Multi-source meteorological satellite image fusion processing method and system

Abstract

The invention discloses a multi-source meteorological satellite image fusion processing method and system, and relates to the technical field of image processing, and the method comprises the following steps: S1, selecting a fusion mode, S2, executing a first fusion mode, S3, executing a second fusion mode, S4, executing a third fusion mode, S5, executing a fourth fusion mode, and S6, outputting a fusion result. According to the method, high-resolution image waveband data and specified multispectral image waveband data are fused by using a color fusion transformation algorithm, so that the fused image is sharpened, the quality of the image data is improved, in the second fusion mode, high-frequency spatial information of panchromatic image data is suppressed through low-pass filtering, and low-frequency information of the panchromatic image data is reserved, so that the image quality is improved. The multispectral image data and the image data subjected to low-pass filtering are analyzed, texture structure information of the fused image data is enhanced, the function of principal component analysis fusion is achieved, meanwhile, the spatial resolution is increased, and the image information is enriched.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a multi-source meteorological satellite image fusion processing method and system. Background Art

[0002] Meteorological satellite image fusion is to process redundant or complementary multi-source meteorological satellite data in space, time, and spectrum according to certain rules to obtain more accurate and richer information than any single data, and generate synthetic image data with new spatial, spectral, and temporal characteristics. A data fusion method and device are disclosed in the invention patent with application number CN202410069856.8. The method includes: obtaining full-color channel data through high-resolution satellites, obtaining first pre-processed data based on the full-color channel data; obtaining texture data of the first pre-processed data; obtaining brightness temperature data through geostationary meteorological satellites, obtaining second pre-processed data based on the brightness temperature data; fusing the texture data with the second pre-processed data to obtain fused data. The data fusion algorithm proposed in the present invention utilizes the high-resolution full-color channel data generated by the high-resolution satellite pre-processing subsystem, and after pre-processing, combines the multi-channel brightness temperature data of geostationary meteorological satellites or other geostationary meteorological satellites of the same or similar time to achieve the fusion of multi-channel brightness temperature and full-color channel data.

[0003] The above-mentioned existing technology solves the problem that image information is prone to spectral distortion, but in the process of use, since it does not provide users with a variety of image fusion methods, all images can only perform the same operation during the fusion process, which makes it impossible to achieve better effects in detail processing of some images, thereby reducing the quality of image data to a certain extent. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-source meteorological satellite image fusion processing method and system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a multi-source meteorological satellite image fusion processing method, comprising the following steps: S1. Selecting a fusion method: dividing the image fusion methods into four types, namely, a first fusion method, a second fusion method, a third fusion method and a fourth fusion method, and determining a corresponding image fusion method according to current image data; S2, executing the first fusion mode: if the image fusion mode is the first fusion mode, obtaining the band name of the multispectral image data and the corresponding data set, extracting the band data set of the high-resolution image data, using the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fused data, using the resampling method to process these data, and transmitting the processed results to the storage device; S3, executing the second fusion mode: if the image fusion mode is the second fusion mode, after obtaining the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, the panchromatic image data is subjected to neighborhood smoothing processing using a brightness smoothing filter adjustment model, the processed panchromatic image data is fused with the multispectral image data, and the obtained result is output according to the selected format; S4, executing the third fusion mode: if the image fusion mode is the third fusion mode, performing principal component transformation on the multispectral image data to construct first principal component image information, transmitting the spatial information corresponding to the panchromatic image to the first principal component image information, performing an inverse principal component transformation operation on the image information to obtain a final fusion result; S5, executing the fourth fusion method: if the image fusion method is the fourth fusion method, performing a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has a similar mean and standard deviation, selecting high-frequency data in the panchromatic image, and fusing the high-frequency image data with the multispectral image data through an image fusion formula to obtain a final fusion result; S6. Output fusion result: After selecting the fusion method, query in the storage device according to the fusion method to obtain the corresponding fusion data, and transmit it to the visualization interface.

[0006] Preferably, S2 comprises the following steps: S201, if the image fusion mode is the first fusion mode, obtaining the band name of the multispectral image data and the corresponding data set, extracting the band data set of the high-resolution image data, and transferring the data to a storage device; S202, traverse the band names, determine the multispectral image band data to be processed and the corresponding high-resolution image band data, and use a color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fused data. The color fusion transformation algorithm is specifically as follows: In the formula, Respectively represent the corresponding values ​​of the fused multispectral image data, Represents the corresponding value of the resized full-color image data, Represents the corresponding value of the resized multispectral image data.

