Multispectral remote sensing image fusion method, system, terminal device and storage medium

Abstract

The application discloses a multispectral remote sensing image fusion method and system, a terminal device and a storage medium. The method comprises the following steps: acquiring original multispectral images and original panchromatic remote sensing images; preprocessing the original multispectral images and the original panchromatic remote sensing images; performing fitting calculation on the preprocessed multispectral images and panchromatic remote sensing images to obtain fitting coefficients; calculating a fitted panchromatic image by using the fitting coefficients and the preprocessed panchromatic remote sensing images; upsampling the fitted panchromatic image so that the fitted panchromatic image and the original panchromatic remote sensing image have the same sampling interval; and fusing the original panchromatic image, the fitted panchromatic image and the multispectral image to obtain a fusion result. The application solves the problem that the existing fusion technology cannot simultaneously retain the spatial resolution of the panchromatic image and the spectral resolution of the multispectral image, and makes the image change and lead to unstable fusion results and poor reliability.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image fusion technology, and in particular to a multispectral remote sensing image fusion method, system, terminal equipment, and storage medium. Background Technology

[0002] Detection equipment typically incorporates multiple image acquisition carriers, enabling the simultaneous acquisition of low-resolution multispectral and high-resolution panchromatic images. To achieve high-resolution multispectral remote sensing imagery, multispectral remote sensing image fusion applications have emerged. Numerous multispectral remote sensing image fusion algorithms exist, with commonly used ones including Principal Component Analysis (PCA), Integral Color Space Transformation (IHS), and Gram-Schmidt Transform (GS) fusion. However, these commonly used fusion algorithms struggle to simultaneously preserve the spatial resolution of panchromatic images and the spectral resolution of multispectral images. Furthermore, variations in the images can lead to inconsistent fusion results. Therefore, a multispectral image fusion algorithm is needed that ensures the fused image possesses both the spatial and spectral resolutions of the panchromatic image, without becoming unstable due to differences in the images themselves. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a multispectral remote sensing image fusion method, system, terminal equipment and storage medium to address the shortcomings of the existing technology. This invention solves the problem that the existing image fusion methods are difficult to retain multiple information in the fusion processing results and are affected by image changes. It complements the information of panchromatic and multispectral images, dynamically calculates the parameters between multispectral and panchromatic images, and improves the stability of the fusion results.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a multispectral remote sensing image fusion method, comprising the following steps:

[0005] S1. Acquire raw multispectral imagery and raw panchromatic remote sensing imagery;

[0006] S2. Preprocess the original multispectral image and the original panchromatic remote sensing image;

[0007] S3. Perform fitting calculations on the preprocessed multispectral and panchromatic remote sensing images to obtain the fitting coefficients;

[0008] S4. Calculate the fitted panchromatic image using the fitting coefficients and the preprocessed panchromatic remote sensing image.

[0009] S5. Upsample the fitted panchromatic image so that the sampling interval of the fitted panchromatic image is the same as that of the original panchromatic remote sensing image.

[0010] S6. Fuse the original panchromatic image, the fitted panchromatic image, and the multispectral image to obtain the fusion result.

[0011] This invention utilizes multispectral and panchromatic remote sensing images. By normalizing both images and then fitting them together, fitting coefficients are obtained between each channel of the panchromatic and multispectral images. These coefficients are then used to simulate and generate a fitted panchromatic image. The coefficients for each pixel are calculated using the fitted panchromatic image and the original panchromatic image. Finally, these coefficients are used to fuse the multispectral remote sensing images with an upsampled multispectral image. This invention enables information complementarity between panchromatic and multispectral images, dynamically calculating parameters between the two, ensuring the fusion result is unaffected by time and location. It solves the problems of existing image fusion methods, which struggle to retain multiple information elements and are susceptible to changes in image quality, thus improving the stability of the fusion result.

[0012] In step S2, the specific implementation process of preprocessing the original panchromatic remote sensing image includes: downsampling the original panchromatic remote sensing image to make the sampling interval of the original multispectral image and the original panchromatic remote sensing image the same.

