A multi-source meteorological satellite image fusion processing method and system

Abstract

The present invention discloses a multi-source meteorological satellite image fusion processing method and system, which relates to the technical field of image processing and includes the following steps: S1, select a fusion method; S2, execute the first fusion method; S3, execute the second fusion method; S4, execute the third fusion method; S5, execute the fourth fusion method; and S6, output the fusion result. The present invention uses a color fusion transformation algorithm to fuse the high-resolution image band data with the specified multi-spectral image band data, so that the fused image has a sharpened effect, improving the quality of the image data. The second fusion method suppresses the high-frequency spatial information of the panchromatic image data through low-pass filtering and retains the low-frequency information, and then analyzes the multi-spectral image data and the image data passed through low-pass filtering. The fused image data enhances the texture structure information, and this method realizes the function of principal component analysis fusion, while increasing the spatial resolution and enriching the image information.

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 Technique

[0002] Meteorological satellite image fusion is to perform arithmetic processing on multi-source meteorological satellite data that is redundant or complementary 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 time characteristics. A data fusion method and device are disclosed in the invention patent with the application number CN202410069856.8. The method includes: obtaining panchromatic channel data through a high-resolution satellite, and obtaining first preprocessed data based on the panchromatic channel data; obtaining texture data of the first preprocessed data; obtaining brightness temperature data through a geostationary meteorological satellite, and obtaining second preprocessed data based on the brightness temperature data; fusing the texture data and the second preprocessed data to obtain fused data. The data fusion algorithm proposed by the present invention uses the high-resolution panchromatic channel data generated by the high-resolution satellite preprocessing subsystem. After preprocessing, it combines multi-channel brightness temperature and other data of the geostationary meteorological satellite or other geostationary meteorological satellites at the same or similar times to achieve the fusion of multi-channel brightness temperature and panchromatic channel data.

[0003] The above-mentioned prior art solves problems such as easy spectral distortion of image information. However, in the process of use, since multiple image fusion methods are not provided to users, all images can only perform the same operations during the fusion process, resulting in the inability to achieve better effects in the detail processing of some images, and 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 technique.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A multi-source meteorological satellite image fusion processing method, including the following steps:

[0006] S1. Select a fusion method: Divide the image fusion method into four types, namely the first fusion method, the second fusion method, the third fusion method, and the fourth fusion method, and determine the corresponding image fusion method according to the current image data;

[0007] S2. Execute the first fusion method: When the image fusion method is the first fusion method, obtain the band names of the multispectral image data and the corresponding data sets. After extracting the band data set of the high-resolution image data, use the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data, and use the resampling method to process these data. The processed results are transmitted to the storage device;

[0008] S3. Execute the second fusion method: When the image fusion method is the second fusion method, obtain the band names of the panchromatic image data and the multispectral image data and the corresponding data sets. Then, use the luminance smoothing filter adjustment model to perform neighborhood smoothing processing on the panchromatic image data, and fuse the processed panchromatic image data with the multispectral image data. The obtained results are output in the selected format;

[0009] S4. Execute the third fusion method: When the image fusion method is the third fusion method, perform principal component transformation on the multispectral image data to construct the first principal component image information. Transmit the spatial information corresponding to the panchromatic image to the first principal component image information, and perform an inverse principal component transformation operation on this image information to obtain the final fusion result;

[0010] S5. Execute the fourth fusion method: When the image fusion method is the fourth fusion method, perform histogram adjustment operations on all bands in the multispectral image and the panchromatic image band so that each band has similar mean values and standard deviations. Select the high-frequency data in the panchromatic image, and fuse the high-frequency image data with the multispectral image data through the image fusion formula to obtain the final fusion result;

[0011] S6. Output the 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.

[0012] Preferably, the S2 includes the following steps:

[0013] S201. When the image fusion method is the first fusion method, obtain the band names of the multispectral image data and the corresponding data sets. After extracting the band data set of the high-resolution image data, transmit these data to the storage device;

[0014] S202. Traverse the band names to determine the multispectral image band data to be processed and the corresponding high-resolution image band data, and use the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data. The color fusion transformation algorithm is specifically:

[0015]

[0016] In the formula, respectively represent the corresponding values of the fused multispectral image data, represents the corresponding value of the panchromatic image data after resizing, represents the corresponding value of the multispectral image data after resizing.

