Unmanned aerial vehicle cluster cross-medium remote sensing data fusion processing method and system

Abstract

The invention relates to the technical field of remote sensing data fusion, and discloses an unmanned aerial vehicle cluster cross-medium remote sensing data fusion processing method and system, and the method comprises the steps: carrying out the inversion of cross-medium remote sensing data based on a medium propagation path model; establishing a physical observation mapping relation between the media, and mapping the physical observation values under different media into unified physical observation values under unified physical semantics; and fusing the unified physical observation values of the ground coordinates in the target area under different media to obtain a target area remote sensing map reflecting the physical characteristics of the ground surface and the underwater structure information. According to the invention, based on the motion field modeling result of the water body medium, dynamic avoidance of a high-disturbance area is realized; a medium propagation path model is introduced to carry out physical inversion on original remote sensing data, a mapping relation from remote sensing observation values under different media to unified physical semantics is established, a target area remote sensing map is generated through fusion, and collaborative perception and analysis of earth surface and underwater structures are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data processing, especially to the technical field of remote sensing data fusion, in particular to a method and system for unmanned aerial vehicle cluster cross-medium remote sensing data fusion processing. BACKGROUND

[0002] With the rapid development of multi-source remote sensing platforms, unmanned aerial vehicle systems are widely used in surface environment monitoring, water body detection, agricultural remote sensing, and other fields. To improve the coverage and resolution of remote sensing data, more and more tasks use unmanned aerial vehicle clusters to carry out large-area remote sensing operations. At the same time, cross-medium remote sensing data collection has become a trend, such as air platforms collecting multi-spectral images and synthetic aperture radar images, ground laser radars obtaining structural information, underwater platforms collecting sonar images, etc. The data obtained by these heterogeneous sensors have different transmission paths, physical quantities, and disturbance effects. How to process and fuse cross-medium remote sensing data physically consistently is an important challenge in current remote sensing intelligent processing.

[0003] Cross-medium remote sensing refers to obtaining medium information of different levels and different properties through different remote sensing means (such as optical, infrared, radar, etc.), which puts higher requirements on the sensor configuration and data processing of unmanned aerial vehicle clusters. Unmanned aerial vehicle clusters work cooperatively through multiple unmanned aerial vehicles, carry different sensors, realize comprehensive detection of target areas in multiple media, and can obtain more comprehensive and accurate ground information. This approach not only enhances the diversity and timeliness of data, but also overcomes the limitations of single unmanned aerial vehicles in task execution, such as short flight time, small coverage, and incomplete information acquisition.

[0004] Existing researches mainly focus on the fusion processing of multi-modal remote sensing data in the same medium. For example, the public patent CN118536061B proposes a multi-dimensional remote sensing data fusion method based on multi-task learning, which establishes spatial, temporal, and spectral information extraction models for time-series high spatial resolution, high temporal resolution, and high spectral resolution data, respectively, and designs a multi-feature joint loss function to realize integrated and efficient fusion of four-dimensional remote sensing data, improving the fusion quality and speed. This method has certain advantages in information dimension modeling, but it mainly focuses on air remote sensing data and does not involve the problem of data incomparability and unregistration caused by physical differences between media.

[0005] To solve this problem, the present application proposes a method for unmanned aerial vehicle cluster cross-medium remote sensing data fusion processing, which realizes the spatio-temporal registration of different medium remote sensing data and improves the utilization efficiency and accuracy of multi-remote sensing data. SUMMARY

[0006] Therefore, the present application provides a UAV cluster cross-medium remote sensing data fusion processing method and system, aiming at the problem that remote sensing data under different media are physically incomparable and difficult to be uniformly analyzed, a medium propagation path model and a physical observation inversion mechanism are introduced, original observation data (such as brightness, point cloud intensity, sonar amplitude) under different media (air / ground / underwater) are inverted into comparable physical observation values, and through establishing a cross-medium physical observation mapping relationship, the data are uniformly processed into uniform physical observation values with the same physical semantics; aiming at the problem that water disturbance causes the failure of UAV synchronous collection, a real-time motion field modeling of water medium is introduced, the interference ability of the water medium is identified, and the cross-medium remote sensing data collection task of the UAV cluster is dynamically adjusted; the observation data of different media of the same ground coordinate are fused to generate a target area remote sensing map under a unified view, and full-factor remote sensing analysis of a complex area is realized.