[0013] This invention preprocesses the original multispectral image and the original panchromatic remote sensing image. This preprocessing is actually a normalization process. Since the panchromatic remote sensing image has a high resolution in the original acquired image, downsampling is performed on the panchromatic remote sensing image to downsample it to the same sampling interval as the multispectral image.

[0014] After step S1, the method further includes: performing an upsampling operation on the original multispectral image so that the size of the original multispectral image is the same as the size of the original panchromatic image; calculating the mean and variance of the upsampled multispectral image; and stretching the upsampled multispectral image using the mean and variance. Then, the multispectral image in step S6 is the stretched multispectral image.

[0015] In this invention, the original multispectral image is upsampled so that the size of the original multispectral image is the same as the size of the original panchromatic image, that is, the sampling interval of the original multispectral image is the same as the sampling interval of the original panchromatic image.

[0016] It should be noted that the upsampling operation on the original multispectral image can be performed at any time before step S6.

[0017] In step S3, the formula for calculating the fitting coefficient is:

[0018] [a1,a2,a3,a4]=PanV (i,j) / [MS 1(i,j) MS2(i,j) MS 3(i,j) MS 4(i,j) ];

[0019] Where MS1, MS2, MS3, and MS4 are the images of the four channels of the downsampled multispectral image; i and j represent the row and column numbers of the pixels in the image, respectively; MS 1(i,j) MS 2(i,j) MS 3(i,j) MS 4(i,j) PanV represents the pixel value of the pixel in the i-th row and j-th column of the image in each of the four channels; (i,j) denoted as the pixel value of the i-th row and j-th column of the panchromatic image after downsampling the original panchromatic image; a1, a2, a3, and a4 are the fitting coefficients of the four channels of the downsampled multispectral image, respectively.

[0020] In this invention, to improve efficiency, the original multispectral image and the preprocessed panchromatic image need to be downsampled during fitting. The sampling interval is 8 times or higher than that of the multispectral image. For example, if the sampling interval of the original multispectral image is 4 meters, the sampling interval of the image during fitting is 32 meters after downsampling (calculated at 8 times). This greatly reduces the amount of calculation during fitting.

[0021] In step S4 of this invention, the pixel in the i-th row and j-th column of the fitted panchromatic image is represented as Pan_s. (i,j) :

[0022] Pan_s (i,j) =a1*MS 1(i,j) +a2*MS 2(i,j) +a3*MS 3(i,j) +a4*MS 4(i,j) .

[0023] In this invention, the fitted panchromatic image is calculated by multiplying the four channels of the multispectral image with the corresponding fitting parameters. Therefore, the sampling interval of the fitted panchromatic image is consistent with that of the multispectral image.

[0024] In step S5 of this invention, the fitted panchromatic image is upsampled using a bilinear interpolation method or a cubic convolution interpolation method.

[0025] In step S6 of this invention, the formula for calculating the fusion result PanSharp is: PanSharp=MS*(SynPan / Pan); where MS represents the image after sampling the original multispectral image to the same sampling interval as the original panchromatic image, Pan represents the original panchromatic image, and SynPan represents the fitted panchromatic image.

[0026] As an inventive concept, the present invention also provides a multispectral remote sensing image fusion system, which includes:

[0027] The acquisition unit is used to acquire raw multispectral images and raw panchromatic remote sensing images;

[0028] The preprocessing unit is used to preprocess the original multispectral image and the original panchromatic remote sensing image.

[0029] The fitting unit is used to perform fitting calculations on multispectral images and preprocessed panchromatic remote sensing images to obtain fitting coefficients.

[0030] The calculation unit is used to calculate the fitted panchromatic image using the fitting coefficients and the preprocessed panchromatic remote sensing image;

[0031] An upsampling unit is used to upsample the fitted panchromatic image so that the sampling interval of the fitted panchromatic image is the same as that of the original panchromatic remote sensing image.

[0032] The fusion unit is used to fuse the original panchromatic image, the fitted panchromatic image, and the multispectral image to obtain the fusion result.

[0033] As an inventive concept, the present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the method described above.