[0017] Preferably, step S2 further includes the following steps:

[0018] S203. Select the format of the output file, output the fused multispectral image data in the selected format, process these data using the resampling method, and transfer the processed result to the storage device.

[0019] Preferably, step S3 specifically includes the following steps:

[0020] S301. When the image fusion method is the second fusion method, after obtaining the band names and corresponding data sets of the panchromatic image data and the multispectral image data, perform neighborhood smoothing processing on the panchromatic image data, and perform simulated panchromatic image operations on the processed data to generate new panchromatic image data;

[0021] S302. Use the brightness smoothing filtering adjustment model to fuse the panchromatic image data and the multispectral image data, output the fused data in the selected format, process these data using the resampling method, and transfer the processed result to the storage device. The brightness smoothing filtering adjustment model is specifically:

[0022]

[0023] In the formula, represents the fused data, represents the irradiance corresponding to the panchromatic image data, represents the surface reflectance of the ground object corresponding to the panchromatic image data, represents the band number of the current image, represents the band number of the panchromatic image.

[0024] Preferably, step S4 specifically includes the following steps:

[0025] S401. When the image fusion method is the third fusion method, obtain the band names and corresponding data sets of the multispectral image data, perform principal component transformation on the multispectral image data, and the image information after transformation is concentrated in the principal component components.

[0026] Preferably, step S4 specifically further includes the following steps:

[0027] S402. Construct the first principal component image information based on the principal component components. After obtaining the panchromatic image data, perform histogram matching on the panchromatic image data and the first principal component image information, transfer the spatial information corresponding to the panchromatic image into the first principal component image information, and perform inverse principal component transformation on the image information to obtain the final fusion result;

[0028] S403. After outputting the fusion result in the selected format, process the multispectral image data using the resampling method, and transfer the processed result to the storage device.

[0029] Preferably, the S5 specifically includes the following steps:

[0030] S501. After obtaining the band names and corresponding data sets of the multispectral image data, perform histogram adjustment on all bands of the multispectral image and the panchromatic image band so that each band has similar mean and standard deviation;

[0031] S502. After calculating the fitting coefficients using the least squares method, select the high-frequency data in the panchromatic image, and fuse the high-frequency image data and the multispectral image data according to the fitting coefficients through the image fusion formula. The image fusion formula is specifically:

[0032]

[0033] In the formula, respectively represent the fused data values, represents the panchromatic image data value, represents the high-frequency image data value, represents the multispectral image data value;

[0034] S503. After outputting the fusion result in the selected format, process the multispectral image data using the resampling method, and transfer the processed result to the storage device.

[0035] The multi-source meteorological satellite image fusion processing system includes a method 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;

[0036] The method selection unit divides the image fusion methods into four types, namely the first fusion method, the second fusion method, the third fusion method, and the fourth fusion method, and determines the corresponding image fusion method according to the current image data;

[0037] When the image fusion method of the first fusion analysis unit is the first fusion method, it obtains the band names of the multispectral image data and the corresponding data sets. After extracting the band data set of the high-resolution image data, it uses the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data, and uses the resampling method to process these data. The processed results are transmitted to the storage device;

[0038] When the image fusion method of the second fusion analysis unit 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, it uses the luminance smoothing filtering adjustment model to perform neighborhood smoothing processing on the panchromatic image data, and fuses the processed panchromatic image data with the multispectral image data. The obtained results are output in the selected format;

[0039] When the image fusion method of the third fusion analysis unit is the third fusion method, it performs principal component transformation on the multispectral image data to construct the first principal component image information, transmits the spatial information corresponding to the panchromatic image to the first principal component image information, and performs an inverse principal component transformation operation on this image information to obtain the final fusion result;

[0040] When the image fusion method of the fourth fusion analysis unit is the fourth fusion method, it performs histogram adjustment operations on all the bands in the multispectral image and the panchromatic image band so that each band has similar mean values and standard deviations, selects the high-frequency data in the panchromatic image, and fuses the high-frequency image data with the multispectral image data through the image fusion formula to obtain the final fusion result;

[0041] After the result display unit selects the fusion method, it queries in the storage device according to the fusion method to obtain the corresponding fusion data, and transmits it to the visualization interface.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] The present invention uses the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data, so that the fused image has a sharpened effect, improving the quality of the image data. The second fusion method suppresses the high-frequency spatial information of the panchromatic image data through low-pass filtering and retains the low-frequency information, and then analyzes the multispectral image data and the image data passed through low-pass filtering. The fused image data enhances the texture structure information, and this method realizes the function of principal component analysis fusion, can also automatically align the panchromatic band data and the multispectral band data, retains the spectral information, increases the spatial resolution and enriches the image information at the same time. Brief Description of the Drawings