[0007] To achieve the above-mentioned purpose, the present application provides a UAV cluster cross-medium remote sensing data fusion processing method, which comprises the following steps: S1: obtaining the medium distribution information of the target area, modeling the motion field of the water medium, identifying the interference ability of the water medium to the data collection process, and dynamically adjusting the cross-medium remote sensing data collection task of the UAV cluster, and collecting the cross-medium remote sensing data of the target area by using the UAV cluster; S2: inverting the cross-medium remote sensing data based on the medium propagation path model, and inverting the remote sensing observation values in the cross-medium remote sensing data into physical observation values of the target area under different media; S3: establishing a physical observation mapping relationship between media, and mapping the physical observation values under different media into uniform physical observation values under a unified physical semantics; S4: fusing the uniform physical observation values of the ground coordinate in the target area under different media to obtain a target area remote sensing map reflecting the physical characteristics of the ground surface and the underwater structure information, and comprehensively detecting the target area.

[0008] As a further improved method of the present application: Optionally, the medium distribution information of the target area is the medium type of each position area in the target area, the medium type includes air medium, water medium and ground medium, the motion field of the water medium is modeled, and the interference ability of the water medium to the data collection process is identified, which comprises: extracting the position area of the medium type being the water medium , and extracting the position area , and arranging wave buoys, and recording the position area of the wave buoys every day, extracting the wave height, wave frequency, phase and wave propagation direction from the time series wave data to generate the position area a time-varying wave velocity vector as the extracted position region a modeling result of a motion field of the water body medium ; wherein, a position region a modeling result of a motion field of the water body medium, t represents time sequence information, a period length of time sequence wave data, is set to 10, a position region a wave height in the associated time sequence wave data, a position region a wave frequency in the associated time sequence wave data, a position region a phase in the associated time sequence wave data, a position region a wave propagation direction in the associated time sequence wave data; the is a two-dimensional vector , corresponding to a position region wave velocities of the water body medium in the position region at t along the horizontal direction and the vertical direction respectively; the is a two-dimensional vector , corresponding to a position region wave velocities of the water body medium in the position region at t along the horizontal direction and the vertical direction respectively: ; wherein, a normalized processing result of the modeling result of the motion field , representing a maximum value in the selected set.

[0009] Optionally, based on the interference ability of the water body medium, the cross-medium remote sensing data collection task of the UAV cluster is adjusted in real time, comprising: The types of the UAVs include fixed-wing UAVs carrying hyperspectral cameras, underwater UAVs carrying sonar devices, and multi-rotor UAVs carrying laser radars, wherein the fixed-wing UAVs, the underwater UAVs, and the multi-rotor UAVs respectively perform remote sensing data collection tasks of position regions of air medium, water body medium, and ground medium, to obtain cross-medium remote sensing data; If the interference capability of the water medium is greater than the interference threshold based on the maximum Doppler shift limit, the remote sensing data collection task of the corresponding position area is postponed, and a unified sampling time window is set for each position area, so that different types of unmanned aerial vehicles in the same position area simultaneously and synchronously perform remote sensing data collection tasks, and adjacent position areas are time-synchronized. The cross-medium remote sensing data is remote sensing data of each position area in the target area under different media, wherein the remote sensing data type under the air medium is hyperspectral remote sensing image, the remote sensing data type under the water medium is sonar image, and the remote sensing data type of the ground medium is near-ground laser imaging. The pixel values of the hyperspectral remote sensing image, the sonar image and the near-ground laser imaging are remote sensing observation values in the remote sensing data.

[0010] Optionally, the cross-medium remote sensing data is inverted based on a medium propagation path model, comprising: The medium propagation path model comprises an air medium propagation path model, a water medium propagation path model and a ground medium propagation path model. The pixel value in the hyperspectral remote sensing image is inverted based on the air medium propagation path model to obtain the reflectivity of the pixel value under the air medium, wherein the inversion formula under the air medium is: ; Wherein, represents the pixel value in the hyperspectral remote sensing image, represents the reflectivity of the pixel value under the air medium, represents the set wavelength of the hyperspectral camera, represents the wave corresponding to the atmospheric path radiation, represents the wavelength corresponding to the atmospheric optical thickness, represents the exponential function with the natural constant as the base; The pixel value in the sonar image is inverted based on the water medium propagation path model to obtain the reflectivity of the pixel value under the water medium, wherein the inversion formula under the water medium is: ; ; Wherein, represents the pixel value in the sonar image, represents the reflectivity of the pixel value under the water medium, represents the propagation distance of the sonar signal, represents the transmission frequency of the sonar device, represents the Doppler shift of the position area associated with the sonar image, , where c represents the sound propagation speed in water, and Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, ; ; , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula,

[0011] Optionally, the physical observation mapping relationship between the media is established, including: , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula, , where Thorp represents the Thorp empirical formula.

[0012] Optionally, the physical observation mapping relationship between the media is utilized to map the physical observation values in different media to the unified physical observation values in the unified physical semantics, to obtain the unified physical observation values of each ground coordinate in the target area in different media.