[0034] As an inventive concept, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon; when the computer program / instructions are executed by a processor, they implement the steps of the method described above.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention solves the problem that existing fusion technologies cannot simultaneously retain the spatial resolution of panchromatic images and the spectral resolution of multispectral images, leading to unstable fusion results and poor reliability due to image variations. This invention achieves multispectral remote sensing image fusion by fitting a panchromatic image and calculating the coefficients of each pixel in the panchromatic image. These coefficients are then used in conjunction with the upsampled multispectral image to calculate the fusion results. This allows for information complementarity between the panchromatic and multispectral images, dynamically calculating the parameters between the multispectral and panchromatic images, making the fusion results unaffected by time and location, and improving the stability and reliability of the fusion results. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0037] Figure 2(a) and Figure 2(b) show the overall view comparison before and after WorldView3 satellite image fusion; Figure 2(a) is the original multispectral image, and Figure 2(b) is the fused result;

[0038] Figures 3(a) to 3(c) Figure 3(a) shows the detail comparison before and after WorldView 3 satellite image fusion; Figure 3(b) shows the original panchromatic image, Figure 3(c) shows the original multispectral image, and Figure 3(c) shows the fusion result.

[0039] Figures 4(a) and 4(b) show detailed images of the WorldView 3 satellite image fusion results;

[0040] Figure 5(a) and Figure 5(b) show the overall view comparison before and after fusion of GF1 satellite images; Figure 5(a) is the original multispectral image, and Figure 5(b) is the fusion result;

[0041] Figures 6(a) to 6(c) Figure 6(a) shows the detail comparison before and after fusion of GF1 satellite images; Figure 6(b) shows the original panchromatic image, Figure 6(c) shows the original multispectral image, and Figure 6(c) shows the fusion result.

[0042] Figures 7(a) and 7(b) show detailed images of the GF1 satellite image fusion results;

[0043] Figures 8(a) to 8(c) Figure 8(a) shows the overall view before and after SPOT6 satellite image fusion; Figure 8(b) shows the original panchromatic image, Figure 8(c) shows the original multispectral image, and Figure 8(c) shows the fusion result.

[0044] Figure 9(a) and Figure 9(b) show a comparison of details before and after SPOT6 satellite image fusion; Figure 9(a) is the original multispectral image, and Figure 9(b) is the fusion result.

[0045] Figures 10(a) and 10(b) show detailed images of the SPOT6 satellite image fusion results;

[0046] Figures 11(a) to 11(c) Figure 11(a) shows the overall view before and after QuickBird satellite image fusion; Figure 11(b) shows the original panchromatic image, Figure 11(c) shows the original multispectral image, and Figure 11(c) shows the fusion result.

[0047] Figures 12(a) to 12(c) Figure 12(a) shows the detail comparison before and after QuickBird satellite image fusion; Figure 12(b) shows the original panchromatic image, Figure 12(c) shows the original multispectral image, and Figure 12(c) shows the fusion result.

[0048] Figures 13(a) to 13(c) Figure 13(a) shows the detail comparison before and after QuickBird satellite image fusion; Figure 13(b) shows the original panchromatic image, Figure 13(c) shows the original multispectral image, and Figure 13(c) shows the fusion result.

[0049] Figures 14(a) and 14(b) show the overall view comparison before and after the fusion of ZY3 satellite images; Figure 14(a) is the original multispectral image, and Figure 14(b) is the fused result.

[0050] Figures 15(a) to 15(c) Figure 15(a) shows the detail comparison before and after the fusion of ZY3 satellite images; Figure 15(b) shows the original panchromatic image, Figure 15(c) shows the original multispectral image, and Figure 15(c) shows the fusion result.

[0051] Figures 16(a) to 16(c) Figure 16(a) shows the detail comparison before and after the fusion of ZY3 satellite images; Figure 16(b) shows the original panchromatic image, Figure 16(c) shows the original multispectral image, and Figure 16(c) shows the fusion result.

[0052] Figures 17(a) and 17(b) show the overall view comparison before and after GeoEye satellite image fusion; Figure 17(a) is the original multispectral image, and Figure 17(b) is the fusion result.

[0053] Figures 18(a) to 18(c) Figure 18(a) shows the details before and after GeoEye satellite image fusion; Figure 18(b) shows the original panchromatic image, Figure 18(c) shows the original multispectral image, and Figure 18(c) shows the fusion result.