[0007] Preferably, the S2 further comprises the following steps: S203, selecting the format of the output file, outputting the fused multispectral image data according to the selected format, processing the data using a resampling method, and transmitting the processed results to a storage device.

[0008] Preferably, the S3 specifically includes the following steps: S301, if the image fusion mode is the second fusion mode, after obtaining the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, perform neighborhood smoothing processing on the panchromatic image data, perform a simulated panchromatic image operation on the processed data, and generate new panchromatic image data; S302, using a brightness smoothing filter adjustment model to fuse the panchromatic image data and the multispectral image data, after outputting the fused data in a selected format, processing the data using a resampling method, and transmitting the processed results to a storage device, wherein the brightness smoothing filter adjustment model is specifically: In the formula, represents the fused data, Represents the irradiance corresponding to the panchromatic image data, Indicates the surface reflectance of the object corresponding to the panchromatic image data. Indicates the band number of the current image. Indicates the band number of the panchromatic image.

[0009] Preferably, the S4 specifically comprises the following steps: S401. If the image fusion mode is the third fusion mode, obtain the band name of the multispectral image data and the corresponding data set, perform principal component transformation on the multispectral image data, and concentrate the transformed image information in the principal component.

[0010] Preferably, the S4 further comprises the following steps: S402, constructing first principal component image information according to the principal component components, after obtaining the panchromatic image data, performing a histogram matching operation on the panchromatic image data and the first principal component image information, transferring the spatial information corresponding to the panchromatic image to the first principal component image information, performing an inverse principal component transformation operation on the image information, and obtaining a final fusion result; S403: After the fusion result is output in the selected format, the multispectral image data is processed using a resampling method, and the processed result is transmitted to a storage device.

[0011] Preferably, the S5 specifically includes the following steps: S501, after obtaining the band names of the multispectral image data and the corresponding data set, perform a histogram adjustment operation on all the bands of the multispectral image and the panchromatic image bands so that each band has a similar mean and standard deviation; S502, after calculating the fitting coefficient using the least square method, after selecting the high-frequency data in the full-color image, the high-frequency image data and the multispectral image data are fused according to the fitting coefficient using an image fusion formula to obtain a final fusion result. The image fusion formula is specifically: In the formula, Respectively represent the fused data values, Represents the full-color image data value, Represents high-frequency image data values, Represents multispectral image data value; S503: After the fusion result is output in the selected format, the multispectral image data is processed using a resampling method, and the processed result is transmitted to a storage device.