[0044] Figure 1 This is the overall method flowchart provided by the embodiments of the present invention;

[0045] Figure 2 This is the method flowchart for executing the first fusion method provided by the embodiments of the present invention;

[0046] Figure 3 This is the method flowchart for executing the third fusion method provided by the embodiments of the present invention;

[0047] Figure 4 This is the method flowchart for executing the fourth fusion method provided by the embodiments of the present invention. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] Please refer to Figures 1 - 4 , the present invention provides a technical solution: a multi-source meteorological satellite image fusion processing method, including the following steps:

[0050] S1. Select a fusion method: Divide the image fusion method into four types, namely the first fusion method, the second fusion method, the third fusion method, and the fourth fusion method, and determine the corresponding image fusion method according to the current image data;

[0051] S2. Execute the first fusion method: When the image fusion method is the first fusion method, obtain the band names of the multi-spectral image data and the corresponding data sets, extract the band data sets of the high-resolution image data, and then use the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multi-spectral image band data to obtain complete fusion data, and use the resampling method to process these data, and the processed results are transmitted to the storage device;

[0052] S3. Execute the second fusion method: When the image fusion method is the second fusion method, obtain the band names of the panchromatic image data and the multi-spectral image data and the corresponding data sets, then use the brightness smoothing filtering adjustment model to perform neighborhood smoothing processing on the panchromatic image data, and fuse the processed panchromatic image data with the multi-spectral image data, and the obtained results are output in the selected format;

[0053] S4. Perform the third fusion method: When the image fusion method is the third fusion method, perform a principal component transformation on the multispectral image data to construct the first principal component image information. Transmit the spatial information corresponding to the panchromatic image to the first principal component image information, and perform an inverse principal component transformation operation on this image information to obtain the final fusion result;

[0054] S5. Perform the fourth fusion method: When the image fusion method is the fourth fusion method, perform a histogram adjustment operation on all bands in the multispectral image and the panchromatic image band so that each band has similar mean and standard deviation. Select the high-frequency data in the panchromatic image, and fuse the high-frequency image data and the multispectral image data through the image fusion formula to obtain the final fusion result;

[0055] S6. Output the 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.

[0056] S2 includes the following steps:

[0057] S201. When the image fusion method is the first fusion method, use the remote sensing data platform to obtain the band names and corresponding data sets of the multispectral image data. After extracting the band data sets of the high-resolution image data through ENVI software, transmit the band data sets to the storage device;

[0058] S202. Use the automatic band recognition engine to traverse the band names in the multispectral image data and the high-resolution image data to determine the multispectral image band data to be processed and the corresponding high-resolution image band data, and establish the corresponding relationship between the high-resolution image band data and the multispectral image band data. Use the color fusion transformation algorithm to fuse the high-resolution image band data and the specified multispectral image band data to obtain the complete fusion data. The color fusion transformation algorithm is specifically:

[0059]

[0060] In the formula, respectively represent the corresponding values of the fused multispectral image data, represents the corresponding value of the resized panchromatic image data, represents the corresponding value of the resized multispectral image data;

[0061] S2 also includes the following steps:

[0062] S203. Select the format of the output file through ENVI software, output the fused multispectral image data in the selected format, adjust the resolution of the multispectral image data using the bilinear interpolation method, and transfer the adjusted image data to the storage device;

[0063] S3 specifically includes the following steps:

[0064] S301. When the image fusion method is the second fusion method, after obtaining the band names and corresponding data sets of the panchromatic image data and multispectral image data using the remote sensing data platform, use the sliding window to perform neighborhood smoothing processing on the panchromatic image data, and perform simulated panchromatic image operations on the processed data through the weighted fusion method to generate new panchromatic image data;

[0065] S302. Use the brightness smoothing filtering adjustment model to fuse the panchromatic image data and the multispectral image data. After outputting the fused data in the selected format through ENVI software, perform resampling operations on the fused image data according to the set target resolution using the bilinear interpolation method, and transfer the processed results to the storage device. The brightness smoothing filtering adjustment model is specifically:

[0066]

