Satellite vegetation index fusion method based on optimal interpolation and fast fourier transform

Abstract

The application provides a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, comprising: obtaining a meteorological vegetation index product and a high-resolution vegetation index product; performing multi-level interpolation processing on the meteorological vegetation index product based on optimal interpolation and plane equation interpolation, taking a specified vegetation index product as a background field, to obtain a vegetation index interpolation; performing fast Fourier transform processing on the high-resolution vegetation index product to eliminate a seam area contained in the high-resolution vegetation index product, to obtain a new high-resolution vegetation index product; and fusing the vegetation index interpolation and texture features corresponding to the new high-resolution vegetation index product, to obtain a vegetation index fusion result, wherein the texture features are used to describe a vegetation index change between two adjacent pixels in the new high-resolution vegetation index product. The application can significantly improve the precision of the vegetation index fusion result in a complex terrain or a region where multiple vegetation types coexist.

Description

Technical Field

[0001] This invention relates to the field of vegetation index fusion technology, and in particular to a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform. Background Technology

[0002] With the rapid advancement of Earth observation technology, remote sensing has entered a stage of development characterized by multi-platform, multi-sensor, and multi-angle observation, significantly improving the ability to acquire high spatial and hyperspectral resolution data. However, limited by the optical diffraction limit, the modulation transfer function of the imaging system, and the signal-to-noise ratio, it is difficult to achieve spaceborne remote sensing images with both high spatial and hyperspectral resolution. While panchromatic and multispectral cameras can provide images with high spatial resolution, their spectral resolution is relatively low and their bands are limited. Hyperspectral cameras, on the other hand, have extremely high spectral resolution, capable of capturing spectral cube data with nanometer-level resolution, covering visible, near-infrared, short-wave infrared, and even mid-infrared and thermal infrared bands, providing images in up to hundreds of narrow spectral bands. However, factors such as payload platform flutter, imaging blurring caused by the optical system transfer function, atmospheric radiation, and cloud cover can also lead to a decrease in the quality of radiometric information in hyperspectral images, lower spatial resolution, and the generation of mixed pixel phenomena. These problems are particularly prominent in hyperspectral image analysis, understanding, and pattern recognition.

[0003] Domestically produced Fengyun meteorological satellites (FY series) play a crucial role in providing vegetation index products (such as NDVI and EVI), especially in large-scale ecological vegetation monitoring and climate change research. However, these products also have significant drawbacks, particularly limitations in spatial and temporal resolution, making it difficult to meet the needs of refined monitoring. First, the spatial resolution of Fengyun meteorological satellite vegetation index products is relatively low, typically ranging from 250 meters to 1 kilometer. This limits their ability to monitor small-scale ecosystem processes and makes them susceptible to the mixed pixel effect, making it difficult to accurately characterize local vegetation changes in areas with high heterogeneity of surface conditions. Second, although Fengyun meteorological satellites have a high imaging frequency, the actual temporal resolution of the products is limited by factors such as cloud cover and observation delays; in areas with frequent cloud cover, spatial continuity is often affected by data sparsity. Furthermore, due to the low spectral resolution of the sensors, quantitative accuracy relies excessively on atmospheric correction and inversion algorithms, leading to errors in areas with complex terrain or multiple vegetation types. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, which can significantly improve the accuracy of vegetation index fusion results in areas with complex terrain or multiple vegetation types.

[0005] In a first aspect, the present invention provides a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, comprising:

[0006] Obtain meteorological vegetation index products and high-resolution vegetation index products;

[0007] Using a specified vegetation index product as the background field, the meteorological vegetation index product is subjected to multi-level interpolation based on optimal interpolation and plane equation interpolation to obtain vegetation index interpolation.

[0008] The high-resolution vegetation index products are processed by fast Fourier transform to remove the seam areas contained in the high-resolution vegetation index products, and new high-resolution vegetation index products are obtained.

[0009] The vegetation index interpolation is fused with the texture features corresponding to the new high-resolution vegetation index product to obtain the vegetation index fusion result. The texture features are used to describe the vegetation index changes between two adjacent pixels in the new high-resolution vegetation index product.

[0010] In one implementation, using a specified vegetation index product as the background field, the meteorological vegetation index product undergoes multi-level interpolation processing based on optimal interpolation and plane equation interpolation to obtain vegetation index interpolation, including:

[0011] For any first grid point in the first grid to be interpolated, determine the vegetation index observation values ​​corresponding to multiple observation points adjacent to the first grid point from the meteorological vegetation index product, and determine the vegetation index background value corresponding to each observation point from the specified vegetation index product. Based on the vegetation index observation value and the vegetation index background value, perform optimal interpolation processing on the first grid point to obtain the vegetation index analysis value corresponding to the first grid point.

[0012] For any second grid point in the second grid to be interpolated, determine the target grid in the first grid where the second grid point is located. Based on the vegetation index analysis value corresponding to the first grid point contained in the target grid, perform planar equation interpolation on the second grid point to obtain the vegetation index interpolation value corresponding to the second grid point.

[0013] The resolution of the second grid is higher than that of the first grid.

[0014] In one implementation, based on the observed vegetation index value and the background vegetation index value, the first grid point is subjected to optimal interpolation to obtain the vegetation index analysis value corresponding to the first grid point, including:

[0015] With the goal of minimizing the error correlation coefficient corresponding to the first grid point, a target weight coefficient is assigned to each observation point;

[0016] The vegetation index difference between the observed vegetation index value and the background vegetation index value corresponding to the same observation point is determined, and the vegetation index correction value is obtained by weighting and fusing all vegetation index differences based on the target weight coefficient.

[0017] The vegetation index background value corresponding to the first grid point is corrected using the vegetation index correction value to obtain the vegetation index analysis value corresponding to the first grid point.

[0018] In one implementation, with the goal of minimizing the error correlation coefficient corresponding to the first grid point, a target weight coefficient is assigned to each observation point, including:

[0019] Any two observation points adjacent to the first grid point are designated as the first observation point and the second observation point, respectively.