[0054] Figures 19(a) and 19(b) show the overall view comparison before and after fusion of GF2 satellite images; Figure 19(a) is the original multispectral image, and Figure 19(b) is the fusion result.

[0055] Figures 20(a) to 20(c) Figure 20(a) shows the detail comparison before and after fusion of GF2 satellite images; Figure 20(b) shows the original panchromatic image, Figure 20(c) shows the original multispectral image, and Figure 20(c) shows the fusion result.

[0056] Figures 21(a) and 21(b) show the overall view comparison before and after Landsat 8 satellite image fusion; Figure 21(a) is the original multispectral image, and Figure 21(b) is the fused result.

[0057] Figures 22(a) and 22(b) show a comparison of details before and after Landsat 8 satellite image fusion; Figure 22(a) is the original multispectral image, and Figure 22(b) is the fused result.

[0058] Figures 23(a) to 23(c) Figure 23(a) shows the details before and after Landsat 8 satellite image fusion; Figure 23(b) shows the original panchromatic image; Figure 23(c) shows the original multispectral image; Figure 23(c) shows the fusion result. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] In this document, the terms "first," "second," and other similar words are not intended to imply any order, quantity, or importance, but are merely used to distinguish different elements. The terms "one," "a," and other similar words are not intended to indicate the existence of only one of the stated things, but rather that the description pertains to only one of the two stated things, which may include one or more. The terms "comprising," "including," and other similar words are intended to indicate a logical relationship, not a spatial relationship. For example, "A includes B" means that logically B belongs to A, not that spatially B is located inside A. Furthermore, the meanings of the terms "comprising," "including," and other similar words should be considered open-ended, not closed. For example, "A includes B" means that B belongs to A, but B does not necessarily constitute all of A; A may also include other elements such as C, D, and E.

[0061] Example 1

[0062] The method in this embodiment can normalize multispectral and panchromatic remote sensing images by calculating the mean and standard deviation of both images. Then, it fits the panchromatic and multispectral images to obtain fitting coefficients between each channel of the multispectral and multispectral images. These coefficients are then used to simulate and generate a fitted panchromatic image. The coefficients for each pixel are calculated using the fitted panchromatic image and the original panchromatic image. Finally, these coefficients are used to calculate the fusion result with the upsampled multispectral image, thus achieving the fusion of multispectral remote sensing images.

[0063] like Figure 1 As shown, the specific implementation process of the method in this embodiment includes:

[0064] S1. Acquire raw multispectral imagery and raw panchromatic remote sensing imagery;

[0065] S2. Normalize the original multispectral image and the original panchromatic remote sensing image.

[0066] S3. Fit the normalized multispectral image and panchromatic remote sensing image to obtain the fitting coefficients;

[0067] S4. Calculate the fitted panchromatic image using the fitting coefficients and the normalized panchromatic image from step S2.

[0068] S5. Upsample the fitted panchromatic image to the same sampling interval as the original panchromatic remote sensing image.

[0069] S6. Calculate the mean and standard deviation of each channel of the upsampled multispectral image, and stretch the multispectral image based on the calculation results;

[0070] S7. Calculate the fusion result by combining the original panchromatic image, the fitted panchromatic image, and the multispectral image.

[0071] Step 6 above can also be performed after step S1.

[0072] In step S2, the normalization process involves downsampling the original panchromatic remote sensing image to the same sampling interval. The panchromatic remote sensing image has a high resolution in the original acquired image; therefore, downsampling is performed on the panchromatic remote sensing image, downsampling it to the same sampling interval as the multispectral image.

[0073] The specific steps for fitting calculation in step S3 are as follows:

[0074] Each channel of the multispectral image is treated as a separate variable, with four channels (blue, green, red, and near-infrared) corresponding to four variables, and the panchromatic remote sensing image as a single variable, resulting in a total of five variables for parameter fitting. The panchromatic remote sensing image is used as the dependent variable, and the variables from the four channels of the multispectral image are used as independent variables for fitting. To improve efficiency, both the multispectral and panchromatic images are downsampled during fitting, with a sampling interval eight times or more than that of the multispectral image. For example, if the original multispectral image has a sampling interval of 4 meters, the image used for fitting, after downsampling (calculated at a factor of 8), has a sampling interval of 32 meters. This significantly reduces the computational load during fitting.

