Omnidirectional wave height spectrum correction method for spaceborne wavelength dispersive spectrometer

Abstract

The invention discloses a satellite-borne spectrometer omnidirectional wave high spectrum correction method, which belongs to the field of microwave remote sensing, and adopts a deep learning network and designs a loss function according to the spectral pattern characteristics of an SWIM omnidirectional wave high spectrum to train the deep learning network; wherein the omnidirectional wave high spectrum is also influenced by residual speckle noise at a high wave number (a wave number range greater than a peak wave number), and the consistency of the omnidirectional wave high spectrum and a reference spectrum at the high wave number is poor, so that the performance of spectrum parameters is influenced; the penalty term is added in the loss function to aggravate the penalty at the high-wave-number position, the part with the wave number larger than two times of the peak wave number serves as the high-wave-number part with the penalty needing to be aggravated, and it can be guaranteed that the corrected SWIM omnidirectional wave high spectrum is good in consistency with the buoy at the high-wave-number position.

Description

Technical Field

[0001] The invention belongs to the field of microwave remote sensing, and more specifically, relates to a method for correcting an omnidirectional wave height spectrum of a satellite-borne spectrometer. Background Art

[0002] The ocean is a treasure trove of natural resources for mankind, waiting to be explored, developed and utilized by mankind. As an intuitive manifestation of ocean movement, the detection of ocean wave spectrum has important scientific and application value. At present, the technologies for detecting ocean waves mainly include on-site detection and remote sensing detection.

[0003] With the development of satellite remote sensing technology, the China-France Oceanography Satellite CFOSAT (China France Oceanography Satellite) has been successfully launched into space. The SWIM (Surface Waves Investigation and Monitoring) carried on CFOSAT is the world's first satellite-borne spectrometer, which can detect waves in the wavelength range of 70-500m. The wave spectrum data obtained by its detection is of great value for marine science research and application.

[0004] At present, the research work on the performance of the satellite-borne spectrometer SWIM wave spectrum product has found that under small sea conditions, the consistency between the omnidirectional wave height spectrum measured by SWIM and the omnidirectional wave height spectrum measured by the buoy is poor. This is mainly reflected in the presence of pseudo-peaks at low wave numbers in the omnidirectional wave height spectrum measured by SWIM, and compared with the omnidirectional wave height spectrum of the buoy, the peak wave number of the SWIM omnidirectional spectrum is biased to the left, and the spectrum peak is low, which is caused by the surfboard effect. The above factors and other unknown nonlinear effects will reduce the performance of the SWIM wave spectrum product, and this spectrum value difference is currently difficult to correct by analytical methods.

[0005] In recent years, artificial neural networks have been introduced into the field of ocean parameter correction, such as significant wave height, mean wave period, etc. However, there has been no research on the correction of the SWIM wave spectrum product itself. Summary of the invention

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a method for correcting the omnidirectional wave height spectrum of a satellite-borne spectrometer to improve the performance of the ocean wave spectrum product of the satellite-borne spectrometer.

[0007] To achieve the above object, according to a first aspect of the present invention, a training method for a satellite-borne spectrometer omnidirectional wave height spectrum correction model is provided, comprising:

[0008] S1, preprocessing the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy to make them correspond one to one;

[0009] S2, construct a loss function to minimize the difference between the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the corresponding buoy, and train the correction model of the satellite-borne spectrometer omnidirectional wave height spectrum;

[0010] Among them, the satellite carrier spectrometer omnidirectional wave height spectrum correction model is a neural network model; the loss function is: M is the batch size of the training data; N is the number of wavenumber points in the unified wavenumber domain; k j is the jth wavenumber point in the unified wavenumber domain; is the peak wave number of the omnidirectional wave height spectrum measured by the buoy corresponding to the i-th SWIM omnidirectional wave height spectrum to be corrected in a batch of samples; is the SWIM omnidirectional wave height spectrum of the i-th sample after being corrected by the neural network model; is the omnidirectional wave height spectrum measured by the buoy corresponding to the i-th SWIM omnidirectional wave height spectrum to be corrected in a batch of samples; η * i is the dimensionless variance corresponding to the i-th spectrum to be corrected in a batch of samples; w ij It is a penalty coefficient based on the sea conditions and the relationship between the wave number and the peak wave number. a1 and a2 are the penalty coefficients of the high wave number part of the SWIM omnidirectional wave height spectrum in pure wind waves and pure swell waves, respectively.