[0020] Based on the vegetation index observation values ​​corresponding to the first observation point and the vegetation index observation values ​​corresponding to the second observation point, a first error correlation coefficient is determined; and based on the vegetation index background values ​​corresponding to the first observation point and the vegetation index background values ​​corresponding to the second observation point, a second error correlation coefficient is determined.

[0021] Based on the first error correlation coefficient, the second error correlation coefficient, the ratio of the error standard deviations corresponding to the first observation point, and the ratio of the error standard deviations corresponding to the second observation point, the target error correlation coefficient between the first observation point and the second observation point is determined.

[0022] The target error correlation coefficient is weighted and fused using the current weight coefficient to obtain the grid error correlation coefficient corresponding to the first grid point. The current weight coefficient is then adjusted, and the target error correlation coefficient is weighted and fused again using the new current weight coefficient until the obtained grid error correlation coefficient is minimized. At this point, the target weight coefficient is determined.

[0023] In one implementation, based on the vegetation index analysis value corresponding to the first grid point contained in the target grid, a planar equation interpolation process is performed on the second grid point to obtain the vegetation index interpolation value corresponding to the second grid point, including:

[0024] Divide the target grid into upper and lower triangles;

[0025] Using the vegetation index analysis value corresponding to the first grid point contained in the upper triangle, the triangular plane coefficient and intercept of the upper triangle are fitted; and using the vegetation index analysis value corresponding to the first grid point contained in the lower triangle, the triangular plane coefficient and intercept of the lower triangle are fitted.

[0026] Determine whether the second grid point is inside the upper triangle or the lower triangle, and then determine the vegetation index interpolation corresponding to the second grid point based on the trigonometric plane coefficient and intercept of the target triangle in which the second grid point is located.

[0027] In one implementation, the high-resolution vegetation index product is subjected to a Fast Fourier Transform (FFT) to remove seam regions from the product, resulting in a new high-resolution vegetation index product. This includes:

[0028] Using Fast Fourier Transform, the high-resolution vegetation index product is transformed from the spatial domain to the frequency domain to obtain the vegetation index spectrum.

[0029] The low-frequency components in the vegetation index spectrum are moved from the edge of the vegetation index spectrum to the center of the vegetation index spectrum to obtain a new vegetation index spectrum.

[0030] Low-frequency components are filtered out from the new vegetation index spectrum using a filter to eliminate seam areas contained in high-scoring vegetation index products.

[0031] By using inverse frequency domain transformation, the vegetation index spectrum diagram after filtering out low-frequency components is transformed from the frequency domain to the spatial domain to obtain a new high-resolution vegetation index product.

[0032] In one implementation, the vegetation index interpolation is fused with the texture features corresponding to the new high-scoring vegetation index product to obtain a vegetation index fusion result, including:

[0033] The texture features corresponding to the new high-scoring vegetation index products are normalized.

[0034] The vegetation index interpolation is adjusted using normalized texture features so that the changes between the adjusted vegetation index interpolation values ​​of two adjacent pixels satisfy the vegetation index changes described by the texture features, thus obtaining the vegetation index fusion result.

[0035] Secondly, the present invention also provides a satellite vegetation index fusion device based on optimal interpolation and fast Fourier transform, comprising:

[0036] The product acquisition module is used to acquire meteorological vegetation index products and high-resolution vegetation index products.

[0037] The interpolation module is used to perform multi-level interpolation processing on meteorological vegetation index products based on optimal interpolation and plane equation interpolation, using a specified vegetation index product as the background field, to obtain vegetation index interpolation;

[0038] The Fast Fourier Transform module is used to perform Fast Fourier Transform processing on high-resolution vegetation index products to remove the seam areas contained in the high-resolution vegetation index products and obtain new high-resolution vegetation index products.

[0039] The fusion module is used to fuse the vegetation index interpolation with the texture features corresponding to the new high-resolution vegetation index product to obtain the vegetation index fusion result. The texture features are used to describe the vegetation index changes between two adjacent pixels in the new high-resolution vegetation index product.

[0040] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0041] Fourthly, the present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0042] The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform provided by this invention first obtains meteorological vegetation index products and high-resolution vegetation index products; then, using a specified vegetation index product as the background field, the meteorological vegetation index product is subjected to multi-level interpolation processing based on optimal interpolation and plane equation interpolation to obtain vegetation index interpolation; next, the high-resolution vegetation index product is subjected to fast Fourier transform processing to remove the seam regions contained in the high-resolution vegetation index product to obtain a new high-resolution vegetation index product; finally, the vegetation index interpolation is fused with the texture features corresponding to the new high-resolution vegetation index product to obtain the vegetation index fusion result, where the texture features are used to describe the vegetation index variation between two adjacent pixels in the new high-resolution vegetation index product. The aforementioned method, utilizing planar equation interpolation, not only maximizes the preservation of meteorological vegetation index products during fusion but also addresses the issue of uneven vegetation index fusion results. Furthermore, optimal interpolation effectively compensates for information loss due to observation angles and cloud cover during fusion, improving the spatial consistency and temporal continuity of vegetation index fusion results. This invention, by combining optimal interpolation with planar equation interpolation, not only maintains the detail and accuracy of meteorological vegetation index products but also enhances the spatial and temporal quality and application reliability of vegetation index fusion results. Additionally, the application of Fast Fourier Transform (FFT) is based on extracting texture from high-resolution satellite climate (monthly, weekly, and seasonal) mosaic images. Since high-resolution vegetation index products have significant systematic errors and daily variations in their climate composite data, FFT frequency domain transformation is used to extract texture features from these products, further improving the accuracy of vegetation index fusion results in complex terrains or areas with multiple vegetation types.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform provided for an embodiment of the present invention;

[0047] Figure 2 A technical framework diagram of a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform provided for embodiments of the present invention;

[0048] Figure 3 This is a schematic diagram of the preprocessing of a meteorological vegetation index product provided in an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of a satellite vegetation index fusion device based on optimal interpolation and fast Fourier transform provided for an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0052] Currently, vegetation indices provided by domestically produced Fengyun meteorological satellites (FY series) are prone to errors in areas with complex terrain or where multiple vegetation types coexist. To address these shortcomings, data fusion with high-resolution satellites has emerged as an effective solution.