[0075] Suppose that a certain pixel value of a panchromatic image can be calculated from the pixel values ​​corresponding to the four channels of a multispectral image, i.e., the following equation exists:

[0076] PanV (i,j) =a1*MS 1(i,j) +a2*MS 2(i,j) +a3*MS 3(i,j) +a4*MS 4(i,j) (1)

[0077] Represented in matrix form as follows:

[0078] PanV (i,j) =[a1,a2,a3,a4]*[MS 1(i,j) MS 2(i,j) MS 3(i,j)MS 4(i,j) (2)

[0079] but:

[0080] [a1,a2,a3,a4]=PanV (i,j) / [MS 1(i,j) MS 2(i,j) MS 3(i,j) MS 4(i,j) (3)

[0081] Where: PanV is the downsampled panchromatic image; MS1, MS2, MS3, and MS4 are the four channels of the downsampled multispectral image; i, j: the row and column numbers of the pixels in the image.

[0082] The specific steps for fitting the panchromatic image in step S4 are as follows:

[0083] The parameters calculated using Equation 3 can be fitted using the following equation to obtain the fitted panchromatic image (Pan_s), i.e., the fitting formula is:

[0084] Pan_s (i,j) =a1*MS 1(i,j) +a2*MS 2(i,j) +a3*MS 3(i,j) +a4*MS 4(i,j) (4)

[0085] The fitted panchromatic image is calculated by multiplying the four channels of the multispectral image with the corresponding fitting parameters. Therefore, the sampling interval of the fitted panchromatic image is consistent with that of the multispectral image.

[0086] The upsampling process in step S5 is as follows: the fitted panchromatic image is upsampled to the original panchromatic image sampling interval. The upsampling method uses bilinear interpolation or cubic convolution interpolation.

[0087] The specific operations for stretching the multispectral image and panchromatic image in step S6 are as follows:

[0088] In the embodiments of this invention, the sampling interval refers to the spatial sampling interval of pixels; multispectral image fusion is to complement the information between panchromatic and multispectral images. The original panchromatic image is a single-band image with high spatial resolution, while the original multispectral image is a multi-channel image with lower resolution. The fused image is a multi-channel image (consistent with the multispectral channels).

[0089] The stretching process in this embodiment includes: calculating the mean and standard deviation of each multispectral channel and the panchromatic image; normalizing each multispectral channel and the panchromatic image with the maximum variance as the target variance and the sum of the means as the target mean.

[0090] The fusion calculation formula in step S7 is as follows:

[0091] MS refers to the image obtained by stretching the original multispectral image, and Pan refers to the original panchromatic image. SynPan is the fitted panchromatic image. PanSharp is the fused image.

[0092] Figures 2(a) to 23(c) It provides original multispectral imagery, original panchromatic imagery, and fused imagery from different satellites and at different resolutions. It can be seen that the fused imagery has consistent color with the multispectral imagery, while its sharpness (resolution) is basically consistent with the panchromatic imagery.

[0093] To verify the universality of this embodiment, imagery data from eight satellites were tested. The panchromatic imagery resolution ranged from a maximum of 0.5 meters to a minimum of 15 meters. Detailed information is shown in Table 1 below:

[0094] Table 1 Experimental Image Information

[0095]

[0096] Example 2

[0097] Embodiment 2 of the present invention provides a multispectral remote sensing image fusion system corresponding to Embodiment 1 above, comprising:

[0098] The acquisition unit is used to acquire raw multispectral images and raw panchromatic remote sensing images;

[0099] The preprocessing unit is used to preprocess the original panchromatic remote sensing image;

[0100] The fitting unit is used to fit the original multispectral image and the preprocessed panchromatic remote sensing image to obtain the fitting coefficients.