[0011] According to the second aspect of the present invention, a method for correcting the omnidirectional wave height spectrum of a satellite-borne spectrometer is provided, wherein the SWIM omnidirectional wave height spectrum to be corrected is input into a satellite-borne spectrometer omnidirectional wave height spectrum correction model trained using the training method described in the first aspect, to obtain a corrected omnidirectional wave height spectrum.

[0012] According to a third aspect of the present invention, there is provided a training system for a satellite-borne spectrometer omnidirectional wave height spectrum correction model, comprising: a computer-readable storage medium and a processor;

[0013] The computer-readable storage medium is used to store executable instructions;

[0014] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the training method as described in the first aspect.

[0015] According to a fourth aspect of the present invention, there is provided a satellite-borne spectrometer omnidirectional wave height spectrum correction system, comprising: a computer-readable storage medium and a processor;

[0016] The computer-readable storage medium is used to store executable instructions;

[0017] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the correction method as described in the second invention.

[0018] According to a fifth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, wherein the computer instructions are used to enable a processor to execute the training method as described in the first aspect, or to execute the correction method as described in the second aspect.

[0019] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0020] 1. The method provided by the present invention adopts a deep learning network and designs a loss function according to the spectral characteristics of the SWIM omnidirectional wave height spectrum to train the deep learning network; wherein, considering that the omnidirectional wave height spectrum is also affected by residual speckle noise at high wavenumbers (wavenumber range greater than the peak wavenumber), and has poor consistency with the reference spectrum at high wavenumbers, thereby affecting the performance of the spectral parameters, a penalty term is added to the loss function to increase the penalty at high wavenumbers: the part with a wavenumber greater than twice the peak wavenumber is regarded as the high wavenumber part that needs to be punished more severely, which can ensure that the corrected SWIM omnidirectional wave height spectrum has good consistency with the buoy at high wavenumbers.

[0021] 2. The method provided by the present invention combines the characteristics of SWIM omnidirectional wave height spectrum to improve the U-Net network model to obtain the SWIM omnidirectional wave height spectrum correction network model. The difference between it and the U-Net network model is that: (1) Considering that the SWIM omnidirectional wave height spectrum data has negative spectral values, the SWIM omnidirectional wave height spectrum correction network model uses leakyReLU activation function in the encoder part to learn the features of negative spectral values ​​for subsequent correction; compared with the encoder part using ReLU activation function, the model can more effectively correct negative spectral values; through the jump connection between the encoder and the decoder, the extraction and fusion of feature information before and after correction of different dimensions are realized, so that the network model can effectively correct the SWIM omnidirectional wave height spectrum; (2) The SWIM omnidirectional wave height spectrum correction network model adds batch normalization (BN) layer after the convolution layer and the transposed convolution layer to solve the problem of internal covariate drift, thereby accelerating the learning speed of the network model and reducing the sensitivity of the network model to the initial learning rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a method for correcting omnidirectional wave height spectrum of a satellite-borne spectrometer provided in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of the structure of a SWIM omnidirectional wave height spectrum correction network model provided in an embodiment of the present invention;

[0024] Figure 3(a) to (c) are the comparison results of the average omnidirectional wave height spectra of SWIM and NDBC buoys before and after correction under the condition of 6-degree beam single wind and wave, U≤12m / s, and Ω at different values. Figure 3 (d) to (f) are the comparison results of the average omnidirectional wave height spectra of SWIM and NDBC buoys before and after correction under single wind and wave conditions, when U>12m / s and Ω takes different values; where U is the wind speed and Ω is the inverse wave age;