[0053] High-resolution satellites (such as the Gaofen series and Sentinel-2) possess higher spatial resolution and spectral sensitivity, significantly improving the accuracy and applicability of vegetation index products. By fusing the refined spatial information from high-resolution satellites, the spatial resolution of meteorological satellite vegetation index products can be effectively enhanced, expanding their applicability to small-scale vegetation dynamic monitoring. Furthermore, utilizing the data interpolation and completion capabilities of Gaofen satellites can mitigate the spatiotemporal discontinuity issues caused by cloud cover or observation intervals in domestic Fengyun meteorological satellites. In addition, the hyperspectral data from Gaofen satellites can correct for the mixed pixel effect and inversion errors of Fengyun meteorological satellites, thereby improving the quantitative accuracy of vegetation indices and achieving an organic unity between large-scale background monitoring and localized refined analysis. The synergistic application of Gaofen satellites and meteorological satellites not only compensates for the deficiencies of single satellite products but also demonstrates significant advancements and application potential in fields such as ecological monitoring, precision agriculture, and disaster response.

[0054] Based on this, the present invention provides a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, which can significantly improve the accuracy of vegetation index fusion results in areas with complex terrain or multiple vegetation types.

[0055] To facilitate understanding of this embodiment, a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, as disclosed in this embodiment, will first be described in detail. (See [link to relevant documentation]). Figure 1 The diagram shows a flowchart of a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform. This method mainly includes the following steps S102 to S108:

[0056] Step S102: Obtain meteorological vegetation index products and high-resolution vegetation index products.

[0057] Among them, the meteorological vegetation index product includes the vegetation index observation values ​​corresponding to multiple observation points in the study area, and the high-resolution vegetation index product also includes the vegetation index observation values ​​corresponding to multiple observation points in the study area. Compared with the meteorological vegetation index product, the high-resolution vegetation index product has higher spatial resolution and spectral sensitivity.

[0058] Step S104: Using the specified vegetation index product as the background field, perform multi-level interpolation processing on the meteorological vegetation index product based on optimal interpolation and plane equation interpolation to obtain the vegetation index interpolation.

[0059] For example, vegetation products from FY4 or FY3D can be used as the background field. In one instance, the meteorological vegetation index product is first processed using optimal interpolation with FY4 or FY3D vegetation products as the background field to obtain the vegetation index analysis value corresponding to each grid point in the first grid; then, planar equation interpolation is performed on the vegetation index analysis value corresponding to each grid point in the first grid to obtain the vegetation index interpolation value corresponding to each grid point in the second grid, where the resolution of the second grid is higher than that of the first grid.

[0060] Step S106: Perform fast Fourier transform on the high-resolution vegetation index product to remove the seam areas contained in the high-resolution vegetation index product and obtain a new high-resolution vegetation index product.

[0061] In one example, a vegetation index spectrum can be obtained by performing a fast Fourier transform on the high-resolution vegetation index product. After centering and filtering the low-frequency components of the vegetation index spectrum, the seam regions contained in the high-resolution vegetation index product can be removed. Then, by performing an inverse frequency domain transformation on the vegetation index spectrum, a new high-resolution vegetation index product without seam regions can be obtained.

[0062] Step S108: The vegetation index interpolation is fused with the texture features corresponding to the new high-scoring vegetation index product to obtain the vegetation index fusion result.

[0063] Texture features are used to describe the vegetation index variation between two adjacent pixels in a new high-resolution vegetation index product. In one example, the texture features of the new high-resolution vegetation product index can be normalized and then fused with the vegetation index interpolation to ensure that the vegetation index variation in the fused vegetation index matches the vegetation index variation described by the texture features.

[0064] The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform provided in this invention not only maximizes the preservation of meteorological vegetation index products during fusion by using plane equation interpolation, but also solves the problem of uneven vegetation index fusion results. Optimal interpolation effectively compensates for information loss caused by observation angle and cloud cover during fusion, improving the spatial consistency and temporal continuity of vegetation index fusion results. It predicts and fills in unobserved areas from adjacent data through weight calculation, ensuring the smoothness and continuity of the fusion results. By combining optimal interpolation and plane equation interpolation, this invention not only maintains the detail and accuracy of meteorological vegetation index products, but also improves the spatial and temporal quality and application reliability of vegetation index fusion results. In addition, the application of fast Fourier transform is based on extracting texture from high-resolution satellite climate (monthly, weekly, pentad, etc.) mosaic images. Since the climate synthesis data of high-resolution vegetation index products has obvious systematic errors and daily variations, the texture features of high-resolution vegetation index products are extracted by fast Fourier frequency domain transformation, further improving the accuracy of vegetation index fusion results in areas with complex terrain or multiple vegetation types.

[0065] To facilitate understanding, this invention provides an implementation method for satellite vegetation index fusion based on optimal interpolation and fast Fourier transform. This invention proposes a method that combines meteorological vegetation index products with high-resolution vegetation index products from the same region, primarily utilizing methods such as plane equation difference, optimal interpolation, and fast Fourier transform to achieve the fusion of multi-source meteorological vegetation index products and high-resolution vegetation index products. See also... Figure 2 The diagram illustrates a technical framework for a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform. The method includes: reading the data required for vegetation index fusion, namely meteorological vegetation index products and high-resolution vegetation index products; performing optimal interpolation fusion on the meteorological vegetation index products; performing plane equation interpolation on the fused vegetation index analysis values ​​to prepare for subsequent fusion with high-resolution vegetation index products; performing Fourier transform on the high-resolution vegetation index products to obtain texture features; achieving fusion of meteorological vegetation index products and high-resolution vegetation index products through data normalization, texture overlay, etc.; and outputting vegetation index fusion-related products and information.

