A method and system for reconstruction of vegetation index

Abstract

The application belongs to the technical field of vegetation coefficient, and discloses a reconstruction method and system for vegetation coefficient, which comprises the following steps: obtaining a vegetation coefficient array of a satellite image as input, and representing the array as a three-dimensional tensor data; removing impervious surface data in the tensor, and dividing data in the tensor into three types of low, medium and high according to vegetation coverage; for data of different types, the average value of the tensor is calculated along different dimensions of the tensor, space and time, and then the average value is subtracted from the initial data to center the data along the space and time axes and initialize unknown data values; the reconstruction error of this iteration, i.e., the normalized root mean square error (NRMSE), is calculated, and if the error is less than the required value, the iteration is ended and the reconstructed tensor is output, otherwise the iteration is restarted; the reconstructed tensor is added to the subtracted average value to complete the de-centering, and finally the reconstructed vegetation index is obtained by merging reconstructed tensors of different types. The application reconstructs the missing vegetation index by using the tensor decomposition algorithm to iteratively reconstruct the missing vegetation index for different types of data, thereby ensuring the effectiveness and accuracy of the reconstruction.

Description

Technical Field

[0001] The present invention belongs to the technical field of vegetation coefficients, and in particular relates to a method and system for reconstructing vegetation coefficients. Background Art

[0002] Vegetation index is an indicator used in remote sensing to assess vegetation conditions. It is usually calculated based on the reflectance or radiation characteristics of vegetation in different spectral bands. Common vegetation indices such as the Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RVI), and the Difference Vegetation Index (DVI) are all calculated based on the reflectance of the near-infrared band and the reflectance of the red band. Vegetation indices are widely used and can be used for monitoring vegetation growth status, assessing vegetation cover, and analyzing crop health. However, the acquisition of vegetation indices relies on remote sensing images. Therefore, when the satellite sensor fails or captures a large area of ​​cloud cover, the vegetation index cannot be calculated. To address the problem of missing remote sensing data, many methods for reconstructing or predicting vegetation coefficients have emerged.

[0003] Because vegetation index reconstruction can be formulated as a spatiotemporal data recovery problem, mainstream approaches are categorized as follows: time series analysis, data mining, machine learning algorithms, and deep learning algorithms. Time series analysis utilizes historical vegetation index data and uses time series models (such as the ARIMA model) to predict future trends. Data mining employs techniques such as decision trees and cluster analysis to extract patterns and trends in vegetation indices from large amounts of remote sensing data. Machine learning algorithms employ various machine learning techniques, such as support vector machines (SVMs), random forests, and gradient boosting machines (GBMs), to build predictive models. Deep learning algorithms use convolutional neural networks (CNNs), long short-term memory networks (LSTMs), or a combination of these to learn the spatiotemporal characteristics of vegetation indices from remote sensing imagery. In short, the principle underlying these approaches is to extract the spatiotemporal characteristics of historical vegetation index data in order to reconstruct future vegetation indices.

[0004] Therefore, how to effectively extract the temporal and spatial features present in vegetation index data and how to combine them will determine the quality of prediction performance. Many methods simply exploit the temporal dependencies of the data while ignoring the spatial dependencies. Some methods, while taking both temporal and spatial dependencies into account, fail to achieve good results in combining these different features. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a method and system for reconstructing vegetation coefficients.

[0006] The present invention is achieved by a method for reconstructing vegetation coefficients, the method comprising:

[0007] S1. Get the vegetation coefficient array of the satellite image as input. The array is represented as a three-dimensional tensor data.

[0008] S2, remove the impervious surface data in the tensor, and classify the data in the tensor into three types: low, medium, and high according to the vegetation coverage;

[0009] S3. For different types of data, calculate the average value of the tensor along different dimensions, space, and time of the tensor, and then subtract these average values ​​from the initial data to center the data along the space and time axes and initialize the unknown data values;

[0010] S4. For different types of data, use the tensor decomposition algorithm PARAFAC to calculate the tensor decomposition of the current tensor representing the data in this iteration, estimate the current reconstructed data based on the tensor decomposition, and fill in the unknown values ​​in the reconstructed data;

[0011] S5. Calculate the reconstruction error of this iteration, i.e., the normalized root mean square error (NRMSE). If the error is less than the required value, terminate the iteration and output the reconstructed tensor. Otherwise, repeat the iteration.