[0025] Figure 4 (a) to (c) in the figure are respectively the conditions of pure surge with a beam of 6 degrees and H s =1, 1.4m, k p Comparison of the average omnidirectional wave height spectra of SWIM and NDBC buoys before and after correction at different values. Figure 4 (d) to (f) in the figure are respectively the conditions of single surge, H s =1.8, 2.2m, k p Comparison of the average omnidirectional wave height spectra of SWIM and NDBC buoys before and after correction at different values; s is the effective wave height, k p is the peak wave number. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and 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. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] Deep learning has strong nonlinear fitting ability, and considering that the omnidirectional wave height spectrum has certain waveform characteristics, based on this, the present invention adopts a deep learning network and designs a loss function according to the spectral characteristics of the SWIM omnidirectional wave height spectrum to calibrate the parameters of the deep learning network.

[0028] The embodiment of the present invention provides a training method for a satellite-borne spectrometer omnidirectional wave height spectrum correction model, comprising:

[0029] S1, preprocessing the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy to make them correspond one to one.

[0030] Specifically, the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy are preprocessed. After the data preprocessing process, each SWIM omnidirectional wave height spectrum sample input into the network has a corresponding buoy omnidirectional wave height spectrum as a reference spectrum, i.e., the correction benchmark.

[0031] Furthermore, the preprocessing includes:

[0032] The SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy are successively unified in wavenumber domain, equalized and standardized.

[0033] Specifically, the wavenumber ranges of the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy are obtained respectively, and the wavenumber domains of the two are unified (that is, the common wavenumber range of the two); the sample sizes corresponding to the pure surge wave and pure wind wave sea conditions are balanced; the SWIM omnidirectional wave height spectrum to be corrected and the omnidirectional wave height spectrum measured by the buoy after the balanced operation are standardized to obtain the standardized spectrum of the SWIM omnidirectional wave height spectrum to be corrected and the reference spectrum of the buoy omnidirectional wave height spectrum; the reference spectrum of the buoy omnidirectional wave height spectrum is used as the benchmark for correcting the SWIM omnidirectional wave height spectrum to be corrected.

[0034] S2, construct a loss function to minimize the difference between the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the corresponding buoy, and train the correction model of the satellite-borne spectrometer omnidirectional wave height spectrum;

[0035] Specifically, the loss function of the regression layer is calculated using the mean square error (MSE), and L2 regularization is used to prevent overfitting.

[0036] The omnidirectional wave height spectrum is also affected by residual speckle noise at high wave numbers (wave number range greater than the peak wave number), and has poor consistency with the reference spectrum at high wave numbers, which in turn affects the performance of the spectrum parameters. Considering that the omnidirectional wave height spectrum has small spectral values ​​at high wave numbers, the present invention increases the penalty at high wave numbers by adding a penalty term to the loss function; the consistency between the spectrum to be corrected and the reference spectrum in the part where the wave number is greater than twice the peak wave number is poor, so this part is punished more severely, and the part where the wave number is greater than twice the peak wave number is regarded as the high wave number part that needs to be punished more severely. The weighted loss function for increasing the penalty for the high wave number part is expressed as follows:

[0037]

[0038] in,

[0039]

[0040] M is the batch size of the training data; N is the number of wavenumber points in the unified wavenumber domain; k j is the jth wavenumber point in the unified wavenumber domain; is the peak wave number of the reference spectrum corresponding to the i-th SWIM omnidirectional wave height spectrum to be corrected in a batch of samples; is the SWIM omnidirectional wave height spectrum of the i-th sample after being corrected by the neural network model; is the reference spectrum corresponding to the i-th SWIM omnidirectional wave height spectrum to be corrected in a batch of samples; η * i is the dimensionless variance corresponding to the i-th spectrum to be corrected in a batch of samples; w ij It is a penalty coefficient based on the sea conditions and the relationship between the wave number and the peak wave number. a1 and a2 are the penalty coefficients of the high wave number part of the SWIM omnidirectional wave height spectrum for pure wind waves and pure surge waves, respectively.