[0066] The specific implementation process is as follows:

[0067] Regarding the aforementioned step S104, this embodiment of the invention provides an implementation method that uses a specified vegetation index product as the background field, performs multi-level interpolation processing on the meteorological vegetation index product based on optimal interpolation and plane equation interpolation to obtain the vegetation index interpolation, as shown in steps 1 to 2 below:

[0068] Step 1: For any first grid point in the first grid to be interpolated, determine the vegetation index observation values ​​corresponding to multiple observation points adjacent to the first grid point from the meteorological vegetation index product, and determine the vegetation index background value corresponding to each observation point from the specified vegetation index product. Based on the vegetation index observation values ​​and the vegetation index background values, perform optimal interpolation processing on the first grid point to obtain the vegetation index analysis value corresponding to the first grid point. See steps 1.1 to 1.3 below for details:

[0069] Step 1.1: Assign a target weight coefficient to each observation point, with the goal of minimizing the error correlation coefficient corresponding to the first grid point. Wherein, the target weight coefficient W... i This is to minimize the error in the vegetation index analysis values ​​corresponding to the grid points. Target weight coefficient W i The determination process is as follows:

[0070] (I) Let any two observation points adjacent to the first grid point be the first observation point i and the second observation point j respectively.

[0071] (II) Based on the observed vegetation index values ​​corresponding to the first observation point i and the second observation point j, determine the first error correlation coefficient; and based on the background vegetation index values ​​corresponding to the first observation point i and the second observation point j, determine the second error correlation coefficient. Wherein, the first error correlation coefficient... That is, the error correlation coefficient between the vegetation index observations corresponding to the first observation point i and the second observation point j, and the second error correlation coefficient. That is, the correlation coefficient error between the background values ​​of vegetation index corresponding to the first observation point i and the second observation point j.

[0072] This invention provides a method for determining the second error correlation coefficient. The second error correlation coefficient is determined according to the following formula:

[0073]

[0074] Where, r Z and r m L represents the longitude and latitudinal distances between the first observation point i and the second observation point j, respectively. Z and L m These represent the radii of influence in the two directions, respectively. Similarly, the first error correlation coefficient can be determined.

[0075] (III) Based on the first error correlation coefficient, the second error correlation coefficient, the ratio of the error standard deviations corresponding to the first observation point i, and the ratio of the error standard deviations corresponding to the second observation point j, determine the target error correlation coefficient between the first observation point i and the second observation point j.

[0076] The ratio of standard deviations of error can be expressed as: Where, σ O and σ B λ represents the standard deviation of the error at the observation point and the standard deviation of the error at the background field, respectively. The value of λ is 1, which means that it is assumed that the standard deviation of the error at the observation point is consistent with the standard deviation of the error at the background field.

[0077] Based on this, the expression for the target error correlation coefficient between the first observation point i and the second observation point j is as follows:

[0078] (IV) Use the current weight coefficient to perform weighted fusion of the target error correlation coefficient to obtain the grid error correlation coefficient corresponding to the first grid point. Adjust the current weight coefficient and continue to use the new current weight coefficient to perform weighted fusion of the target error correlation coefficient until the obtained grid error correlation coefficient is the smallest, and then determine the target weight coefficient.

[0079] Wherein, the grid error correlation coefficient corresponding to the first grid point g The calculation formula is as follows:

[0080]

[0081] Where N is the number of observation points (specifically, valid observation points) adjacent to the first grid point g. The error is the correlation coefficient between the background vegetation index values ​​corresponding to the first observation point i and the second observation point j. Let λ be the error correlation coefficient between the vegetation index observations corresponding to the first observation point i and the second observation point j. i λ is the ratio of the standard deviations of the errors corresponding to the first observation point i. j W is the ratio of the standard deviations of the errors corresponding to the second observation point j. i The target weight coefficient, is the grid error correlation coefficient corresponding to the first grid point g.

[0082] Step 1.2: Determine the vegetation index difference between the observed vegetation index value and the background vegetation index value for the same observation point, and then perform a weighted fusion of all vegetation index differences based on the target weight coefficient to obtain the vegetation index correction value. The expression for the vegetation index correction value is as follows: Among them, O i Let B be the vegetation index observation value at the first observation point i. iThe vegetation index background value is the first observation point i.

[0083] Step 1.3: Use the vegetation index correction value to correct the background value of the vegetation index corresponding to the first grid point, and obtain the vegetation index analysis value corresponding to the first grid point.

[0084] Considering the non-continuous nature of vegetation cover data, this embodiment of the invention employs an optimal interpolation algorithm for fusion. Here, FY4 or FY3D vegetation products are used as the background field. The vegetation index analysis value participating in the analysis is ultimately calculated by adding the vegetation index background value at the first grid point to the vegetation index correction value at that first grid point. The correction value is obtained by weighting the deviations between the vegetation index observation values ​​and the vegetation index background values ​​of all valid observation points surrounding the first grid point. The vegetation index analysis value A corresponding to the first grid point g is... g The calculation formula is as follows:

[0085]

[0086] Among them, A g B represents the vegetation index analysis value corresponding to the first grid point g. g Here, N represents the background vegetation index value corresponding to the first grid point g, and N is the number of neighboring observation points (specifically, valid observation points) of the first grid point g. i Let B be the vegetation index observation value at the first observation point i. i W represents the background vegetation index value at the first observation point i. i The target weight coefficient W is used when there is no data in the observation area. i The value is 0. In this embodiment of the invention, the background field is an initial estimate of the vegetation index observation value, and the background field is corrected by the vegetation index observation value of the sensor and the weighting function.

[0087] Step 2: For any second grid point in the second grid to be interpolated, determine the target grid in the first grid where the second grid point is located. Based on the vegetation index analysis value corresponding to the first grid point contained in the target grid, perform planar equation interpolation on the second grid point to obtain the vegetation index interpolation value corresponding to the second grid point.