[0012] S6. The reconstructed tensor is added with the subtracted mean value to complete the decentering, and finally the reconstructed tensors of different types are combined to obtain the reconstructed vegetation index.

[0013] Furthermore, the S1 includes:

[0014] The vegetation index contained in each satellite image is a two-dimensional data, which only has the characteristics of the spatial dimension, namely latitude and longitude. Collecting multiple satellite images over a continuous period of time is represented as a three-dimensional tensor data, whose dimensions are latitude, longitude, and time. Therefore, the three-dimensional tensor includes the characteristics of both spatial and temporal dimensions.

[0015] Furthermore, the S2 includes:

[0016] Auxiliary data is used to remove impervious surface data such as urban roads, reservoirs, and rivers in the image tensor, and the data in the tensor are divided into three types: low, medium, and high according to vegetation coverage.

[0017] Furthermore, the S3 includes:

[0018] For different types of data, ignore the missing data values, calculate the spatial dimension average and the time dimension average along the spatial dimension, latitude and longitude, and time dimension respectively, subtract the corresponding average from each data in the tensor, center each data on the spatial axis and time axis, and fill the missing data values ​​with zero values ​​to complete the initialization.

[0019] Furthermore, the S4 includes:

[0020] For different types of data, the tensor decomposition algorithm PARAFAC is used to calculate the tensor decomposition of the current tensor representing the data, and the reconstructed data is obtained by using the tensor decomposition. The unknown values ​​in the reconstructed data are filled according to the current tensor data.

[0021] Furthermore, the S5 includes:

[0022] The reconstruction error of this iteration, namely the normalized root mean square error NRMSE, is calculated. If the error is less than the required value, the iteration is terminated and the reconstructed tensor is output; otherwise, the iteration is repeated.

[0023] The normalized root mean square error NRMSE is:

[0024]

[0025] where y′ represents the missing value reconstructed in the current iteration, y represents the missing value reconstructed in the previous iteration, and σ[y] represents the variance of the reconstructed missing value in the previous iteration.

[0026] Furthermore, the S6 includes:

[0027] If the reconstruction error meets the requirements, the centered reconstructed tensor is output. Then, de-centering is performed, which means that the spatial and temporal average values ​​are added to each data point in the reconstructed tensor to maintain the consistency of the data before and after reconstruction. Finally, the different types of reconstructed data are combined to obtain the reconstructed vegetation index.

[0028] Furthermore, the PARAFAC tensor decomposition algorithm and solution process are as follows:

[0029] Assume the input three-dimensional tensor is Where I represents the latitude, J represents the longitude, and K represents the number of time periods.

[0030] The goal of the PARAFAC algorithm is to iteratively calculate the best approximation of χ using the alternating least squares ALS algorithm under the assumption that the number of rank-one components, that is, the number of reconstructed vegetation coefficient components R, is fixed. The goals are:

[0031]

[0032] in represents the vector outer product, A, B, and C represent the rank-one decomposition matrix of the tensor in different modes or dimensions, λ represents the weight vector, and a r 、b r 、c r Column vectors representing the rth rank-one components of different decomposition matrices.

[0033] The ALS algorithm first initializes the decomposition matrix, fixes B and C, and calculates the optimal A. Then, fix A and C and calculate the optimal B. Finally, fix A and B and calculate the optimal C. Repeat these steps until convergence. For example, when B and C are fixed, the problem is reduced to linear least squares, and the objective is rewritten as:

[0034]

[0035] where ⊙ denotes the Khatri–Rao product, T denotes the transpose of the matrix, X (1) represents the component of X under mode 1, and the optimal solution is:

[0036]

[0037] in represents the pseudo-inverse of the matrix. Perform column normalization to obtain A, that is, for r=1,…,R, let Both The same is true for calculating B and C. Repeat the above calculation until convergence, output λ, A, B, C, and have:

[0038]

[0039] Finally, the unknown values ​​in the reconstructed data are filled in according to the current tensor decomposition results, and the reconstruction error is calculated.