[0041] Preferably, the neural network model is a BU-Net network model; the BU-Net network model is obtained by processing a U-Net network model;

[0042] The processing includes: adding a batch normalization layer after each convolution layer in the encoder of the U-Net network model, each convolution layer in the decoder and the transposed convolution layer, and modifying the activation function of the encoder: using the leakyReLU activation function as the activation function of the encoder.

[0043] Specifically, the present invention combines the characteristics of the SWIM omnidirectional wave height spectrum to improve the U-Net network model and obtain the BU-Net network model, which is used as the SWIM omnidirectional wave height spectrum correction network model. The difference between it and the U-Net network model is that: (1) Considering that the SWIM omnidirectional wave height spectrum data has negative spectral values, the SWIM omnidirectional wave height spectrum correction network model uses the leakyReLU activation function in the encoder part to learn the characteristics of negative spectral values ​​for subsequent correction; compared with the use of the ReLU activation function in the encoder part, this model can more effectively correct negative spectral values. (2) The SWIM omnidirectional wave height spectrum correction network model adds a batch normalization (BN) layer after the convolution layer and the transposed convolution layer to solve the problem of internal covariate drift, thereby accelerating the learning speed of the network model and reducing the sensitivity of the network model to the initial learning rate.

[0044] The SWIM omnidirectional wave height spectrum correction network model uses a combination of leakyReLU activation function in the encoder part and ReLU activation function in the decoder part, so that the negative features of the SWIM omnidirectional wave height spectrum can be effectively corrected; through the jump connection between the encoder and the decoder, the extraction and fusion of feature information before and after correction in different dimensions are realized, so that the network model can effectively correct the SWIM omnidirectional wave height spectrum.

[0045] Furthermore, the structure diagram of the SWIM omnidirectional wave height spectrum correction network model is shown in Figure 2As shown. The input and output of the network are the uncorrected SWIM omnidirectional wave height spectrum and the corrected SWIM omnidirectional wave height spectrum, respectively. The BU-Net network structure is divided into an encoder and a decoder. The encoder includes multiple repeated structures, each of which includes: a convolutional layer, a BN layer (respectively connected after each convolutional layer), an activation function layer, and a pooling layer; the decoder includes multiple repeated structures, each of which includes: a transposed convolutional layer, a convolutional layer, a BN layer (respectively connected after each transposed convolutional layer and convolutional layer), and an activation function layer.

[0046] Preferably, if Figure 2 As shown in the figure, the encoder part includes a repetitive structure consisting of two convolutional layers (1×3), two batch normalization layers (BN, batch normalization), two activation layers and a pooling layer. The length and width of the input and output of the convolutional layer remain unchanged, and the number of convolution kernels determines the number of output channels. The BN layer is usually added after the convolution layer and before the activation function unit to speed up the training process. Considering that the spectral value of the input omnidirectional wave height spectrum will be less than 0, in order to retain the negative features of this part, the activation function of the encoder part uses leakyReLU. The size of the convolution kernel is 1×3 and the stride is 1. Downsampling is achieved by maximum pooling with a pooling size of 1×2 and a stride of 1×2, so that the size of the image is halved. After each downsampling, the number of convolution kernels is doubled and the above structure is repeated. The encoding process on the left continuously compresses the width of the feature. In the decoder part, upsampling is done by transposed convolution. After each upsampling, the number of convolution kernels is halved. The upsampling result is fused with the corresponding feature of the encoder part, and then the convolution operation is performed. The size and stride of the convolution kernel are the same as those of the encoder. The activation function of the decoder is ReLU.