[0088] Meteorological vegetation index products have higher accuracy than high-resolution vegetation index products, but their spatial resolution is relatively lower. Therefore, before fusing the two datasets, the meteorological vegetation index products need to be interpolated or downscaled. See also Figure 3The diagram shows a preprocessing schematic for a meteorological vegetation index product. The intersections of the black solid lines represent satellite observation points, and the white solid lines represent the preprocessed data points (e.g., points (px, py)). To ensure good continuity of the preprocessed data, various methods were tested and compared (e.g., bilinear interpolation, inverse distance weighting, etc.). The conclusion is that using the plane equation interpolation method not only preserves the original data values ​​to the greatest extent but also yields the smoothest interpolation results. See steps 2.1 to 2.4 below for details.

[0089] Step 2.1: Determine the target grid in which the second grid point is located within the first grid. For an example, please refer to [link to example]. Figure 3 For the second grid point p(px, py), the four vertices of the target grid (i.e. the first grid point) are p1(x1, y1, z1), p2(x2, y2, z2), p3(x3, y3, z3), and p4(x4, y4, z4), where x and y are the planar coordinates of the grid point, and z is the vegetation index analysis value of the grid point.

[0090] Step 2.2: Divide the target grid into upper and lower triangles. For example, the triangle formed by p2, p3, and p4 is designated as the lower triangle, and the triangle formed by p1, p2, and p3 is designated as the upper triangle.

[0091] Step 2.3: Using the vegetation index analysis value corresponding to the first grid point contained in the upper triangle, fit the triangular plane coefficient and intercept of the upper triangle; and using the vegetation index analysis value corresponding to the first grid point contained in the lower triangle, fit the triangular plane coefficient and intercept of the lower triangle.

[0092] Given three points p1(x1,y1,z1), p2(x2,y2,z2), and p3(x3,y3,z3), to determine the equation of the plane, the key is to find a normal vector of the plane. For this, construct vectors p1p2(x2-x1,y2-y1,z2-z1) and p1p3(x3-x1,y3-y1,z3-z1). The plane normal is perpendicular to these two vectors, therefore the normal vector n is:

[0093]

[0094] Where i, j, and k represent p1, p2, and p3, and a, b, and c represent trigonometric coefficients.

[0095] Based on the above plane equation, the trigonometric coefficients and intercepts of the upper and lower triangles can be obtained as follows:

[0096] Upper triangle:

[0097] a1=y1*z2-y1*z3-y2*z1+y2*z3+y3*z1-y3*z2;

[0098] b1=-x1*z2+x1*z3+x2*z1-x2*z3-x3*z1+x3*z2;

[0099] c1=x1*y2-x1*y3-x2*y1+x2*y3+x3*y1-x3*y2;

[0100] d1=-x1*y2*z3+x1*y3*z2+x2*y1*z3-x2*y3*z1-x3*y1*z2+x3*y2*z1;

[0101] Lower triangle:

[0102] a2=y4*z2-y4*z3-y2*z4+y2*z3+y3*z4-y3*z2;

[0103] b2=-x4*z2+x4*z3+x2*z4-x2*z3-x3*z4+x3*z2;

[0104] c2=x4*y2-x4*y3-x2*y4+x2*y3+x3*y4-x3*y2;

[0105] d2=-x4*y2*z3+x4*y3*z2+x2*y4*z3-x2*y3*z4-x3*y4*z2+x3*y2*z4.

[0106] Step 2.4: Determine whether the second grid point is inside the upper triangle or the lower triangle, and determine the vegetation index interpolation corresponding to the second grid point based on the trigonometric plane coefficient and intercept of the target triangle in which the second grid point is located.

[0107] In one example, we can calculate the intersection of the y-vector containing (px, py) with the lines connecting (x3, y3) and (x2, y2):

[0108] y32=y3-(x3-px) / (x3-x2)*(y3-y2);

[0109] If py is less than y32, then the second grid point is determined to be inside the upper triangle. In this case, the vegetation index interpolation zo can be calculated using the trigonometric plane coefficients and intercepts of the upper triangle.

[0110] zo = (-d1 - a1*px - b1*py) / c1

[0111] Otherwise, if the second grid point is determined to be inside the lower triangle, the vegetation index interpolation zo can be calculated using the trigonometric plane coefficients and intercepts of the lower triangle.

[0112] zo = (-d2-a2*px-b2*py) / c2.

[0113] Regarding the aforementioned step S106, this embodiment of the invention provides a specific implementation method for performing fast Fourier transform processing on high-resolution vegetation index products to remove the seam areas contained in the high-resolution vegetation index products and obtain new high-resolution vegetation index products.

[0114] This fusion algorithm extracts texture from high-resolution satellite climate (monthly, weekly, and seasonal) mosaic images. Since the climate composite data of high-resolution vegetation index products has noticeable seams, these seams contain instrumental systematic errors and daily variations, which are low-frequency changes. Therefore, the algorithm proposes to extract texture features within a specific frequency range from high-resolution vegetation index products through frequency domain transformation, spectrogram centering, filter design and application, and inverse frequency domain transformation.

[0115] Specifically, it includes the following steps a through d:

[0116] Step a: Using Fast Fourier Transform (FFT), the high-resolution vegetation index product is transformed from the spatial domain to the frequency domain, resulting in a vegetation index spectrum. The FFT transforms the image from the spatial domain to the frequency domain, decomposing the image into a superposition of sine and cosine waves of different frequencies. The low-frequency components in the vegetation index spectrum represent the overall structure of the image, such as smooth regions, uniform backgrounds, and large-scale brightness variations, while the high-frequency components represent the image's details, edges, and textures.

[0117] Step b: Move the low-frequency components in the vegetation index spectrum from the edge of the vegetation index spectrum to the center of the vegetation index spectrum to obtain a new vegetation index spectrum.

[0118] This process is known as spectrogram centering. In the vegetation index spectrogram calculated by FFT, low-frequency components are located in the four corners of the image by default, making intuitive analysis of the vegetation index spectrogram less convenient. To facilitate observation and processing, the low-frequency components are first moved to the center of the vegetation index spectrogram. The central part of the vegetation index spectrogram contains low-frequency information, while the edge parts contain high-frequency information. By centering the vegetation index spectrogram, frequency filtering becomes easier, especially since image texture extraction focuses on both low-frequency (overall structure) and high-frequency (detail texture) components. Therefore, centering the vegetation index spectrogram facilitates more intuitive frequency domain processing.