[0040] Another object of the present invention is to provide a vegetation coefficient reconstruction system based on the vegetation coefficient reconstruction method, the system specifically comprising:

[0041] The input acquisition module is used to obtain the vegetation coefficient array of the satellite image as input. The array is represented as a three-dimensional tensor data;

[0042] The elimination module is connected to the input acquisition module to eliminate the impervious surface data in the tensor and classify the data in the tensor into three types: low, medium and high according to the vegetation coverage;

[0043] The calculation module is connected to the elimination module. For different types of data, it calculates the average value of the tensor along different dimensions of the tensor, space and time, and then subtracts these average values ​​from the initial data to center the data along the space and time axes and initialize the unknown data values;

[0044] The tensor decomposition module is connected to the calculation module. For different types of data, it uses the tensor decomposition algorithm PARAFAC to calculate the tensor decomposition of the current tensor representation data in this iteration, estimates the current reconstructed data based on the tensor decomposition, and fills in the unknown values ​​in the reconstructed data.

[0045] The normalization module is connected to the tensor decomposition module to calculate the reconstruction error of this iteration, namely the normalized root mean square error NRMSE. If the error is less than the required value, the iteration ends and the reconstructed tensor is output. Otherwise, the iteration is repeated.

[0046] The merging module is connected to the normalization module, and the reconstructed tensor is added with the subtracted mean value to complete the decentering. Finally, the reconstructed tensors of different types are merged to obtain the reconstructed vegetation index.

[0047] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the vegetation coefficient reconstruction method.

[0048] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0049] The present invention comprehensively considers the spatial and temporal characteristics of the data, represents the data as a three-dimensional tensor, innovatively classifies the data according to vegetation coverage, and iteratively reconstructs the missing vegetation index for different types of data using a tensor decomposition algorithm, thus ensuring the effectiveness and accuracy of the reconstruction.

[0050] The technical problems solved by the technical solution of the present invention in industrial applications are:

[0051] 1) Solve the problem of incomplete vegetation coefficient data:

[0052] In the existing technology, when using satellite remote sensing images to obtain vegetation coefficient data, some spatial or temporal data are missing due to cloud obstruction, equipment failure, transmission interference, data collection cycle limitations, etc., resulting in incomplete data and affecting the accuracy of vegetation cover analysis.

[0053] 2) Solve the problem of data noise and outliers:

[0054] Traditional vegetation index reconstruction methods often fail to exclude impervious surface data or fail to classify according to vegetation coverage, resulting in large data noise and high outliers in the reconstruction results, making the reconstruction results unreliable.

[0055] 3) Solve the problem of insufficient accuracy and efficiency of reconstruction algorithm:

[0056] Existing reconstruction methods (such as linear interpolation and simple mean filling) struggle to ensure data consistency and integrity across time and space, resulting in low reconstruction accuracy. Furthermore, some complex algorithms are computationally intensive, have slow iterative convergence, and exhibit low computational efficiency, making them difficult to apply to large-scale data reconstruction.

[0057] The significant technical advancements achieved by the present invention are:

[0058] 1) Improve data integrity and accuracy:

[0059] The present invention effectively eliminates irrelevant data and reduces data noise by eliminating impervious surface data and classifying the data into low, medium and high vegetation coverage.

[0060] The tensor decomposition algorithm (PARAFAC) is used to reconstruct and fill in different types of vegetation coefficient data in time and space dimensions, significantly improving the integrity and accuracy of the data.