[0013] Optionally, the unified physical observation values of the ground coordinates in the target area in different media are fused to obtain a target area remote sensing image reflecting the physical characteristics of the ground and the underwater structure information, including: The unified physical observation values of the ground coordinates in the target region under different media are fused by using an adaptive weighting method to obtain remote sensing reflection values of the ground coordinates in the target region, and a target region remote sensing image reflecting the physical characteristics of the ground and the underwater structure information is formed, wherein the higher the remote sensing reflection value is, the stronger the reflection characteristics of the structure contained in the ground coordinate are.

[0014] To solve the above problems, the present application provides a kind of unmanned aerial vehicle cluster cross-media remote sensing data fusion processing system, the unmanned aerial vehicle cluster cross-media remote sensing data fusion processing system includes data acquisition module, physical inversion module and fusion processing module: The data acquisition module is used to obtain the medium distribution information of the target region, model the motion field of the water medium, identify the interference ability of the water medium to the data acquisition process, adjust the cross-media remote sensing data acquisition task of the unmanned aerial vehicle cluster in real time, and collect the cross-media remote sensing data of the target region using the unmanned aerial vehicle cluster; The physical inversion module is used to invert the cross-media remote sensing data based on the medium propagation path model, invert the remote sensing observation values in the cross-media remote sensing data into physical observation values of the target region under different media, establish the physical observation mapping relationship between media, and map the physical observation values under different media into unified physical observation values under unified physical semantics; The fusion processing module is used to fuse the unified physical observation values of the ground coordinates in the target region under different media, obtain the target region remote sensing image reflecting the physical characteristics of the ground and the underwater structure information, and comprehensively detect the target region. To realize the unmanned aerial vehicle cluster cross-media remote sensing data fusion processing method as described above.

[0015] To solve the above problems, the present application provides an electronic device, which comprises: A memory stores at least one instruction; A communication interface realizes the communication of the electronic device; and A processor executes the instructions stored in the memory to realize the unmanned aerial vehicle cluster cross-media remote sensing data fusion processing method as described above.

[0016] To solve the above problems, the present application further provides a computer readable storage medium, which stores at least one instruction, and the at least one instruction is executed by the processor in the electronic device to realize the unmanned aerial vehicle cluster cross-media remote sensing data fusion processing method as described above.

[0017] Compared with the prior art, the present application provides a kind of unmanned aerial vehicle cluster cross-media remote sensing data fusion processing method and system, and the technology has the following advantages: Firstly, the application is directed to the influence of water medium disturbance on remote sensing data acquisition, introduces a water medium motion field modeling module, constructs a continuous space-time motion field modeling result based on wave propagation speed, wave height, direction and other parameters, and calculates the disturbance intensity index according to this, quantifies the interference ability of the water medium in the current position area. At the same time, combined with the interference threshold of Doppler frequency shift tolerance, the interference ability is compared with the threshold in real time, and the position area exceeding the threshold is automatically identified. In the task planning stage, the cooperative flight plan and data acquisition window of the unmanned aerial vehicle are adaptively adjusted according to the interference ability map, so as to avoid the interference strong area or delay the shooting time, and ensure the spatial synchronization and waveform consistency of the remote sensing data.

[0018] At the same time, by constructing the propagation path attenuation model in different media, it can adapt to various remote sensing equipment and environmental changes, solve the problem that the traditional method is difficult to be universal, and after normalization, various remote sensing data are converted into reflectivity, which is an index with physical meaning, and the foundation of constructing the cross-media response domain is constructed, which provides a unified input format for subsequent multi-modal data fusion, atlas construction or target detection. Compared with the original pixel reflectivity estimation without considering the medium propagation attenuation, the model can effectively eliminate the influence of path interference, frequency shift distortion and signal scattering, so that the underwater sonar image and the aerial hyperspectral remote sensing image have higher alignment and response reliability, especially suitable for environmental monitoring and information extraction in complex ground-air-water scenes. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of an unmanned aerial vehicle cluster cross-media remote sensing data fusion processing method provided by an embodiment of the application.

[0020] Figure 2 A cross-media remote sensing data processing flowchart provided by an embodiment of the application.

[0021] The implementation of the application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application.

[0023] The embodiment of the present application provides a UAV cluster cross-medium remote sensing data fusion processing method. The execution subject of the UAV cluster cross-medium remote sensing data fusion processing method includes but is not limited to at least one of electronic devices such as a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the present application. In other words, the UAV cluster cross-medium remote sensing data fusion processing method can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server, or a cloud server cluster, and the like.