[0047] In order to quantitatively analyze the correction effect of the omnidirectional wave height spectrum, the evaluation indicators include the correlation coefficient between the average omnidirectional wave height spectrum of SWIM and the average omnidirectional wave height spectrum of the corresponding buoy before or after correction, the integrated energy error and the relative error of the peak wave number.

[0048] Correlation coefficient CC (in Figure 3-4 C in the figure represents the correlation between the SWIM average omnidirectional wave height spectrum and the corresponding NDBC buoy average omnidirectional wave height spectrum before or after correction. The calculation method is as follows:

[0049]

[0050] Where N represents the wave number point, Indicates that at wave number point k i The average omnidirectional wave height spectrum before SWIM correction or the average omnidirectional wave height spectrum after SWIM correction by neural network, F buoy (k i) indicates that at wave number point k i The average omnidirectional wave height spectrum of the buoy at μ A , μ B Represents N wave number points respectively F buoy (k i )’s mean.

[0051] The integrated energy relative error ΔE is used to quantitatively describe the energy difference between the SWIM average omnidirectional wave height spectrum and the corresponding NDBC buoy average omnidirectional wave height spectrum before or after correction. The integrated energy error is calculated as follows:

[0052]

[0053] Among them, k i , and F buoy (k) has the same meaning as above, Δk i is an equal wavenumber interval in a uniform wavenumber domain.

[0054] Peak wave number relative error Δk p It is used to quantitatively describe the degree of deviation of the peak wavenumber of the SWIM average omnidirectional wave height spectrum before or after correction relative to the peak wavenumber of the corresponding NDBC buoy average omnidirectional wave height spectrum. p Defined as:

[0055]

[0056] Among them, k p_SWIM and k p_buoy are the peak wavenumbers of the SWIM average omnidirectional wave height spectrum and the corresponding NDBC buoy average omnidirectional wave height spectrum before or after correction. The peak wavenumber of the SWIM average omnidirectional wave height spectrum before correction is the wave value at the corresponding spectrum peak after excluding the pseudo peak, while the peak wavenumber of the SWIM average omnidirectional wave height spectrum after correction is directly determined by the spectrum peak.

[0057] The training method provided by the present invention is used to train the SWIM omnidirectional wave height spectrum correction network model, which is run on the NVIDIA TITAN X independent graphics card based on MATLAB2022b. The batch size of the neural network training is set to 256, the optimizer is Adam, the initial learning rate is set to 0.01, the loss function of the regression layer is calculated using the mean square error (MSE), and L2 regularization is used to prevent overfitting. a1=8, a2=10; the hyperparameter α of the LeakyReLU activation function of the encoder part of each beam network model is set to 0.01, and α represents the slope of the activation function when the input data is less than 0.

[0058] The SWIM omnidirectional wave height spectrum to be corrected is input into the trained SWIM omnidirectional wave height spectrum correction network model. Under single wind and wave conditions and single surge conditions, the comparison results of the average omnidirectional wave height spectra of SWIM and NDBC buoys before and after correction are as follows: Figure 3 , 4 shown.

[0059] Figure 3 Under various sea conditions of wind and waves shown in (a) to (f), the minimum correlation coefficient after 6° beam correction is 0.96, which is greater than the correlation coefficient before correction. The absolute value of the integrated energy error ΔE between the average omnidirectional wave height spectrum after SWIM 6° beam correction and the buoy data is less than 20%. The integrated energy error anomaly caused by the pseudo peak in small sea conditions is corrected. The relative error Δk between the peak wave number of the buoy after SWIM 6° beam correction p The absolute values ​​of are all less than 10%, which reduces the degree of peak wave number shift caused by the surfboard effect.

[0060] Figure 4 Under various sea surface conditions of swell waves shown in (a) to (f), the minimum correlation coefficient after 6° beam correction is 0.97, which is greater than the correlation coefficient before correction. The absolute value of the integrated energy error after SWIM 6° beam correction is less than 10%. From the relative error of peak wave number, the average omnidirectional wave height spectrum of SWIM 6° beam after correction corresponds to Δk p The absolute values ​​of are all less than 10%. Therefore, under pure swell sea surface conditions, the SWIM omnidirectional wave height spectrum correction network model has a good correction effect on the pseudo peaks and peak wave number offset in the SWIM omnidirectional wave height spectrum.