[0061] 2) Optimize spatial and temporal consistency:

[0062] By calculating the average value along the time axis and the spatial axis and centering the data, the data is balanced along the two dimensions before and after reconstruction, eliminating data deviation.

[0063] After tensor decomposition and reconstruction, the mean value is added back to complete the decentering, ensuring the coherence and consistency of the data in spatial and temporal dimensions.

[0064] 3) Iterative optimization improves reconstruction accuracy:

[0065] The present invention monitors the reconstruction error (normalized root mean square error NRMSE) in real time through iterative calculation, and terminates the iteration only when the error is less than a preset threshold.

[0066] This adaptive iterative optimization mechanism greatly improves the reconstruction accuracy, making the final result meet high-precision requirements.

[0067] 4) Enhanced computing efficiency and big data adaptability:

[0068] This method adopts the PARAFAC tensor decomposition algorithm, which can efficiently process large-scale three-dimensional tensor data with low computational complexity and is suitable for vegetation index reconstruction over a large area and for a long time.

[0069] Classification of the processed data (low, medium, and high vegetation cover) further reduces computational redundancy and improves the efficiency of data reconstruction.

[0070] 5) Extensive industrial application value:

[0071] The present invention can be applied to fields such as ecological monitoring, land use analysis, and vegetation restoration assessment, and has significant advantages in particular in cloud-prone areas or urban greening data analysis.

[0072] Through high-precision data reconstruction, accurate and reliable data support can be provided for vegetation coverage analysis, environmental protection policy formulation, agricultural resource planning, etc.

[0073] The present invention significantly improves the integrity, accuracy and efficiency of vegetation coefficient data reconstruction through classification and noise elimination, centering processing, tensor decomposition and iterative optimization, and solves the problems of data missing, noise interference and high computational complexity in the existing technology. It has broad industrial application value and significant technological progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of a method for reconstructing vegetation coefficients provided by an embodiment of the present invention;

[0075] Figure 2 This is a structural diagram of a vegetation coefficient reconstruction system provided by an embodiment of the present invention;

[0076] In the figure: 1. Input acquisition module; 2. Elimination module; 3. Computation module; 4. Tensor decomposition module; 5. Normalization module; 6. Merging module. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0078] The following are two specific examples of the vegetation coefficient reconstruction method, detailing the application process and effects in different scenarios:

[0079] Example 1: Reconstruction of regional vegetation index based on remote sensing data

[0080] Long-term uneven rainfall and human activities have impacted a region, resulting in incomplete or anomaly vegetation index data. Three-dimensional tensor data of vegetation coefficients for this region, obtained through satellite imagery, needs to be reconstructed to improve data integrity and accuracy.

[0081] 1) Get input data (S1):

[0082] Use satellite remote sensing images (such as MODIS or Landsat) to obtain vegetation index data for the area over a period of time to form a three-dimensional tensor with dimensions including time, spatial longitude, and spatial latitude.

[0083] 2) Eliminate impervious surface data and classify (S2):

[0084] Eliminate data on impervious surfaces such as buildings and roads.

[0085] According to vegetation coverage, regional data are divided into three types: low vegetation coverage (such as 0-30%), medium vegetation coverage (such as 30-70%) and high vegetation coverage (70-100%).

[0086] 3) Data centering (S3):

[0087] Calculate the average of the three types of data along the time and space dimensions respectively.

[0088] Subtract the corresponding mean from the initial data to center the data and initialize the missing values ​​in the data.

[0089] 4) Tensor decomposition and reconstruction (S4):

[0090] The PARAFAC tensor decomposition algorithm is used to decompose the low, medium and high types of tensor data respectively.

[0091] The data were reconstructed based on the decomposition results, missing values ​​were filled in, and preliminary reconstructed vegetation index data were obtained.

[0092] 5) Iterative error detection (S5):

[0093] The normalized root mean square error (NRMSE) is calculated to determine whether the reconstruction result meets the preset accuracy requirements (such as NRMSE < 0.05).

[0094] If it is not satisfied, continue iterating until the error meets the requirement.