[0119] Step c involves filtering out low-frequency components from the new vegetation index spectrum using a filter to eliminate seam areas contained in high-scoring vegetation index products.

[0120] First, filters are designed, including frequency domain filtering, which enhances or suppresses specific frequency components by manipulating the spectrum, thereby achieving effects such as texture extraction, noise reduction, and smoothing. Common frequency domain filters include low-pass filters, high-pass filters, and band-pass filters. Low-pass filters are used to remove high-frequency noise while preserving the overall structure of the image, and are often used for image smoothing or removing details; high-pass filters remove low-frequency components, preserve image details and textures, remove smoothed backgrounds, and retain only edge and texture information; band-pass filters are used to extract textures within a specific frequency range, and are particularly suitable for extracting texture features that exist within a specific spatial frequency range.

[0121] Then, filters are applied, including selectively removing specific low-frequency components in the frequency domain by designing filters and applying them to the vegetation index spectrogram. Filters are typically implemented using masks that cover specific regions in the vegetation index spectrogram corresponding to the frequency range of interest. High-pass filtering masks low-frequency regions (located in the center) while preserving high-frequency regions (spectral edges), highlighting image details and edges.

[0122] Step d: Using inverse frequency domain transformation, the vegetation index spectrum diagram after filtering out low-frequency components is transformed from the frequency domain to the spatial domain to obtain a new high-resolution vegetation index product.

[0123] After processing the image in the frequency domain, it is usually necessary to convert the frequency domain results back to the spatial domain using the inverse fast Fourier transform. Through the inverse transform, the processed vegetation index spectrum is converted into a spatial domain image, which will reflect the effect of the frequency domain processing. The inverse transform process reverses the conversion of frequency domain data back to the spatial domain, recovering the specific content of the image. At this point, the high-frequency components in the frequency domain are transformed into the detailed parts of the image.

[0124] Regarding the aforementioned step S108, this embodiment of the invention provides a specific implementation method for fusing vegetation index interpolation with the texture features corresponding to the new high-resolution vegetation index product to obtain a vegetation index fusion result. The texture features corresponding to the new high-resolution vegetation index product are normalized, and the vegetation index interpolation is adjusted using the normalized texture features so that the change between the adjusted vegetation index interpolation of two adjacent pixels satisfies the vegetation index change described by the texture features, thereby obtaining a vegetation index fusion result.

[0125] In practical applications, to ensure the comparability and consistency of texture information during the fusion process, the extracted texture features are normalized to eliminate dimensional differences and scale effects between data. After normalization, the processed texture features are overlaid and fused with vegetation index interpolation to generate a vegetation index fusion result that complements the texture and product, thereby enhancing the expressive power and analytical value of remote sensing data.

[0126] In summary, the satellite vegetation index multi-source fusion algorithm proposed in this invention is innovatively based on domestic Fengyun series meteorological satellite products. It effectively compensates for insufficient spatial distribution and systematic errors in the data by employing an optimal interpolation method. The optimal interpolation algorithm fills in unobserved areas caused by factors such as observation angle and cloud cover through weight calculation, improving the spatial consistency and temporal continuity of the fused data. This invention also innovatively applies Fast Fourier Transform (FFT) technology to extract texture features from high-resolution satellite climate data (such as monthly, weekly, and pentad scales). Through frequency domain transformation, filter design, and inverse frequency domain transformation, it successfully extracts and corrects systematic errors and daily variations in the high-resolution data. Furthermore, by combining plane equation interpolation and normalization processing with the fusion of meteorological satellite products and high-resolution texture features, it improves the spatial resolution and temporal continuity of vegetation index products, expanding the application effectiveness and reliability of domestic remote sensing data at high resolution and multiple time phases.

[0127] Based on the foregoing embodiments, this invention provides a satellite vegetation index fusion device based on optimal interpolation and fast Fourier transform. (See also...) Figure 4 The diagram shows a structural schematic of a satellite vegetation index fusion device based on optimal interpolation and fast Fourier transform. The device mainly includes the following parts:

[0128] Product acquisition module 402 is used to acquire meteorological vegetation index products and high-resolution vegetation index products.

[0129] Interpolation module 404 is used to perform multi-level interpolation processing on meteorological vegetation index products based on optimal interpolation and plane equation interpolation, using a specified vegetation index product as the background field, to obtain vegetation index interpolation.

[0130] The Fast Fourier Transform module 406 is used to perform Fast Fourier Transform processing on the high-resolution vegetation index product to remove the seam areas contained in the high-resolution vegetation index product and obtain a new high-resolution vegetation index product.

[0131] The fusion module 408 is used to fuse the vegetation index interpolation with the texture features corresponding to the new high-resolution vegetation index product to obtain the vegetation index fusion result. The texture features are used to describe the vegetation index changes between two adjacent pixels in the new high-resolution vegetation index product.

[0132] The satellite vegetation index fusion device based on optimal interpolation and fast Fourier transform provided in this invention not only maximizes the preservation of meteorological vegetation index products during fusion by using plane equation interpolation, but also solves the problem of uneven vegetation index fusion results. Optimal interpolation effectively compensates for information loss caused by observation angle and cloud cover during fusion, improving the spatial consistency and temporal continuity of vegetation index fusion results. It predicts and fills in unobserved areas from adjacent data through weight calculation, ensuring the smoothness and continuity of the fusion results. By combining optimal interpolation and plane equation interpolation, this invention not only maintains the detail and accuracy of meteorological vegetation index products, but also improves the spatial and temporal quality and application reliability of vegetation index fusion results. In addition, the application of fast Fourier transform is based on extracting texture from high-resolution satellite climate (monthly, weekly, pentad, etc.) mosaic images. Since the climate synthesis data of high-resolution vegetation index products has obvious systematic errors and daily variations, the texture features of high-resolution vegetation index products are extracted by fast Fourier frequency domain transformation, further improving the accuracy of vegetation index fusion results in areas with complex terrain or multiple vegetation types.