[0061] An embodiment of the present invention provides a method for correcting a satellite-borne spectrometer omnidirectional wave height spectrum, which inputs a SWIM omnidirectional wave height spectrum to be corrected into a satellite-borne spectrometer omnidirectional wave height spectrum correction model trained by the method described in any of the above embodiments to obtain a corrected omnidirectional wave height spectrum.

[0062] The embodiment of the present invention provides a training system for a satellite-borne spectrometer omnidirectional wave height spectrum correction model, comprising: a computer-readable storage medium and a processor;

[0063] The computer-readable storage medium is used to store executable instructions;

[0064] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the training method described in any of the above embodiments.

[0065] The embodiment of the present invention provides a satellite carrier spectrometer omnidirectional wave height spectrum correction system, including: a computer readable storage medium and a processor;

[0066] The computer-readable storage medium is used to store executable instructions;

[0067] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the correction method described in the above embodiment.

[0068] An embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to execute the training method described in any of the above embodiments, or execute the correction method described in the above embodiments.

[0069] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A training method for a satellite-borne spectrometer omnidirectional wave height spectrum correction model, characterized in that: include: S1, preprocessing the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy to make them correspond one to one; S2, construct a loss function to minimize the difference between the SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the corresponding buoy, and train the correction model of the satellite-borne spectrometer omnidirectional wave height spectrum; Among them, the satellite carrier spectrometer omnidirectional wave height spectrum correction model is a neural network model; the loss function is: M is the batch size of the training data; N is the number of wavenumber points in the unified wavenumber domain; k j is the jth wavenumber point in the unified wavenumber domain; is the peak wave number of the omnidirectional wave height spectrum measured by the buoy corresponding to the i-th SWIM omnidirectional wave height spectrum to be corrected in a batch of samples; is the SWIM omnidirectional wave height spectrum of the i-th sample after being corrected by the neural network model; is the omnidirectional wave height spectrum measured by the buoy corresponding to the i-th SWIM omnidirectional wave height spectrum to be corrected in a batch of samples; η * i is the dimensionless variance corresponding to the i-th spectrum to be corrected in a batch of samples; w ij It is a penalty coefficient based on the sea conditions and the relationship between the wave number and the peak wave number. a1 and a2 are the penalty coefficients of the high wave number part of the SWIM omnidirectional wave height spectrum in pure wind waves and pure swell waves, respectively.

2. The method according to claim 1, characterized in that The neural network model is a BU-Net network model; the BU-Net network model is obtained by processing a U-Net network model; The processing includes: adding a batch normalization layer after each convolution layer in the encoder of the U-Net network model, each convolution layer in the decoder and the transposed convolution layer, and using a leakyReLU activation function as the activation function of the encoder.

3. The method according to claim 1 or 2, characterized in that The pre-processing comprises: The SWIM omnidirectional wave height spectrum to be corrected measured by the satellite-borne spectrometer and the omnidirectional wave height spectrum measured by the buoy are successively unified in wavenumber domain, equalized and standardized.

4. A method for correcting the omnidirectional wave height spectrum of a satellite-borne spectrometer, characterized in that: The SWIM omnidirectional wave height spectrum to be corrected is input into the satellite-borne spectrometer omnidirectional wave height spectrum correction model trained by the training method according to any one of claims 1 to 3 to obtain the corrected omnidirectional wave height spectrum.

5. A training system for a satellite-borne spectrometer omnidirectional wave height spectrum correction model, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the training method as described in any one of claims 1-3.

6. A satellite-borne spectrometer omnidirectional wave height spectrum correction system, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the correction method as claimed in claim 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the training method as described in any one of claims 1 to 3, or to execute the correction method as described in claim 4.

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