[0095] 6) Data de-centering and merging (S6):

[0096] Decentering is accomplished by adding the reconstructed data back to the previously subtracted mean.

[0097] The reconstructed tensor data of three types, low, medium and high, are merged to obtain the complete vegetation index data.

[0098] The reconstruction of vegetation index data was completed in this area, missing values ​​were filled, and the spatial continuity and temporal consistency of the data were significantly improved.

[0099] The reconstruction error NRMSE is 0.03, which meets the accuracy requirements.

[0100] Example 2: Reconstruction and Analysis of Urban Greening Vegetation Index

[0101] During a city's greening assessment, vegetation index data for some time periods was missing due to cloud cover and equipment interference. This method was used to reconstruct vegetation index data for urban greening areas to accurately assess vegetation coverage.

[0102] 1) Get input data (S1):

[0103] Vegetation index data are obtained through remote sensing satellite images of urban areas to form three-dimensional tensor data of time, space (urban grid coordinates) and green area types.

[0104] 2) Eliminate impervious surface data and classify (S2):

[0105] Eliminate impervious surface data such as buildings, roads, and water bodies.

[0106] The data is divided into three categories according to greening areas: park green space (high vegetation coverage), residential greening (medium vegetation coverage) and industrial greening (low vegetation coverage).

[0107] 3) Data centering (S3):

[0108] The average values ​​of the three regional types of data are calculated along the time and space dimensions respectively.

[0109] The mean was subtracted from the data, the data were centered along the temporal and spatial axes, and missing values ​​were initialized.

[0110] 4) Tensor decomposition and reconstruction (S4):

[0111] The PARAFAC tensor decomposition algorithm is used on three types of data: park green space, residential green space and industrial green space, to obtain the tensor decomposition representation of each area.

[0112] Estimate data based on tensor decomposition results and fill missing values ​​in a 3D tensor.

[0113] 5) Iterative error detection (S5):

[0114] Calculate the normalized root mean square error (NRMSE) for each iteration.

[0115] When the NRMSE is less than 0.04, the iteration ends and the reconstructed data is output.

[0116] 6) Data de-centering and merging (S6):

[0117] The three types of reconstructed data were added back to their respective means.

[0118] The reconstructed park green space, residential area greening and industrial area greening data were merged to obtain the complete vegetation index data of the urban area.

[0119] The reconstruction of the vegetation index in urban areas was completed, solving the problem of data missing caused by cloud cover.

[0120] The NRMSE of the reconstruction result is 0.035, ensuring high accuracy.

[0121] It provides complete urban greening assessment data to support greening management and planning decisions.

[0122] The above two examples demonstrate the application effects of this method in different scenarios, including regional vegetation index reconstruction and urban greening assessment. Both achieve high-precision data reconstruction through tensor decomposition and iterative optimization, and have strong applicability and practical value.

[0123] like Figure 1 As shown, an embodiment of the present invention provides a method for reconstructing vegetation coefficients, the method comprising:

[0124] S1. Get the vegetation coefficient array of the satellite image as input. The array is represented as a three-dimensional tensor data.

[0125] S2, remove the impervious surface data in the tensor, and classify the data in the tensor into three types: low, medium, and high according to the vegetation coverage;

[0126] S3. For different types of data, calculate the average value of the tensor along different dimensions of the tensor, space and time, and then subtract these average values ​​from the initial data to center the data along the space and time axes and initialize the unknown data values;

[0127] S4. For different types of data, use the tensor decomposition algorithm PARAFAC to calculate the tensor decomposition of the current tensor representing the data in this iteration, estimate the current reconstructed data based on the tensor decomposition, and fill in the unknown values ​​in the reconstructed data;

[0128] S5. Calculate the reconstruction error of this iteration, i.e., the normalized root mean square error (NRMSE). If the error is less than the required value, terminate the iteration and output the reconstructed tensor. Otherwise, repeat the iteration.