[0133] In one implementation, the interpolation module 404 is specifically used for:

[0134] For any first grid point in the first grid to be interpolated, determine the vegetation index observation values ​​corresponding to multiple observation points adjacent to the first grid point from the meteorological vegetation index product, and determine the vegetation index background value corresponding to each observation point from the specified vegetation index product. Based on the vegetation index observation value and the vegetation index background value, perform optimal interpolation processing on the first grid point to obtain the vegetation index analysis value corresponding to the first grid point.

[0135] For any second grid point in the second grid to be interpolated, determine the target grid in the first grid where the second grid point is located. Based on the vegetation index analysis value corresponding to the first grid point contained in the target grid, perform planar equation interpolation on the second grid point to obtain the vegetation index interpolation value corresponding to the second grid point.

[0136] The resolution of the second grid is higher than that of the first grid.

[0137] In one implementation, the interpolation module 404 is specifically used for:

[0138] With the goal of minimizing the error correlation coefficient corresponding to the first grid point, a target weight coefficient is assigned to each observation point;

[0139] The vegetation index difference between the observed vegetation index value and the background vegetation index value corresponding to the same observation point is determined, and the vegetation index correction value is obtained by weighting and fusing all vegetation index differences based on the target weight coefficient.

[0140] The vegetation index background value corresponding to the first grid point is corrected using the vegetation index correction value to obtain the vegetation index analysis value corresponding to the first grid point.

[0141] In one implementation, the interpolation module 404 is specifically used for:

[0142] Any two observation points adjacent to the first grid point are designated as the first observation point and the second observation point, respectively.

[0143] Based on the vegetation index observation values ​​corresponding to the first observation point and the vegetation index observation values ​​corresponding to the second observation point, a first error correlation coefficient is determined; and based on the vegetation index background values ​​corresponding to the first observation point and the vegetation index background values ​​corresponding to the second observation point, a second error correlation coefficient is determined.

[0144] Based on the first error correlation coefficient, the second error correlation coefficient, the ratio of the error standard deviations corresponding to the first observation point, and the ratio of the error standard deviations corresponding to the second observation point, the target error correlation coefficient between the first observation point and the second observation point is determined.

[0145] The target error correlation coefficient is weighted and fused using the current weight coefficient to obtain the grid error correlation coefficient corresponding to the first grid point. The current weight coefficient is then adjusted, and the target error correlation coefficient is weighted and fused again using the new current weight coefficient until the obtained grid error correlation coefficient is minimized. At this point, the target weight coefficient is determined.

[0146] In one implementation, the interpolation module 404 is specifically used for:

[0147] Divide the target grid into upper and lower triangles;

[0148] Using the vegetation index analysis value corresponding to the first grid point contained in the upper triangle, the triangular plane coefficient and intercept of the upper triangle are fitted; and using the vegetation index analysis value corresponding to the first grid point contained in the lower triangle, the triangular plane coefficient and intercept of the lower triangle are fitted.

[0149] Determine whether the second grid point is inside the upper triangle or the lower triangle, and then determine the vegetation index interpolation corresponding to the second grid point based on the trigonometric plane coefficient and intercept of the target triangle in which the second grid point is located.

[0150] In one implementation, the Fast Fourier Transform module 406 is specifically used for:

[0151] Using Fast Fourier Transform, the high-resolution vegetation index product is transformed from the spatial domain to the frequency domain to obtain the vegetation index spectrum.

[0152] The low-frequency components in the vegetation index spectrum are moved from the edge of the vegetation index spectrum to the center of the vegetation index spectrum to obtain a new vegetation index spectrum.

[0153] Low-frequency components are filtered out from the new vegetation index spectrum using a filter to eliminate seam areas contained in high-scoring vegetation index products.

[0154] By using inverse frequency domain transformation, the vegetation index spectrum diagram after filtering out low-frequency components is transformed from the frequency domain to the spatial domain to obtain a new high-resolution vegetation index product.

[0155] In one implementation, the fusion module 408 is specifically used for:

[0156] The texture features corresponding to the new high-scoring vegetation index products are normalized.

[0157] The vegetation index interpolation is adjusted using normalized texture features so that the changes between the adjusted vegetation index interpolation values ​​of two adjacent pixels satisfy the vegetation index changes described by the texture features, thus obtaining the vegetation index fusion result.

[0158] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0159] This invention provides an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0160] Figure 5 The present invention provides a schematic diagram of the structure of an electronic device 100, which includes a processor 50, a memory 51, a bus 52 and a communication interface 53. The processor 50, the communication interface 53 and the memory 51 are connected through the bus 52. The processor 50 is used to execute executable modules, such as computer programs, stored in the memory 51.

[0161] The memory 51 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 53 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0162] Bus 52 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0163] The memory 51 is used to store programs. After receiving an execution instruction, the processor 50 executes the programs. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0164] Processor 50 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 50 or by instructions in software form. Processor 50 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 51. The processor 50 reads the information in memory 51 and, in conjunction with its hardware, completes the steps of the above method.

[0165] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0166] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, characterized in that, include: Obtain meteorological vegetation index products and high-resolution vegetation index products; Using a specified vegetation index product as the background field, the meteorological vegetation index product is subjected to multi-level interpolation processing based on optimal interpolation and plane equation interpolation to obtain vegetation index interpolation. This includes: for any first grid point in the first grid to be interpolated, determining the vegetation index observation values ​​corresponding to multiple observation points adjacent to the first grid point from the meteorological vegetation index product, and determining the vegetation index background value corresponding to each observation point from the specified vegetation index product; based on the vegetation index observation values ​​and the vegetation index background values, performing optimal interpolation processing on the first grid point to obtain the vegetation index analysis value corresponding to the first grid point; for any second grid point in the second grid to be interpolated, determining the target grid in which the second grid point is located within the first grid; based on the vegetation index analysis value corresponding to the first grid point contained in the target grid, performing plane equation interpolation processing on the second grid point to obtain the vegetation index interpolation value corresponding to the second grid point; wherein the resolution of the second grid is higher than the resolution of the first grid. The high-resolution vegetation index product is subjected to fast Fourier transform processing to remove the seam areas contained in the high-resolution vegetation index product, and a new high-resolution vegetation index product is obtained. The vegetation index interpolation is fused with the texture features corresponding to the new high-resolution vegetation index product to obtain the vegetation index fusion result. The texture features are used to describe the vegetation index changes between two adjacent pixels in the new high-resolution vegetation index product.