[0012] The multi-source meteorological satellite image fusion processing system comprises a mode selection unit, a first fusion analysis unit, a second fusion analysis unit, a third fusion analysis unit, a fourth fusion analysis unit and a result display unit; The mode selection unit divides the image fusion modes into four types, namely, a first fusion mode, a second fusion mode, a third fusion mode and a fourth fusion mode, and determines a corresponding image fusion mode according to current image data; If the image fusion mode is the first fusion mode, the first fusion analysis unit obtains the band name of the multispectral image data and the corresponding data set, extracts the band data set of the high-resolution image data, fuses the high-resolution image band data with the specified multispectral image band data using a color fusion transformation algorithm to obtain complete fused data, processes these data using a resampling method, and transmits the processed results to a storage device; If the image fusion mode is the second fusion mode, the second fusion analysis unit obtains the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, performs neighborhood smoothing processing on the panchromatic image data using a brightness smoothing filter adjustment model, fuses the processed panchromatic image data with the multispectral image data, and outputs the obtained results in the selected format; If the image fusion mode is the third fusion mode, the third fusion analysis unit performs principal component transformation on the multispectral image data to construct first principal component image information, transmits the spatial information corresponding to the panchromatic image to the first principal component image information, performs an inverse principal component transformation operation on the image information, and obtains a final fusion result; If the image fusion mode is the fourth fusion mode, the fourth fusion analysis unit performs a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has a similar mean and standard deviation, selects high-frequency data in the panchromatic image, and fuses the high-frequency image data with the multispectral image data through an image fusion formula to obtain a final fusion result; After selecting the fusion mode, the result display unit searches the storage device according to the fusion mode to obtain corresponding fusion data, and transmits the fusion data to the visualization interface.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention utilizes a color fusion transformation algorithm to fuse high-resolution image band data with specified multispectral image band data, thereby achieving a sharpening effect on the fused image and improving the quality of the image data. The second fusion method suppresses the high-frequency spatial information of the panchromatic image data through a low-pass filter and retains the low-frequency information, and then analyzes the multispectral image data and the image data through the low-pass filter. The fused image data enhances the texture structure information, and the method realizes the function of principal component analysis fusion, and can also automatically align the panchromatic band data with the multispectral band data, retain the spectral information, and at the same time increase the spatial resolution and enrich the image information. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Provide an overall method flow chart for an embodiment of the present invention; Figure 2 A flow chart of a method for executing a first fusion mode provided in an embodiment of the present invention; Figure 3 A flow chart of a method for executing the third fusion mode provided in an embodiment of the present invention; Figure 4 A flow chart of a method for executing the fourth fusion mode provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] See also Figure 1-Figure 4 The present invention provides a technical solution: a multi-source meteorological satellite image fusion processing method, comprising the following steps: S1. Selecting a fusion method: dividing the image fusion methods into four types, namely, a first fusion method, a second fusion method, a third fusion method and a fourth fusion method, and determining a corresponding image fusion method according to current image data; S2, executing the first fusion mode: if the image fusion mode is the first fusion mode, obtaining the band name of the multispectral image data and the corresponding data set, extracting the band data set of the high-resolution image data, using the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fused data, using the resampling method to process these data, and transmitting the processed results to the storage device; S3, executing the second fusion mode: if the image fusion mode is the second fusion mode, after obtaining the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, the panchromatic image data is subjected to neighborhood smoothing processing using a brightness smoothing filter adjustment model, the processed panchromatic image data is fused with the multispectral image data, and the obtained result is output according to the selected format; S4, executing the third fusion mode: if the image fusion mode is the third fusion mode, performing principal component transformation on the multispectral image data to construct first principal component image information, transmitting the spatial information corresponding to the panchromatic image to the first principal component image information, performing an inverse principal component transformation operation on the image information to obtain a final fusion result; S5, executing the fourth fusion method: if the image fusion method is the fourth fusion method, performing a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has a similar mean and standard deviation, selecting high-frequency data in the panchromatic image, and fusing the high-frequency image data with the multispectral image data through an image fusion formula to obtain a final fusion result; S6. Output fusion result: After selecting the fusion method, query in the storage device according to the fusion method to obtain the corresponding fusion data, and transmit it to the visualization interface.