[0067] In the formula, represents the fused data, represents the irradiance corresponding to the panchromatic image data, represents the surface reflectance of the ground object corresponding to the panchromatic image data, represents the band number of the current image, represents the band number of the panchromatic image;

[0068] S4 specifically includes the following steps:

[0069] S401. When the image fusion method is the third fusion method, obtain the band names and corresponding data sets of the multispectral image data using the remote sensing data platform, perform principal component transformation on the multispectral image data through the processor, calculate the covariance matrix between the bands in the multispectral image data, and generate principal component components using the covariance matrix;

[0070] S4 specifically further includes the following steps:

[0071] S402. Construct the first principal component image information based on the principal component components. After obtaining the panchromatic image data using the remote sensing data platform, perform histogram matching operations on the panchromatic image data and the first principal component image information through ENVI software, transfer the spatial information corresponding to the panchromatic image to the first principal component image information, and perform inverse principal component transformation operations on this image information to obtain the final fusion result;

[0072] S403. After outputting the fusion result in the selected format using ENVI software, process the multispectral image data using the resampling method, and transfer the processed result to the storage device;

[0073] S5 specifically includes the following steps:

[0074] 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 band through ENVI software so that each band has similar mean and standard deviation;

[0075] S502. After calculating the fitting coefficients using the least squares method, select the high-frequency data in the panchromatic image, and fuse the high-frequency image data and the multispectral image data according to the fitting coefficients through the image fusion formula to obtain the final fusion result. The specific image fusion formula is:

[0076]

[0077] In the formula, respectively represent the fused data values, represents the panchromatic image data value, represents the high-frequency image data value, represents the multispectral image data value;

[0078] S503. After outputting the fusion result in the selected format using ENVI software, process the multispectral image data using the resampling method, and transfer the processed result to the storage device;

[0079] A multi-source meteorological satellite image fusion processing system includes a method 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;

[0080] The method selection unit divides the image fusion methods into four types, namely the first fusion method, the second fusion method, the third fusion method, and the fourth fusion method, and determines the corresponding image fusion method according to the current image data;

[0081] When the image fusion method of the first fusion analysis unit is the first fusion method, it obtains the band names of the multispectral image data and the corresponding data sets. After extracting the band data set of the high-resolution image data, it uses the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data, and uses the resampling method to process these data, and the processed results are transmitted to the storage device;

[0082] When the image fusion method of the second fusion analysis unit 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, it uses the luminance smoothing filtering adjustment model to perform neighborhood smoothing processing on the panchromatic image data, and fuses the processed panchromatic image data with the multispectral image data, and the obtained results are output in the selected format;

[0083] When the image fusion method of the third fusion analysis unit is the third fusion method, it performs a principal component transformation on the multispectral image data to construct the first principal component image information, transmits the spatial information corresponding to the panchromatic image to the first principal component image information, and performs an inverse principal component transformation operation on this image information to obtain the final fusion result;

[0084] When the image fusion method of the fourth fusion analysis unit is the fourth fusion method, it performs a histogram adjustment operation on all the bands in the multispectral image and the panchromatic image band so that each band has similar mean and standard deviation, selects the high-frequency data in the panchromatic image, and fuses the high-frequency image data with the multispectral image data through the image fusion formula to obtain the final fusion result;

[0085] After selecting the fusion method, the result display unit queries in the storage device according to the fusion method, obtains the corresponding fusion data, and transmits it to the visualization interface.

[0086] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is 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 expressly listed, or elements inherent to such process, method, article or device.