[0024] Reference Figure 1 In the embodiment 1 of the present application, A UAV cluster cross-medium remote sensing data fusion processing method includes the following steps: S1: Obtain medium distribution information of a target area, model a water body medium, identify the interference ability of the water body medium to a data collection process, adjust a cross-medium remote sensing data collection task of a UAV cluster in real time, and collect cross-medium remote sensing data of the target area by using the UAV cluster.

[0025] The medium distribution information of the target area is a medium type of each position area in the target area, and the medium type includes an air medium, a water body medium, and a ground medium. The water body medium is modeled, and the interference ability of the water body medium to the data collection process is identified, including: It should be noted that a digital elevation model of the target area and a geographic vector diagram are obtained, the digital elevation model is a data set representing the elevation of each position in the target area, and the geographic vector diagram is a spatial data set describing the boundary of a geographic entity, used to divide the boundary between geographic entities such as rivers, lakes, oceans, roads, and buildings. In combination with the digital elevation model and the geographic vector diagram, the water body elevation average of the rivers, lakes, and oceans is obtained. Specifically, the boundary of the geographic entity in the geographic vector diagram is taken as the boundary of the position area in the target area, the target area is divided into a plurality of position areas, and the elevation average of the position area is identified. The medium type of the position area in the target area whose elevation average is lower than the water body elevation average is marked as the water body medium, the medium type of the position area in the target area whose elevation average is higher than the water body elevation average is marked as the ground medium, and the medium type of the position area in the target area whose elevation average is higher than the minimum flight height of the UAV is marked as the air medium, wherein the minimum flight height of the UAV is 20 meters. Extract the position area whose medium type is the water body medium And record the position area Lay out wave buoys, and record the position area extracting wave height, wave frequency, phase and wave propagation direction from the time-series wave data, generating a position area wave velocity vector changing over time as the extracted position area the modeling result of the motion field of the water medium: ; wherein, the position area the modeling result of the motion field of the water medium, t represents time-series information, the period length of the time-series wave data is set to 10, the position area wave height in the associated time-series wave data, the position area wave frequency in the associated time-series wave data, the position area phase in the associated time-series wave data, the position area wave propagation direction in the associated time-series wave data; the is a two-dimensional vector corresponding to the position area wave velocity of the water medium in the horizontal direction and the vertical direction at time t; as an embodiment of the present application, the horizontal direction is the north-south direction, and the vertical direction is the east-west direction; Specifically, the wave height and the wave frequency are the amplitude and the frequency in the time-series wave data respectively, and the wave propagation direction is the angle measured counterclockwise relative to the north direction by the wave determined by the plurality of wave buoys; standardizing the modeling result of the motion field as the interference ability of the water medium in the position area , wherein the standardization processing formula is: ; wherein, the standardization processing result of the modeling result of the motion field , wherein the maximum value in the selected set is selected; The higher the interference ability of the water medium, the higher the amplitude of the Doppler shift generated by the remote sensing equipment carried by the unmanned aerial vehicle, and the received remote sensing data will have the problems of carrier frequency offset, waveform distortion and signal-to-noise ratio decrease. Based on the interference ability of the water medium, the cross-medium remote sensing data acquisition task of the unmanned aerial vehicle cluster is adjusted in real time, including:

[0026] ​The types of the unmanned aerial vehicles include a fixed-wing unmanned aerial vehicle carrying a hyperspectral camera, an underwater unmanned aerial vehicle carrying a sonar device, and a multi-rotor unmanned aerial vehicle carrying a laser radar, wherein the fixed-wing unmanned aerial vehicle, the underwater unmanned aerial vehicle, and the multi-rotor unmanned aerial vehicle respectively perform a remote sensing data collection task of a position area in an air medium, a water medium, and a ground medium to obtain cross-medium remote sensing data. If the interference capability of the water medium is greater than an interference threshold based on a maximum Doppler shift limit, the remote sensing data collection task of the corresponding position area is postponed, and a unified sampling time window is set for each position area, so that different types of unmanned aerial vehicles in the same position area simultaneously and synchronously perform the remote sensing data collection task, and adjacent position areas are time-synchronized. Specifically, the deviation of the collection time of the remote sensing data collection task of the adjacent position areas is less than 10 minutes. The interference threshold based on the maximum Doppler shift limit is: ; Wherein, represents the interference threshold based on the maximum Doppler shift limit, c represents the propagation speed of the sonar in water, represents the transmission frequency of the sonar device, represents the maximum Doppler shift (ranging from 500 Hz to 1000 Hz) that can be tolerated. The cross-medium remote sensing data is remote sensing data of each position area in the target area under different media, wherein the remote sensing data type under the air medium is a hyperspectral remote sensing image, the remote sensing data type under the water medium is a sonar image, and the remote sensing data type of the ground medium is near-ground laser imaging. The pixel values of the hyperspectral remote sensing image, the sonar image, and the near-ground laser imaging are remote sensing observation values in the remote sensing data. It should be noted that the same position area can contain multiple medium types to obtain remote sensing data under multiple media.

