain point as the center point and 8 surrounding areas.

[0022] Step 4.4 Calculation of plane curvature elements; The plane curvature element calculates the curvature perpendicular to the maximum slope direction. If the plane curvature calculation result is positive, it means that the area protrudes upward, and if it is negative, it means that the area protrudes downward. A high plane curvature value indicates that the water flow will accelerate and converge when it flows through this area, and a low value indicates that the water flow will slow down and converge when it flows through this area. The calculation method can be calculated using a fourth-order polynomial with a certain point as the center point and eight surrounding areas.

[0023] Step 4.5 Calculation of vegetation cover elements; The calculation of vegetation coverage elements is mainly based on the reflectance of the red light band and near-infrared band obtained by the multispectral camera in step 3.2 to calculate the NDVI index. The value range is [-1,1]. The larger the value is greater than 0, the higher the vegetation density and vegetation vitality. A value of 0 indicates that this is rock or bare land. A value less than 0 indicates that this is covered by water and snow.

[0024] , In the formula, is the vegetation cover element value, is the reflectance value in the near-infrared band, is the reflectance value of the red light band.

[0025] Step 5: Disaster risk assessment and data feedback; The five disaster risk assessment factors, namely slope factor, aspect factor, slope curvature factor, plane curvature factor and vegetation cover factor, calculated in step 4 are input into the UAV platform data processing algorithm for evaluation, and the evaluation results are transmitted back to the ground control end through the link carried by the UAV to achieve rapid detection and identification of disaster risks and surface elements by active and passive imaging.

[0026] On the other hand, the present invention provides a disaster risk detection system based on active and passive imaging of an unmanned aerial vehicle platform, comprising: A time synchronization module, used to synchronize the time between the lidar scanner and the multispectral camera; A data storage module, used to store active and passive imaging data and position and attitude data acquired by the UAV platform; The spatial coordinate settlement module is used to solve the spatial coordinates of point cloud and multispectral image data based on active and passive imaging data and position and posture data; The element calculation module is used to obtain five disaster risk elements, namely slope, aspect, slope curvature, plane curvature and vegetation cover, based on point cloud data with spatial coordinates and multispectral image data; The factor assessment module is used to input the calculated five disaster risk factors into the drone platform data processing algorithm for assessment, and transmit the assessment results back to the ground control end through the link carried by the drone.

[0027] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; A memory for storing one or more programs; Among them, when one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform.

[0028] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0029] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform, characterized in that: The steps include: Step 1: Time-synchronize the LiDAR scanner and the multispectral camera; Step 2: Storing active and passive imaging data and position and attitude data acquired by the UAV platform; Step 3: Calculate the spatial coordinates of point cloud and multispectral image data based on active and passive imaging data and position and posture data; Step 4: Obtain five disaster risk factors, namely slope, aspect, slope curvature, plane curvature and vegetation cover, based on point cloud data with spatial coordinates and multispectral image data; Step 5: Input the calculated five disaster risk factors into the drone platform data processing algorithm for evaluation, and transmit the evaluation results back to the ground control end through the link carried by the drone.

2. According to claim 1, a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform is characterized in that: The step 1 includes the GNSS integrated navigation system carried by the UAV platform realizing time synchronization of the lidar scanner and the multispectral camera through the GPRMC signal in the satellite navigation data.

3. According to claim 2, a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform is characterized in that: The GPRMC signal includes at least one of the following information: UTC time, positioning status, latitude, latitude hemisphere, longitude, longitude hemisphere, ground speed, ground heading, UTC time, magnetic declination, magnetic declination direction and mode guidance.

4. According to claim 1, a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform is characterized in that: The step 2 includes the data processing system carried by the UAV platform storing the ranging data obtained by the lidar scanner, the image data obtained by the multispectral camera, and the instantaneous position and attitude data obtained by the GNSS integrated navigation system.

5. According to claim 1, a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform is characterized in that: The spatial coordinate solution of the point cloud data in step 3 includes: The distance measurement data obtained by the time-synchronized LiDAR scanner and the instantaneous position and attitude data obtained by the GNSS integrated navigation system are aligned through time information, and the absolute spatial coordinates of the point cloud data are solved.

6. According to claim 1, a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform is characterized in that: The step 3 of multispectral image data spatial coordinate solution includes: The instantaneous position and attitude data obtained by the GNSS integrated navigation system are aligned with the image data obtained by the multispectral camera based on the timestamp. The instantaneous position and attitude information at the time of shooting are used as the exterior orientation elements of the image data. The spatial coordinate solution of the multispectral image data with spatial coordinate information is obtained through the collinearity equation.

7. The method for disaster risk detection based on active and passive imaging of an unmanned aerial vehicle platform according to claim 1, characterized in that: The step 4 includes obtaining the slope, slope aspect, slope curvature, and plane curvature based on the spatial coordinates of the point cloud data, and obtaining vegetation coverage elements based on the spatial coordinates of the multispectral image data.

8. A disaster risk detection system based on active and passive imaging of an unmanned aerial vehicle platform, characterized in that: include: A time synchronization module, used to synchronize the time between the lidar scanner and the multispectral camera; A data storage module, used to store active and passive imaging data and position and attitude data acquired by the UAV platform; The spatial coordinate settlement module is used to solve the spatial coordinates of point cloud and multispectral image data based on active and passive imaging data and position and posture data; The element calculation module is used to obtain five disaster risk elements, namely slope, aspect, slope curvature, plane curvature and vegetation cover, based on point cloud data with spatial coordinates and multispectral image data; The factor assessment module is used to input the calculated five disaster risk factors into the drone platform data processing algorithm for assessment, and transmit the assessment results back to the ground control end through the link carried by the drone.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Among them, when one or more programs are executed by the one or more processors, the one or more processors implement the disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, which, when executed by a processor, enable the processor to implement a disaster risk detection method based on active and passive imaging of an unmanned aerial vehicle platform as described in any one of claims 1-7.

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