2. The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform according to claim 1, characterized in that, Based on the observed vegetation index values ​​and the background vegetation index values, optimal interpolation is performed on the first grid point to obtain the vegetation index analysis value corresponding to the first grid point, including: With the goal of minimizing the error correlation coefficient corresponding to the first grid point, a target weight coefficient is assigned to each observation point; The vegetation index difference between the observed value of the vegetation index and the background value of the vegetation index corresponding to the same observation point is determined, and the vegetation index correction value is obtained by weighting and fusing all the vegetation index differences based on the target weight coefficient. Using the vegetation index correction value, the background value of the vegetation index corresponding to the first grid point is corrected to obtain the vegetation index analysis value corresponding to the first grid point.

3. The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform according to claim 2, characterized in that, With the goal of minimizing the error correlation coefficient corresponding to the first grid point, a target weight coefficient is assigned to each observation point, including: Any two observation points adjacent to the first grid point are designated as the first observation point and the second observation point, respectively. Based on the vegetation index observation values ​​corresponding to the first observation point and the vegetation index observation values ​​corresponding to the second observation point, a first error correlation coefficient is determined; and based on the vegetation index background values ​​corresponding to the first observation point and the vegetation index background values ​​corresponding to the second observation point, a second error correlation coefficient is determined. Based on the first error correlation coefficient, the second error correlation coefficient, the ratio of the error standard deviations corresponding to the first observation point, and the ratio of the error standard deviations corresponding to the second observation point, the target error correlation coefficient between the first observation point and the second observation point is determined. The target error correlation coefficient is weighted and fused using the current weight coefficient to obtain the grid error correlation coefficient corresponding to the first grid point. The current weight coefficient is then adjusted, and the target error correlation coefficient is weighted and fused again using the new current weight coefficient until the obtained grid error correlation coefficient is minimized, at which point the target weight coefficient is determined.

4. The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform according to claim 1, characterized in that, Based on the vegetation index analysis value corresponding to the first grid point contained in the target grid, a planar equation interpolation process is performed on the second grid point to obtain the vegetation index interpolation value corresponding to the second grid point, including: The target grid is divided into an upper triangle and a lower triangle; Using the vegetation index analysis values ​​corresponding to the first grid points contained in the upper triangle, the triangular plane coefficients and intercepts corresponding to the upper triangle are fitted; and using the vegetation index analysis values ​​corresponding to the first grid points contained in the lower triangle, the triangular plane coefficients and intercepts corresponding to the lower triangle are fitted. Determine whether the second grid point is inside the upper triangle or outside the lower triangle, and determine the vegetation index interpolation corresponding to the second grid point based on the triangular plane coefficient and the intercept of the target triangle in which the second grid point is located.

5. The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform according to claim 1, characterized in that, Perform a Fast Fourier Transform (FFT) on the high-resolution vegetation index product to remove the seam regions contained in the high-resolution vegetation index product, resulting in a new high-resolution vegetation index product, including: Using Fast Fourier Transform, the high-resolution vegetation index product is transformed from the spatial domain to the frequency domain to obtain a vegetation index spectrum. The low-frequency components in the vegetation index spectrum are moved from the edge of the vegetation index spectrum to the center of the vegetation index spectrum to obtain a new vegetation index spectrum. The low-frequency components are filtered out from the new vegetation index spectrum using a filter to eliminate the seam areas contained in the high-scoring vegetation index product. By using inverse frequency domain transformation, the vegetation index spectrum diagram after filtering out the low-frequency components is transformed from the frequency domain to the spatial domain to obtain a new high-resolution vegetation index product.

6. The satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform according to claim 1, characterized in that, The vegetation index interpolation is fused with the texture features corresponding to the new high-scoring vegetation index product to obtain the vegetation index fusion result, including: The texture features corresponding to the new high-scoring vegetation index product are normalized. The vegetation index interpolation is adjusted using the normalized texture features so that the variation between the adjusted vegetation index interpolation values ​​of two adjacent pixels satisfies the vegetation index variation described by the texture features, thus obtaining the vegetation index fusion result.

7. A satellite vegetation index fusion device based on optimal interpolation and fast Fourier transform, characterized in that, include: The product acquisition module is used to acquire meteorological vegetation index products and high-resolution vegetation index products. An interpolation module is used to perform multi-level interpolation processing on a specified vegetation index product as a background field, based on optimal interpolation and plane equation interpolation, to obtain a vegetation index interpolation. This includes: for any first grid point in a first grid to be interpolated, determining the vegetation index observation values ​​corresponding to multiple observation points adjacent to the first grid point from the meteorological vegetation index product, and determining the vegetation index background value corresponding to each observation point from the specified vegetation index product; based on the vegetation index observation values ​​and the vegetation index background values, performing optimal interpolation processing on the first grid point to obtain a vegetation index analysis value corresponding to the first grid point; for any second grid point in a second grid to be interpolated, determining the target grid in which the second grid point is located within the first grid; based on the vegetation index analysis values ​​corresponding to the first grid point contained in the target grid, performing plane equation interpolation processing on the second grid point to obtain a vegetation index interpolation value corresponding to the second grid point; wherein the resolution of the second grid is higher than the resolution of the first grid. The Fast Fourier Transform module is used to perform Fast Fourier Transform processing on the high-resolution vegetation index product to remove the seam areas contained in the high-resolution vegetation index product and obtain a new high-resolution vegetation index product. The fusion module is used to fuse the vegetation index interpolation with the texture features corresponding to the new high-resolution vegetation index product to obtain a vegetation index fusion result. The texture features are used to describe the vegetation index changes between two adjacent pixels in the new high-resolution vegetation index product.

8. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

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