[0129] S6. The reconstructed tensor is added with the subtracted mean value to complete the decentering, and finally the reconstructed tensors of different types are combined to obtain the reconstructed vegetation index.

[0130] Said S1 comprises:

[0131] The vegetation index contained in each satellite image is a two-dimensional data, which only has the characteristics of the spatial dimension, namely latitude and longitude. Collecting multiple satellite images over a continuous period of time is represented as a three-dimensional tensor data, whose dimensions are latitude, longitude, and time. Therefore, the three-dimensional tensor includes the characteristics of both spatial and temporal dimensions.

[0132] Said S2 comprises:

[0133] Auxiliary data is used to remove impervious surface data such as urban roads, reservoirs, and rivers in the image tensor, and the data in the tensor are divided into three types: low, medium, and high according to vegetation coverage.

[0134] Said S3 comprises:

[0135] For different types of data, ignore the missing data values, calculate the spatial dimension average and the time dimension average along the spatial dimension, latitude and longitude, and time dimension respectively, subtract the corresponding average from each data in the tensor, center each data on the spatial axis and time axis, and fill the missing data values ​​with zero values ​​to complete the initialization.

[0136] Said S4 comprises:

[0137] For different types of data, the tensor decomposition algorithm PARAFAC is used to calculate the tensor decomposition of the current tensor representing the data, and the reconstructed data is obtained by using the tensor decomposition. The unknown values ​​in the reconstructed data are filled according to the current tensor data.

[0138] Said S5 comprises:

[0139] The reconstruction error of this iteration, namely the normalized root mean square error NRMSE, is calculated. If the error is less than the required value, the iteration is terminated and the reconstructed tensor is output; otherwise, the iteration is repeated.

[0140] The normalized root mean square error NRMSE is:

[0141]

[0142] where y′ represents the missing value reconstructed in the current iteration, y represents the missing value reconstructed in the previous iteration, and σ[y] represents the variance of the reconstructed missing value in the previous iteration.

[0143] Said S6 comprises:

[0144] If the reconstruction error meets the requirements, the centered reconstructed tensor is output. Then, de-centering is performed, which means that the spatial and temporal average values ​​are added to each data point in the reconstructed tensor to maintain the consistency of the data before and after reconstruction. Finally, the different types of reconstructed data are combined to obtain the reconstructed vegetation index.

[0145] The PARAFAC tensor decomposition algorithm and solution process are as follows:

[0146] Assume the input three-dimensional tensor is Where I represents the latitude, J represents the longitude, and K represents the number of time periods.

[0147] The goal of the PARAFAC algorithm is to iteratively calculate the number of rank-one components, that is, the number of reconstructed vegetation coefficient components R, using the alternating least squares ALS algorithm. The best approximation of

[0148] The goals are:

[0149]

[0150] in represents the vector outer product, A, B, and C represent the rank-one decomposition matrix of the tensor in different modes or dimensions, λ represents the weight vector, and a r 、b r 、c r Column vectors representing the rth rank-one components of different decomposition matrices.

[0151] The ALS algorithm first initializes the decomposition matrix, fixes B and C, and calculates the optimal A. Then, fix A and C and calculate the optimal B. Finally, fix A and B and calculate the optimal C. Repeat these steps until convergence. For example, when B and C are fixed, the problem is reduced to linear least squares, and the objective is rewritten as:

[0152]

[0153] where ⊙ denotes the Khatri–Rao product, T denotes the transpose of the matrix, X represents the component of X in mode 1, and the optimal solution is:

[0154]

[0155] in represents the pseudo-inverse of the matrix. Perform column normalization to obtain A, that is, for r=1,…,R, let Both The same is true for calculating B and C. Repeat the above calculation until convergence, output λ, A, B, C, and have:

[0156]

[0157] Finally, the unknown values ​​in the reconstructed data are filled in according to the current tensor decomposition results, and the reconstruction error is calculated.