[0027] Specifically, the sonar image and the near-ground laser imaging are both point cloud data, including three-dimensional coordinates and pixel values, and the hyperspectral remote sensing image is a two-dimensional image.

[0028] Compared with the traditional fixed collection time or rough weather warning mechanism, the method realizes real-time positioning, quantifiable evaluation, and dynamic avoidance control of the interference area, significantly improves the collection accuracy of the remote sensing data in the complex water medium environment and the reliability of multi-platform cooperation, and is especially suitable for high-precision environmental monitoring in strong disturbance areas such as sea surface and port.

[0029] S2: Inversion based on a medium propagation path model is performed on the cross-medium remote sensing data to invert the remote sensing observation values in the cross-medium remote sensing data into physical observation values of the target area under different media.

[0030] Inversion of cross-media remote sensing data based on media propagation path models includes: The medium propagation path model includes an air medium propagation path model, a water medium propagation path model, and a ground medium propagation path model. The pixel values ​​in the hyperspectral remote sensing image are inverted based on an aerial propagation path model to obtain the reflectance of the pixel values ​​in the aerial medium. The inversion formula for the aerial medium is as follows: ; in, Represents the pixel values ​​in hyperspectral remote sensing images. Represents pixel value Reflectivity in an air medium This indicates the set wavelength of the hyperspectral camera. Indicates wavelength Corresponding atmospheric path radiation, Indicates wavelength The corresponding atmospheric optical thickness, Represents an exponential function with the natural constant as its base; In this embodiment, atmospheric path radiation can be preset or obtained by combining publicly available radiative transfer models (such as the 6S model) with environmental parameters such as the UAV observation angle, solar altitude angle, and aerosol type during the data collection, ensuring that this value has engineering usability. Atmospheric optical thickness can be obtained by looking up values ​​in the atmospheric model (such as MODTRAN) after setting the UAV observation angle, solar altitude angle, and regional atmospheric type. The pixel values ​​in the sonar image are inverted based on a water propagation path model to obtain the reflectivity of the pixel values ​​in the water medium. The inversion formula for the water medium is as follows: ; ; in, Represents the pixel values ​​in a sonar image. Represents pixel value Reflectivity in water media Indicates the propagation distance of the sonar signal. This indicates the transmission frequency of the sonar equipment. This indicates the Doppler frequency shift of the location region associated with the sonar image. The formula for calculating the underwater attenuation coefficient is given, using Thorp's empirical formula, where c represents the underwater propagation speed of the sonar. This indicates the interference capability of the water medium in the area associated with the sonar image; The pixel value in the near-ground laser imaging is inversely calculated based on a ground medium propagation path model to obtain reflectivity of the pixel value in the ground medium, wherein the inverse calculation formula under the ground medium is: ; ; wherein, represents a pixel value in near-ground laser imaging, represents a pixel value reflectivity of the pixel value in the ground medium, represents an incident angle of a laser radar, represents a refraction coefficient of a laser radar signal, and P represents atmospheric pressure during near-ground laser imaging, represents an environmental temperature during near-ground laser imaging, represents a pixel value corresponding ground position to a distance of the laser radar; The pixel coordinates are converted to ground coordinates to obtain reflectivity of different ground coordinates in different media in the target area, and the reflectivity is taken as a physical observation value.

[0031] It should be noted that the ground coordinates are two-dimensional coordinates, and in order to realize accurate alignment of ground objects, the application constructs a cross-medium coordinate conversion module to respectively perform geometric solving and projection conversion on hyperspectral remote sensing images, sonar images and near-ground laser imaging, and convert the pixel coordinates to ground coordinates in the same coordinate system. Specifically, the coordinate system of the ground coordinates is a latitude and longitude coordinate system. For the hyperspectral remote sensing image, combined with the unmanned aerial vehicle attitude information and the digital elevation model, orthorectification is performed to obtain the ground coordinates of each pixel. For the sonar image, based on the polar coordinate expansion of the sound wave emission point and the attitude angle compensation, the ground coordinates of the sound wave echo landing point are calculated. For the near-ground laser imaging, the three-dimensional points are transformed to a unified coordinate system through the unmanned aerial vehicle attitude information and the time synchronization information, and are projected to the latitude and longitude coordinate system.