[0087] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and 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 includes the following steps: S1. Select a fusion method: Divide the image fusion methods into four types, namely the first fusion method, the second fusion method, the third fusion method, and the fourth fusion method, and determine the corresponding image fusion method according to the current image data; S2. Execute the first fusion method: When the image fusion method is the first fusion method, obtain the band names of the multispectral image data and the corresponding data sets. After extracting the band data sets of the high-resolution image data, use the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data, and use the resampling method to process these data. The processed results are transmitted to the storage device; S3. Execute the second fusion method: When the image fusion method is the second fusion method, obtain the band names of the panchromatic image data and the multispectral image data and the corresponding data sets, and use the luminance smoothing filter adjustment model to perform neighborhood smoothing processing on the panchromatic image data. Then fuse the processed panchromatic image data with the multispectral image data, and output the obtained results in the selected format; S4. Execute the third fusion method: When the image fusion method is the third fusion method, perform a principal component transformation on the multispectral image data to construct the first principal component image information. Transmit the spatial information corresponding to the panchromatic image to the first principal component image information, and perform an inverse principal component transformation operation on this image information to obtain the final fusion result; S5. Execute the fourth fusion method: When the image fusion method is the fourth fusion method, perform a histogram adjustment operation on all the bands in the multispectral image and the panchromatic image band so that each band has similar mean and standard deviation. Select the high-frequency data in the panchromatic image, and fuse the high-frequency image data with the multispectral image data through the image fusion formula to obtain the final fusion result; S6. Output the 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, wherein: The above S2 includes the following steps: S201. When the image fusion method is the first fusion method, obtain the band names of the multispectral image data and the corresponding data sets. After extracting the band data sets of the high-resolution image data, transmit these data to the storage device; S202. Traverse the band names to determine the multispectral image band data to be processed and the corresponding high-resolution image band data, and use the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data.

3. A multi-source meteorological satellite image fusion processing method according to claim 2, characterized in that: The above S2 also includes the following steps: S203. Select the format of the output file, output the fused multispectral image data in the selected format, and use the resampling method to process these data. The processed results are transmitted to the storage device.

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

5. A multi-source meteorological satellite image fusion processing method according to claim 1, characterized in that: The specific steps of S4 are as follows: S401. When the image fusion method is the third fusion method, obtain the band names and corresponding data sets of the multispectral image data, and perform principal component transformation on the multispectral image data. After the transformation, the image information is concentrated in the principal component components.

6. A multi-source meteorological satellite image fusion processing method according to claim 5, characterized in that: The specific steps of S4 also include the following: S402. Construct the first principal component image information according to the principal component components. After obtaining the panchromatic image data, perform histogram matching operations on the panchromatic image data and the first principal component image information, transfer the spatial information corresponding to the panchromatic image to the first principal component image information, and perform inverse principal component transformation operations on this image information to obtain the final fusion result; S403. After outputting the fusion result in the selected format, use the resampling method to process the multispectral image data, and transfer the processed results to the storage device.

7. A multi-source meteorological satellite image fusion processing method according to claim 1, characterized in that: The specific steps of S5 are as follows: S501. After obtaining the band names and corresponding data sets of the multispectral image data, perform histogram adjustment operations on all bands of the multispectral image and the panchromatic image band so that each band has similar mean values and standard deviations; S502. After calculating the fitting coefficients using the least squares method, select the high-frequency data in the panchromatic image, and fuse the high-frequency image data with the multispectral image data according to the fitting coefficients through the image fusion formula to obtain the final fusion result; S503. After outputting the fusion result in the selected format, use the resampling method to process the multispectral image data, and transfer the processed results to the 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 1-7, including a method 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 method selection unit divides the image fusion method into four types, namely the first fusion method, the second fusion method, the third fusion method, and the fourth fusion method, and determines the corresponding image fusion method according to the current image data; When the image fusion method of the first fusion analysis unit is the first fusion method, it obtains the band names of the multispectral image data and the corresponding data sets. After extracting the band data set of the high-resolution image data, it uses the color fusion transformation algorithm to fuse the high-resolution image band data with the specified multispectral image band data to obtain complete fusion data, and uses the resampling method to process these data. The processed results are transmitted to the storage device; When the image fusion method of the second fusion analysis unit 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, it uses the luminance smoothing filtering adjustment model to perform neighborhood smoothing processing on the panchromatic image data, and fuses the processed panchromatic image data with the multispectral image data. The obtained results are output in the selected format; When the image fusion method of the third fusion analysis unit is the third fusion method, it performs principal component transformation on the multispectral image data to construct the first principal component image information, transmits the spatial information corresponding to the panchromatic image to the first principal component image information, and performs an inverse principal component transformation operation on this image information to obtain the final fusion result; When the image fusion method of the fourth fusion analysis unit is the fourth fusion method, it performs histogram adjustment operations on all the bands in the multispectral image and the panchromatic image band so that each band has similar mean values and standard deviations. It selects the high-frequency data in the panchromatic image, and fuses the high-frequency image data with the multispectral image data through the image fusion formula to obtain the final fusion result; After the result display unit selects the fusion method, it queries in the storage device according to the fusion method to obtain the corresponding fusion data, and transmits it to the visualization interface.

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