[0017] S2 includes the following steps: S201, if the image fusion mode is the first fusion mode, using the remote sensing data platform to obtain the band name of the multispectral image data and the corresponding data set, extracting the band data set of the high-resolution image data through ENVI software, and then transferring the band data set to a storage device; S202, using an automated band recognition engine to traverse the band names in the multispectral image data and the high-resolution image data, determine the multispectral image band data to be processed and the corresponding high-resolution image band data, establish a corresponding relationship between the high-resolution image band data and the multispectral image band data, and use a color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fused data. The color fusion transformation algorithm is specifically as follows: In the formula, Respectively represent the corresponding values ​​of the fused multispectral image data, Represents the corresponding value of the resized full-color image data, Represents the corresponding value of the resized multispectral image data; S2 also includes the following steps: S203, selecting the format of the output file through ENVI software, outputting the fused multispectral image data according to the selected format, adjusting the resolution of the multispectral image data using a bilinear interpolation method, and transmitting the adjusted image data to a storage device; S3 specifically includes the following steps: S301. If the image fusion method is the second fusion method, after obtaining the band names of the panchromatic image data and the multispectral image data and the corresponding data sets using the remote sensing data platform, use The panchromatic image data is processed by neighborhood smoothing using a sliding window, and the processed data is subjected to simulated panchromatic image operations using a weighted fusion method to generate new panchromatic image data. S302, using a brightness smoothing filter adjustment model to fuse the panchromatic image data with the multispectral image data, outputting the fused data in a selected format through ENVI software, and then resampling the fused image data according to a set target resolution using a bilinear interpolation method, and transmitting the processed results to a storage device. The brightness smoothing filter adjustment model is specifically: In the formula, represents the fused data, Represents the irradiance corresponding to the panchromatic image data, Indicates the surface reflectance of the object corresponding to the panchromatic image data. Indicates the band number of the current image. Indicates the band number of the panchromatic image; S4 specifically includes the following steps: S401, if the image fusion mode is the third fusion mode, using the remote sensing data platform to obtain the band name of the multispectral image data and the corresponding data set, the multispectral image data is transformed into a principal component through a processor, and the covariance matrix between the bands in the multispectral image data is calculated, and the principal component is generated using the covariance matrix; S4 specifically further includes the following steps: S402, constructing the first principal component image information according to the principal component components, obtaining the panchromatic image data by using the remote sensing data platform, performing a histogram matching operation on the panchromatic image data and the first principal component image information by using the ENVI software, transferring the spatial information corresponding to the panchromatic image to the first principal component image information, performing an inverse principal component transformation operation on the image information, and obtaining the final fusion result; S403, after outputting the fusion result according to the selected format using ENVI software, the multispectral image data is processed using a resampling method, and the processed result is transmitted to a storage device; S5 specifically includes the following steps: S501, after obtaining the band names and corresponding data sets of the multispectral image data using the remote sensing data platform, perform histogram adjustment operations on all bands of the multispectral image and the panchromatic image bands using ENVI software so that each band has a similar mean and standard deviation; S502, after calculating the fitting coefficient using the least square method, after selecting the high-frequency data in the full-color image, the high-frequency image data is fused with the multispectral image data according to the fitting coefficient through the image fusion formula to obtain the final fusion result. The specific image fusion formula is: In the formula, Respectively represent the fused data values, Represents the full-color image data value, Represents high-frequency image data values, Represents multispectral image data value; S503, after the fusion result is output in the selected format through ENVI software, the multispectral image data is processed by using a resampling method, and the processed result is transmitted to a storage device; A multi-source meteorological satellite image fusion processing system comprises a mode selection unit, a first fusion analysis unit, a second fusion analysis unit, a third fusion analysis unit, a fourth fusion analysis unit and a result display unit; The mode selection unit divides the image fusion modes into four types, namely, the first fusion mode, the second fusion mode, the third fusion mode and the fourth fusion mode, and determines the corresponding image fusion mode according to the current image data; The first fusion analysis unit obtains the band name and the corresponding data set of the multispectral image data if the image fusion mode is the first fusion mode, extracts the band data set of the high-resolution image data, fuses the high-resolution image band data with the specified multispectral image band data using a color fusion transformation algorithm to obtain complete fused data, processes these data using a resampling method, and transmits the processed results to a storage device; The second fusion analysis unit, if the image fusion mode is the second fusion mode, obtains the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, performs neighborhood smoothing processing on the panchromatic image data using the brightness smoothing filter adjustment model, fuses the processed panchromatic image data with the multispectral image data, and outputs the obtained results according to the selected format; The third fusion analysis unit, if the image fusion mode is the third fusion mode, performs principal component transformation on the multispectral image data to construct first principal component image information, transmits the spatial information corresponding to the panchromatic image to the first principal component image information, performs inverse principal component transformation operation on the image information, and obtains the final fusion result; The fourth fusion analysis unit, if the image fusion mode is the fourth fusion mode, performs a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has a similar mean and standard deviation, selects high-frequency data in the panchromatic image, and fuses the high-frequency image data with the multispectral image data through an image fusion formula to obtain a final fusion result; After the result display unit selects the fusion mode, it searches the storage device according to the fusion mode, obtains the corresponding fusion data, and transmits it to the visualization interface.

[0018] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0019] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source meteorological satellite image fusion processing method, characterized in that: The method comprises the following steps: S1. Selecting a fusion method: dividing the image fusion methods into four types, namely, a first fusion method, a second fusion method, a third fusion method and a fourth fusion method, and determining a corresponding image fusion method according to current image data; S2, executing the first fusion mode: if the image fusion mode is the first fusion mode, obtaining the band name of the multispectral image data and the corresponding data set, extracting the band data set of the high-resolution image data, using the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fused data, using the resampling method to process these data, and transmitting the processed results to the storage device; S3, executing the second fusion mode: if the image fusion mode is the second fusion mode, after obtaining the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, the panchromatic image data is subjected to neighborhood smoothing processing using a brightness smoothing filter adjustment model, the processed panchromatic image data is fused with the multispectral image data, and the obtained result is output according to the selected format; S4, executing the third fusion mode: if the image fusion mode is the third fusion mode, performing principal component transformation on the multispectral image data to construct first principal component image information, transmitting the spatial information corresponding to the panchromatic image to the first principal component image information, performing an inverse principal component transformation operation on the image information to obtain a final fusion result; S5, executing the fourth fusion method: if the image fusion method is the fourth fusion method, performing a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has a similar mean and standard deviation, selecting high-frequency data in the panchromatic image, and fusing the high-frequency image data with the multispectral image data through an image fusion formula to obtain a final fusion result; S6. Output fusion result: After selecting the fusion method, query in the storage device according to the fusion method to obtain the corresponding fusion data, and transmit it to the visualization interface.