[0032] Specifically, the atmospheric optical thickness introduced by the air medium explicitly considers the loss of light through aerosols, ozone and the like, the water medium is explicitly modeled to consider the energy attenuation of the signal in the underwater propagation distance, and the ground medium is introduced to correct the atmospheric pressure and temperature, thereby eliminating the energy offset caused by different propagation paths. In the dynamic water body, the frequency of the sound wave will be shifted due to the relative motion of the target (such as waves, currents) during propagation, resulting in device demodulation error or misjudgment of reflectivity. Therefore, the Doppler shift and the attenuation coefficient of the underwater signal are introduced to realize compensation and correction of frequency distortion, and the incident angle of the laser radar and the atmospheric path radiation are used to attenuate the scattering phenomenon.

[0033] S3: Establishing a physical observation mapping relationship between media, mapping physical observation values under different media to unified physical observation values under unified physical semantics.

[0034] Establishing a physical observation mapping relationship between media, including: adopting an empirical regression method to construct a physical observation mapping relationship between air medium and water medium and a physical observation mapping relationship between air medium and ground medium , the physical observation mapping relationship taking physical observation values under water medium as input and taking unified physical observation values as output, the physical observation mapping relationship taking physical observation values under ground medium and the incident angle of the associated laser radar as input and taking unified physical observation values as output.

[0035] Specifically, the physical observation mapping relationship between air medium and water medium is: ; wherein, represents physical observation values under water medium converted to unified physical observation values under unified physical semantics, are empirical regression coefficients, obtained by collecting multi-medium remote sensing data samples in the spatially overlapping area and performing regression fitting; considering the large dynamic range of acoustic reflectivity, logarithmic conversion is used to align with the scale of optical reflectivity; The physical observation mapping relationship between air medium and ground medium is: ; wherein, represents physical observation values under ground medium converted to unified physical observation values under unified physical semantics, are empirical regression coefficients, obtained by collecting multi-medium remote sensing data samples in the spatially overlapping area and performing regression fitting. The empirical regression coefficients are fitted by least squares method or regularization regression algorithm, which not only ensures physical comparability, but also has good migration and calibration flexibility, and is suitable for multi-medium data standardization processing requirements in various unmanned aerial vehicle cluster remote sensing tasks.

[0036] Using the physical observation mapping relationship between media, the physical observation values under different media are mapped to unified physical observation values under unified physical semantics, to obtain unified physical observation values of each ground coordinate in the target area under different media.

[0037] Specifically, the unified physical observation values of the ground coordinates in the target area under different media are ,in Representing ground coordinates Unified physical observations under an aerial medium, i.e., ground coordinates Physical observations in the air medium The ground coordinates are represented in sequence. Unified physical observations in water and surface media; For example, if ground coordinates If the medium type of the location area does not include water, then .

[0038] S4: The unified physical observation values ​​of ground coordinates in the target area under different media are fused to obtain a remote sensing map of the target area that reflects the physical characteristics of the surface and the underwater structure information, and the target area is comprehensively explored.

[0039] By fusing unified physical observations of ground coordinates in the target area under different media, a remote sensing image of the target area reflecting surface physical properties and underwater structure information is obtained, including: An adaptive weighting method is used to fuse the unified physical observation values ​​of the ground coordinates in the target area under different media to obtain the remote sensing reflectance values ​​of the ground coordinates in the target area. This forms a remote sensing map of the target area that reflects the physical characteristics of the surface and the information of the underwater structure. The higher the remote sensing reflectance value, the stronger the reflectance characteristics of the ground features or underwater structures at that ground coordinate.

[0040] Specifically, the ground coordinates The adaptive weighting method is as follows: ; ; in, Ground coordinates The remote sensing reflectance value, In order to be The weight, Representing ground coordinates Unified physical observations under an aerial medium, i.e., ground coordinates Physical observations in the air medium The ground coordinates are represented in sequence. Unified physical observations in water and surface media; like ,but , ,but , ,but The weights of the uniform physical observations that are not zero are consistent; The target region is comprehensively detected based on the remote sensing map of the target region. Specifically, for the position region under the water medium, the abnormal increase or decrease of the remote sensing reflection value indicates that the concentration of suspended solids in the water changes, the turbidity of the water changes, and the reflection is affected. For the position region under the ground medium, the lower the remote sensing reflection value, the more the vegetation coverage is reduced, the larger the bare soil area is, and the more the soil erosion is.

[0041] As shown in a cross-medium remote sensing data processing flowchart, the collection and processing flow of cross-medium remote sensing data are illustrated. Figure 2 As shown in a cross-medium remote sensing data processing flowchart, the collection and processing flow of cross-medium remote sensing data are illustrated.