[0158] like Figure 2 As shown, an embodiment of the present invention provides a vegetation coefficient reconstruction system based on the vegetation coefficient reconstruction method, and the system specifically includes:

[0159] Input acquisition module 1 is used to obtain the vegetation coefficient array of the satellite image as input, and the array is represented as a three-dimensional tensor data;

[0160] Elimination module 2 is connected to input acquisition module 1, eliminates impervious surface data in the tensor, and classifies the data in the tensor into three types: low, medium, and high according to vegetation coverage;

[0161] The calculation module 3 is connected to the elimination module 2. For different types of data, it calculates the average value of the tensor along different dimensions of the tensor, space and time, and then subtracts these average values ​​from the initial data to center the data along the space and time axes and initialize the unknown data values;

[0162] The tensor decomposition module 4 is connected to the calculation module 3. For different types of data, it uses the tensor decomposition algorithm PARAFAC to calculate the tensor decomposition of the current tensor representation data in this iteration, estimates the current reconstructed data based on the tensor decomposition, and fills in the unknown values ​​in the reconstructed data;

[0163] Normalization module 5 is connected to tensor decomposition module 4 to calculate the reconstruction error of this iteration, namely the normalized root mean square error NRMSE. If the error is less than the required value, the iteration is terminated and the reconstructed tensor is output. Otherwise, the iteration is repeated.

[0164] The merging module 6 is connected to the normalization module 5, and the reconstructed tensor is added with the subtracted mean value to complete the decentering, and finally the reconstructed tensors of different types are merged to obtain the reconstructed vegetation index.

[0165] An embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the vegetation coefficient reconstruction method.

[0166] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0167] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for reconstructing vegetation index, characterized in that: The method includes: S1. Get the vegetation index array of the satellite image as input. The array is represented as a three-dimensional tensor data. S2, remove the impervious surface data in the tensor, and classify the data in the tensor into three types: low, medium, and high according to the vegetation coverage; S3. For different types of data, calculate the average value of the tensor along different dimensions of the tensor, space and time, and then subtract these average values ​​from the initial data to center the data along the space and time axes and initialize the unknown data values; S4. For different types of data, use the tensor decomposition algorithm PARAFAC to calculate the tensor decomposition of the current tensor representing the data in this iteration, estimate the current reconstructed data based on the tensor decomposition, and fill in the unknown values ​​in the reconstructed data; S5. Calculate the reconstruction error of this iteration, i.e., the normalized root mean square error (NRMSE). If the error is less than the required value, terminate the iteration and output the reconstructed tensor. Otherwise, repeat the iteration. S6. The reconstructed tensor is added with the subtracted mean value to complete the decentering, and finally the reconstructed tensors of different types are combined to obtain the reconstructed vegetation index.

2. The vegetation index reconstruction method according to claim 1, characterized in that: Said S1 comprises: The vegetation index contained in each satellite image is a two-dimensional data, which only has the characteristics of the spatial dimension, namely latitude and longitude. Collecting multiple satellite images over a continuous period of time is represented as a three-dimensional tensor data, whose dimensions are: latitude, longitude, and time. Therefore, the three-dimensional tensor includes the characteristics of both spatial and temporal dimensions.

3. The vegetation index reconstruction method according to claim 1, characterized in that: Said S2 comprises: Auxiliary data is used to remove impervious surface data in the image tensor, which includes urban roads, reservoirs, and rivers. At the same time, the data in the tensor is divided into three types: low, medium, and high according to vegetation coverage.

4. The vegetation index reconstruction method according to claim 1, characterized in that: Said S3 comprises: For different types of data, ignore the missing data values, calculate the spatial dimension average and the time dimension average along the spatial dimension, latitude and longitude, and time dimension respectively, subtract the corresponding average from each data in the tensor, center each data on the spatial axis and time axis, and fill the missing data values ​​with zero values ​​to complete the initialization.

5. The vegetation index reconstruction method according to claim 1, characterized in that: Said S4 comprises: For different types of data, the tensor decomposition algorithm PARAFAC is used to calculate the tensor decomposition of the current tensor representing the data, and the reconstructed data is obtained by using the tensor decomposition. The unknown values ​​in the reconstructed data are filled according to the current tensor data.