2. The multi-source meteorological satellite image fusion processing method according to claim 1 is characterized in that: The S2 comprises the following steps: S201, if the image fusion mode is the first fusion mode, obtaining the band name of the multispectral image data and the corresponding data set, extracting the band data set of the high-resolution image data, and transferring the data to a storage device; S202, traversing the band names, determining the multispectral image band data to be processed and the corresponding high-resolution image band data, and using a color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fused data.

3. The multi-source meteorological satellite image fusion processing method according to claim 2 is characterized in that: The S2 further comprises the following steps: S203, selecting the format of the output file, outputting the fused multispectral image data according to the selected format, processing the data using a resampling method, and transmitting the processed results to a storage device.

4. The multi-source meteorological satellite image fusion processing method according to claim 1, characterized in that: The S3 specifically includes the following steps: S301, if the image fusion mode is the second fusion mode, after obtaining the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, perform neighborhood smoothing processing on the panchromatic image data, perform a simulated panchromatic image operation on the processed data, and generate new panchromatic image data; S302, using a brightness smoothing filter adjustment model to fuse the panchromatic image data with the multispectral image data, outputting the fused data in a selected format, processing the data using a resampling method, and transmitting the processed results to a storage device.

5. The multi-source meteorological satellite image fusion processing method according to claim 1 is characterized in that: The S4 specifically comprises the following steps: S401. If the image fusion mode is the third fusion mode, obtain the band name of the multispectral image data and the corresponding data set, perform principal component transformation on the multispectral image data, and concentrate the transformed image information in the principal component.

6. The multi-source meteorological satellite image fusion processing method according to claim 5 is characterized in that: The S4 specifically further comprises the following steps: S402, constructing first principal component image information according to the principal component components, after obtaining the panchromatic image data, performing a histogram matching operation on the panchromatic image data and the first principal component image information, transferring the spatial information corresponding to the panchromatic image to the first principal component image information, performing an inverse principal component transformation operation on the image information, and obtaining a final fusion result; S403: After the fusion result is output in the selected format, the multispectral image data is processed using a resampling method, and the processed result is transmitted to a storage device.

7. The multi-source meteorological satellite image fusion processing method according to claim 1 is characterized in that: The S5 specifically includes the following steps: S501, after obtaining the band names of the multispectral image data and the corresponding data set, perform a histogram adjustment operation on all the bands of the multispectral image and the panchromatic image bands so that each band has a similar mean and standard deviation; S502, after calculating the fitting coefficient using the least square method, after selecting the high-frequency data in the full-color image, the high-frequency image data is fused with the multispectral image data according to the fitting coefficient using an image fusion formula to obtain a final fusion result; S503: After the fusion result is output in the selected format, the multispectral image data is processed using a resampling method, and the processed result is transmitted to a storage device.

8. A multi-source meteorological satellite image fusion processing system, characterized in that: The fusion processing system is applicable to a multi-source meteorological satellite image fusion processing method according to any one of claims 1 to 7, comprising a mode selection unit, a first fusion analysis unit, a second fusion analysis unit, a third fusion analysis unit, a fourth fusion analysis unit and a result display unit; The mode selection unit divides the image fusion modes into four types, namely, a first fusion mode, a second fusion mode, a third fusion mode and a fourth fusion mode, and determines a corresponding image fusion mode according to current image data; If the image fusion mode is the first fusion mode, the first fusion analysis unit obtains the band name of the multispectral image data and the corresponding data set, extracts the band data set of the high-resolution image data, fuses the high-resolution image band data with the specified multispectral image band data using a color fusion transformation algorithm to obtain complete fused data, processes these data using a resampling method, and transmits the processed results to a storage device; If the image fusion mode is the second fusion mode, the second fusion analysis unit obtains the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, performs neighborhood smoothing processing on the panchromatic image data using a brightness smoothing filter adjustment model, fuses the processed panchromatic image data with the multispectral image data, and outputs the obtained results in the selected format; If the image fusion mode is the third fusion mode, the third fusion analysis unit performs principal component transformation on the multispectral image data to construct first principal component image information, transmits the spatial information corresponding to the panchromatic image to the first principal component image information, performs an inverse principal component transformation operation on the image information, and obtains a final fusion result; If the image fusion mode is the fourth fusion mode, the fourth fusion analysis unit performs a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has a similar mean and standard deviation, selects high-frequency data in the panchromatic image, and fuses the high-frequency image data with the multispectral image data through an image fusion formula to obtain a final fusion result; After selecting the fusion mode, the result display unit searches the storage device according to the fusion mode to obtain corresponding fusion data, and transmits the fusion data to the visualization interface.

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