[0101] The calculation unit is used to calculate the fitted panchromatic image using the fitting coefficients and the preprocessed panchromatic remote sensing image;

[0102] An upsampling unit is used to upsample the fitted panchromatic image so that the sampling interval of the fitted panchromatic image is the same as that of the original panchromatic remote sensing image.

[0103] The fusion unit is used to fuse the original panchromatic image, the fitted panchromatic image, and the multispectral image to obtain the fusion result.

[0104] The calculation process for each of the above units is the same as in Example 1, and will not be repeated here.

[0105] Example 3

[0106] Embodiment 3 of the present invention provides a terminal device corresponding to Embodiment 1 above. The terminal device can be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., to execute the method of the above embodiments.

[0107] The terminal device in this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method in Embodiment 1 described above.

[0108] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0109] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0110] Example 4

[0111] Embodiment 4 of the present invention provides a computer-readable storage medium corresponding to Embodiment 1 above, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, they implement the steps of the method of Embodiment 1 above.

[0112] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0114] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0117] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A multispectral remote sensing image fusion method, characterized in that, Includes the following steps: S1. Acquire raw multispectral imagery and raw panchromatic remote sensing imagery; S2. Preprocess the original multispectral image and the original panchromatic remote sensing image, that is, downsample the original panchromatic remote sensing image to make the sampling interval of the original multispectral image and the original panchromatic remote sensing image the same. S3. Perform fitting calculations on the preprocessed multispectral image and the original panchromatic remote sensing image to obtain the fitting coefficients; the formula for calculating the fitting coefficients is: MS1, MS2, MS3, and MS4 are the images of the four channels of the downsampled multispectral image, respectively; i and j represent the row and column numbers of the pixels in the image, respectively. These represent the pixel values ​​of the i-th row and j-th column pixels in the four channels of the image; This represents the pixel value of the pixel in the i-th row and j-th column of the panchromatic image after downsampling the original panchromatic remote sensing image. These are the fitting coefficients for the four channels of the downsampled multispectral image; S4. Calculate the fitted panchromatic image using the fitting coefficients and the preprocessed original panchromatic remote sensing image; the pixel in the i-th row and j-th column of the fitted panchromatic image is represented as... : ; S5. Upsample the fitted panchromatic image so that the sampling interval of the fitted panchromatic image is the same as that of the original panchromatic remote sensing image. S6. Fuse the original panchromatic remote sensing image, the fitted panchromatic image, and the multispectral image to obtain the fusion result; Fusion result The calculation formula is: Where MS represents the image after sampling the original multispectral image to the same sampling interval as the original panchromatic remote sensing image; Pan represents the original panchromatic remote sensing image; and SynPan represents the fitted panchromatic image.

2. The multispectral remote sensing image fusion method according to claim 1, characterized in that, After step S1, the method further includes: performing an upsampling operation on the original multispectral image so that the size of the original multispectral image is the same as the size of the original panchromatic remote sensing image; calculating the mean and variance of the upsampled multispectral image; and stretching the upsampled multispectral image using the mean and variance. Then, the multispectral image in step S6 is the stretched multispectral image.

3. The original panchromatic remote sensing image, as described in claim 1, is characterized in that... In step S5, the fitted panchromatic image is upsampled using either bilinear interpolation or cubic convolution interpolation.

4. A multispectral remote sensing image fusion system for implementing the method described in any one of claims 1 to 3, characterized in that, include: The acquisition unit is used to acquire raw multispectral images and raw panchromatic remote sensing images; The preprocessing unit is used to preprocess the original panchromatic remote sensing image; The fitting unit is used to perform fitting calculations on the original multispectral image and the preprocessed original panchromatic remote sensing image to obtain the fitting coefficients. The calculation unit is used to calculate the fitted panchromatic image using the fitting coefficients and the preprocessed original panchromatic remote sensing image. An upsampling unit is used to upsample the fitted panchromatic image so that the sampling interval of the fitted panchromatic image is the same as that of the original panchromatic remote sensing image. The fusion unit is used to fuse the original panchromatic remote sensing image, the fitted panchromatic image, and the multispectral image to obtain the fusion result.

5. A terminal device, comprising a memory, a processor, and a computer program stored in the memory; characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program / instructions stored thereon; characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 4.

Images