6. The vegetation index reconstruction method according to claim 1, characterized in that: Said S5 comprises: Calculate the reconstruction error of this iteration, that is, the normalized root mean square error NRMSE. If the error is less than the required value, the iteration ends and the reconstructed tensor is output. Otherwise, the iteration is repeated. The normalized root mean square error NRMSE is: where y′ represents the missing value reconstructed in the current iteration, y represents the missing value reconstructed in the previous iteration, and σ[y] represents the variance of the reconstructed missing value in the previous iteration.

7. The vegetation index reconstruction method according to claim 1, characterized in that: Said S6 comprises: If the reconstruction error meets the requirements, the centered reconstructed tensor is output, and then de-centered is performed, that is, each data in the reconstructed tensor is added with the average value in the spatial and temporal dimensions to maintain the consistency of the data before and after reconstruction; finally, different types of reconstructed data are merged to obtain the reconstructed vegetation index.

8. The vegetation index reconstruction method according to claim 5, characterized in that: The tensor decomposition algorithm PARAFAC and the solution process are as follows: Assume the input three-dimensional tensor is Where I represents the latitude, J represents the longitude, and K represents the number of time periods; The goal of the PARAFAC algorithm is to iteratively calculate the best approximation of x using the alternating least squares ALS algorithm under the assumption that the number of rank-one components, that is, the number of reconstructed vegetation index components R, is fixed. The goals are: in represents the vector outer product, A, B, and C represent the rank-one decomposition matrix of the tensor in different modes or dimensions, λ represents the weight vector, and a r 、b r 、c r Column vectors representing the rth rank-one components of different decomposition matrices; The ALS algorithm first initializes the decomposition matrix, fixes B and C, and calculates the optimal A; then fixes A and C, calculates the optimal B; finally, fixes A and B, calculates the optimal C; repeats the above steps until convergence; when B and C are fixed, the problem is reduced to linear least squares, and the objective is rewritten as: where ⊙ denotes the Khatri–Rao product, T denotes the transpose of the matrix, X (1) represents the component of X under mode 1, and the optimal solution is: in represents the pseudo-inverse of the matrix; finally Perform column normalization to obtain A, that is, for r=1,…,R, let Both The calculation of B and C is the same; repeat the above calculation until convergence, output λ, A, B, C, and have: Finally, the unknown values ​​in the reconstructed data are filled in according to the current tensor decomposition results, and the reconstruction error is calculated.

9. A vegetation index reconstruction system based on the vegetation index reconstruction method according to claims 1-8, characterized in that: The system specifically includes: The input acquisition module is used to obtain the vegetation index array of the satellite image as input. The array is represented as a three-dimensional tensor data. The elimination module is connected to the input acquisition module to eliminate the impervious surface data in the tensor and classify the data in the tensor into three types: low, medium and high according to the vegetation coverage; The calculation module is connected to the elimination module. For different types of data, it calculates the average value of the tensor along different dimensions of the tensor, space and time, and then subtracts these average values ​​from the initial data to center the data along the space and time axes and initialize the unknown data values; The tensor decomposition module is connected to the calculation module. For different types of data, it uses the tensor decomposition algorithm PARAFAC to calculate the tensor decomposition of the current tensor representation data in this iteration, estimates the current reconstructed data based on the tensor decomposition, and fills in the unknown values ​​in the reconstructed data. The normalization module is connected to the tensor decomposition module to calculate the reconstruction error of this iteration, namely the normalized root mean square error NRMSE. If the error is less than the required value, the iteration ends and the reconstructed tensor is output. Otherwise, the iteration is repeated. The merging module is connected to the normalization module, and the reconstructed tensor is added with the subtracted mean value to complete the decentering. Finally, the reconstructed tensors of different types are merged to obtain the reconstructed vegetation index.

10. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the vegetation index reconstruction method according to any one of claims 1 to 8.

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