[0042] Embodiment 2: A UAV cluster cross-medium remote sensing data fusion processing system includes a data acquisition module, a physical inversion module, and a fusion processing module. The data acquisition module is configured to obtain medium distribution information of a target region, model a motion field of a water medium, identify the interference ability of the water medium on the data acquisition process, adjust the cross-medium remote sensing data acquisition task of the UAV cluster in real time, and collect cross-medium remote sensing data of the target region by using the UAV cluster. The physical inversion module is configured to invert the cross-medium remote sensing data based on a medium propagation path model, invert remote sensing observation values in the cross-medium remote sensing data into physical observation values of the target region under different media, establish a physical observation mapping relationship between media, and map the physical observation values under different media into unified physical observation values under unified physical semantics. The fusion processing module is configured to fuse the unified physical observation values of the ground coordinates in the target region under different media to obtain a remote sensing map of the target region reflecting the physical characteristics of the ground surface and the underwater structure information, and comprehensively detect the target region.

[0043] A UAV cluster cross-medium remote sensing data fusion processing method is implemented.

[0044] It should be understood that the embodiments are only for illustration and are not limited in the scope of the patent application by the structure.

[0045] It should be noted that the above-mentioned embodiment numbers of the application are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "include", "contain" or any other variant in this paper are intended to cover non-exclusive inclusion, so that the process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, device, article or method. Without more limitation, the element defined by the statement "including a" does not exclude the existence of another identical element in the process, device, article or method including the element.

[0046] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk) as described above, and includes a plurality of instructions for causing an end device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

[0047] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent flow transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied to other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for cross-media remote sensing data fusion processing of a UAV cluster, characterized in that, The method comprises: S1: obtaining medium distribution information of a target area, modeling a water medium motion field, identifying the interference ability of the water medium to a data collection process, adjusting the cross-medium remote sensing data collection task of the UAV cluster in real time, and collecting cross-medium remote sensing data of the target area by using the UAV cluster; S2: inverting the cross-medium remote sensing data based on a medium propagation path model, and inverting remote sensing observation values in the cross-medium remote sensing data into physical observation values of the target area under different media; S3: establishing a physical observation mapping relationship between media, and mapping the physical observation values under different media into unified physical observation values under unified physical semantics; S4: fusing the unified physical observation values of the ground coordinates in the target area under different media, obtaining a target area remote sensing map reflecting the physical characteristics of the ground and the underwater structure information, and comprehensively detecting the target area. 2.The method of claim 1, wherein, The medium distribution information of the target area is the medium type of each position area in the target area, and the medium type includes an air medium, a water medium, and a ground medium. The water medium motion field is modeled, and the interference ability of the water medium to the data collection process is identified, including: extracted location area and in the extracted location area deploying wave buoys, recording the location area with the wave buoys time series wave data of each day, extracting wave height, wave frequency, phase and wave propagation direction from the time series wave data, generating the location area wave velocity vectors over time, as the extracted location area the modeling results of the motion field of the water body medium: ； wherein, represents a location area modeling results of the water body medium, t represents time sequence information, represents the period length of the time sequence wave data, is set to 10, represents a location area the wave height in the associated time sequence wave data, represents a location area the wave frequency in the associated time sequence wave data, represents a location area the phase in the associated time sequence wave data, represents a location area the wave propagation direction in the associated time sequence wave data; The is a two-dimensional vector corresponding position area wave velocity of the water medium in the horizontal direction and the vertical direction at time t, respectively Modeling results for the playing field Standardization as a position area The interference capability of the medium of the water body, wherein the standardization formula is: ； wherein, representing the modeling results of the motion field standardized processing results of representing the maximum value in the selected set. 3.The method of claim 2, wherein, Based on the interference ability of the water medium, the cross-medium remote sensing data collection task of the UAV cluster is adjusted in real time, including: The types of the UAVs include a fixed-wing UAV carrying a hyperspectral camera, an underwater UAV carrying a sonar device, and a multi-rotor UAV carrying a laser radar. The fixed-wing UAV, the underwater UAV, and the multi-rotor UAV respectively perform remote sensing data collection tasks of position areas of the air medium, the water medium, and the ground medium to obtain cross-medium remote sensing data; If the interference ability of the water medium is greater than the interference threshold based on the maximum Doppler shift limit, the remote sensing data collection task of the corresponding position area is postponed, and a unified sampling time window is set at each position area, so that the same type of UAVs in the same position area simultaneously and synchronously perform the remote sensing data collection task, and adjacent position areas are time-synchronized; The cross-medium remote sensing data is remote sensing data of each position area in the target area under different media. The remote sensing data type under the air medium is a hyperspectral remote sensing image, the remote sensing data type under the water medium is a sonar image, and the remote sensing data type of the ground medium is near-ground laser imaging. The pixel values of the hyperspectral remote sensing image, the sonar image, and the near-ground laser imaging are remote sensing observation values in the remote sensing data.

4. The method of claim 3, wherein, The cross-medium remote sensing data is inverted based on the medium propagation path model, including: The medium propagation path model includes an air medium propagation path model, a water medium propagation path model, and a ground medium propagation path model; The pixel values in the hyperspectral remote sensing image are inverted based on the air medium propagation path model to obtain reflectivity of the pixel values under the air medium. The inversion formula under the air medium is: ； wherein, represents a pixel value in a hyperspectral remote sensing image, represents a pixel value reflectance under the aerial medium, represents a set wavelength of a hyperspectral camera, represents a wavelength corresponding atmospheric path radiance, represents a wavelength corresponding atmospheric optical thickness, represents an exponential function with a natural constant as base; The pixel values in the sonar image are inverted based on the water medium propagation path model to obtain reflectivity of the pixel values under the water medium. The inversion formula under the water medium is: ； ； wherein, represents a pixel value in a sonar image, represents a pixel value reflectivity under water medium, represents a propagation distance of a sonar signal, represents a transmission frequency of a sonar device, represents a Doppler shift of a position area associated with a sonar image, represents a calculation formula of an underwater attenuation coefficient, which is calculated by using Thorp empirical formula, and c represents a propagation speed of a sonar in water, represents an interference ability of a water medium in a position area associated with a sonar image; The pixel value in the near-earth laser imaging is inversed based on a ground medium propagation path model to obtain reflectivity of the pixel value in the ground medium, wherein the inversion formula under the ground medium is: ； ； wherein, represents a pixel value in near-ground laser imaging, represents a pixel value reflectivity under a ground medium, represents an incident angle of a laser radar, represents a refraction coefficient of a laser radar signal, P represents an atmospheric pressure when near-ground laser imaging is performed, represents an ambient temperature when near-ground laser imaging is performed, represents a pixel value a distance from a corresponding ground position to a laser radar; The pixel coordinates are converted to ground coordinates to obtain reflectivity of different ground coordinates in different media in the target area, and the reflectivity is taken as a physical observation value.

5. The unmanned aerial vehicle cluster cross-media remote sensing data fusion processing method of claim 1, wherein, A physical observation mapping relationship between media is established, including: An empirical regression method is used to construct a physical observation mapping relationship between the air medium and the water medium and a physical observation mapping relationship between the air medium and the ground medium The physical observation mapping relationship takes the physical observation value under the water medium as input and takes the unified physical observation value as output takes the physical observation value under the ground medium and the incident angle of the associated laser radar as input and takes the unified physical observation value as output.

6. The method of claim 5, wherein, The physical observation values in different media are mapped to unified physical observation values in a unified physical semantic by using the physical observation mapping relationship between media to obtain unified physical observation values of each ground coordinate in different media in the target area.

7. The method of claim 6, wherein, The unified physical observation values of the ground coordinates in different media in the target area are fused to obtain a target area remote sensing image reflecting the physical characteristics of the ground and the underwater structure information, including: The unified physical observation values of the ground coordinates in different media in the target area are fused by using an adaptive weighting method to obtain remote sensing reflectance values of the ground coordinates in the target area, which constitute the target area remote sensing image reflecting the physical characteristics of the ground and the underwater structure information, and the higher the remote sensing reflectance value is, the stronger the reflection characteristics of the structure contained in the ground coordinate are.

8. An unmanned aerial vehicle cluster cross-media remote sensing data fusion processing system, characterized in that, The unmanned aerial vehicle cluster cross-medium remote sensing data fusion processing system includes a data acquisition module, a physical inversion module, and a fusion processing module: The data acquisition module is used to obtain medium distribution information of a target area, model a water body medium motion field, identify the interference ability of the water body medium to the data acquisition process, adjust the cross-medium remote sensing data acquisition task of the unmanned aerial vehicle cluster in real time, and collect cross-medium remote sensing data of the target area by using the unmanned aerial vehicle cluster; The physical inversion module is used to inverse the cross-medium remote sensing data based on a medium propagation path model, inverse remote sensing observation values in the cross-medium remote sensing data to physical observation values of the target area in different media, establish a physical observation mapping relationship between media, and map the physical observation values in different media to unified physical observation values in a unified physical semantic; The fusion processing module is used to fuse the unified physical observation values of the ground coordinates in different media in the target area to obtain a target area remote sensing image reflecting the physical characteristics of the ground and the underwater structure information, and comprehensively detect the target area; To realize the unmanned aerial vehicle cluster cross-medium remote sensing data fusion processing method according to any one of claims 1-7.

Images