A method and system for retrieving dynamic parameters of oceanic eddies in a SAR image

Abstract

The application provides a method and system for inverting marine vortex dynamic parameters in a SAR image, which is based on a vortex SAR imaging theory model to invert and extract dynamic characteristics of the vortex, and a quantitative relationship between a SAR image radar backscattering coefficient and a vortex physical model is constructed, which is different from previous methods of digital image processing and machine learning, which can only extract the position and radius characteristics of the vortex. The vortex dynamic parameters obtained by the method include vortex center position, suction intensity, viscosity coefficient, velocity circulation, maximum rotation speed radius of the vortex, maximum angular velocity, maximum vortex intensity and other dynamic parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of microwave remote sensing, in particular to a method and system for retrieving oceanic vortex dynamic parameters from SAR images. BACKGROUND

[0002] At present, the quantitative detection of vortexes based on SAR images is mostly from the perspective of image processing, and the vortex information is retrieved according to the gray value characteristics of the SAR images. However, in order to study the physical oceanic process of the vortex and quantitatively interpret the characteristics and dynamic process of the vortex in the SAR image, it is necessary to establish a connection between the radar backscattering coefficient of the SAR image and the physical model of the vortex. On the basis of the vortex SAR imaging theory model, a vortex dynamic parameter retrieval model and method based on the SAR image is constructed to retrieve and extract the dynamic characteristic parameters of the vortex.

[0003] The commonly used method for retrieving ocean phenomenon parameters from SAR images internationally is the curve fitting method. The curve fitting method can fit the vortex intensity variation curve and estimate the vortex dynamic parameters according to the SAR image backscattering coefficient. However, the curve fitting method only considers the influence of additive random noise, while the influence of multiplicative speckle noise in the SAR image is also particularly serious. Therefore, based on the curve fitting method, the vortex parameter retrieval from the SAR image has poor accuracy of vortex dynamic parameter retrieval due to the mismatch of model noise.

[0004] The present method constructs a SAR image ocean vortex detection model affected by multiplicative speckle noise, proposes a method for retrieving ocean vortex hydrodynamic parameters from SAR images, and retrieves the center position, maximum rotational velocity radius, maximum angular velocity, maximum vortex intensity and other dynamic parameters of the vortex by constructing a joint probability density function of the ocean vortex SAR image theoretical model and the sea surface scattering value, combining the maximum likelihood estimation theory and the Newton iteration method. SUMMARY

[0005] To solve at least one of the above problems, the first aspect of the present application provides a method for retrieving ocean vortex dynamic parameters from SAR images, comprising:

[0006] obtaining an ocean vortex radar image theoretical model according to a sea wave-radar modulation transfer function and a vortex empirical velocity field model;

[0007] establishing a vortex hydrodynamic model according to the ocean vortex radar image theoretical model and the SAR radar sampling results;

[0008] obtaining vortex dynamic parameters from the vortex physical parameters in the SAR image in combination with the vortex hydrodynamic model, wherein the physical parameters include the vortex center position, the suction intensity, the viscosity coefficient and the velocity circulation, and the dynamic parameters include the maximum rotational velocity radius, the maximum angular velocity and the maximum vortex intensity.

[0009] Further, the method for retrieving oceanic vortex dynamic parameters from SAR images further comprises:

[0010] Preprocessing the obtained SAR images.

[0011] Further, the method for obtaining a theoretical model of oceanic vortex radar images from a sea wave-radar modulation transfer function and a vortex empirical velocity field model comprises:

[0012] Obtaining an initial value of vortex parameters according to radar parameters and the sea wave-radar modulation transfer function;

[0013] Inputting the initial value of vortex parameters into the vortex empirical velocity field model to obtain an expression of wave number spectrum of sea surface wave field;

[0014] Obtaining a theoretical model of oceanic vortex radar images according to the expression of wave number spectrum of sea surface wave field and Bragg scattering theory.

[0015] Further, the method for establishing a vortex hydrodynamic model according to the theoretical model of oceanic vortex radar images and SAR radar sampling results comprises:

[0016] Obtaining at least two signal sequences obtained by SAR radar sampling points, wherein a profile of a vortex at any position can be characterized by the signal sequences obtained by SAR radar sampling;

[0017] Obtaining a joint probability density function of sea surface scattering values according to the at least two signal sequences and the theoretical model of oceanic vortex radar images;

[0018] Obtaining the vortex hydrodynamic model according to the joint probability density function of sea surface scattering values and maximum likelihood estimation theory.

[0019] Further, the method for obtaining vortex dynamic parameters from vortex physical parameters in SAR images and the vortex hydrodynamic model comprises:

[0020] Extracting the vortex physical parameters from SAR images;

[0021] Obtaining an estimated value of vortex parameters according to the physical parameters and Newton iteration method after multiple iterations;

[0022] Obtaining the vortex dynamic parameters according to the estimated value of vortex parameters and the vortex hydrodynamic model.

[0023] Further, the method for preprocessing the obtained SAR images comprises:

[0024] The SAR image is geometrically corrected in combination with a geometric correction mode and a nearest neighbor resampling method.

[0025] The SAR image is subjected to speckle noise suppression processing by using a Gamma MAP filtering method.

[0026] The second aspect of the present application provides a system for inverting oceanic vortex dynamic parameters in a SAR image, comprising:

[0027] An oceanic vortex radar image theoretical model construction module: an oceanic vortex radar image theoretical model is obtained according to a sea wave-radar modulation transfer function and a vortex empirical velocity field model;

[0028] A vortex hydrodynamic model construction module: a vortex hydrodynamic model is established according to the oceanic vortex radar image theoretical model and a SAR radar sampling result;

[0029] A vortex dynamic parameter calculation module: vortex dynamic parameters are obtained according to vortex physical parameters in a SAR image in combination with the vortex hydrodynamic model, wherein the physical parameters include vortex center positions, suction intensities, viscosity coefficients and velocity circulations, and the dynamic parameters include vortex maximum rotating speed radii, maximum rotating angular velocities and maximum vortex intensities.

[0030] Further, the system for inverting oceanic vortex dynamic parameters in a SAR image further comprises:

[0031] A SAR image preprocessing module: a SAR image obtained is preprocessed.

[0032] The third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the method for inverting oceanic vortex dynamic parameters in a SAR image according to any one of the above aspects when executing the computer program.

[0033] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method for inverting oceanic vortex dynamic parameters in a SAR image according to any one of the above aspects when executed by a processor.

[0034] Advantages of the invention

[0035] The application provides a method and system for inverting marine vortex dynamic parameters in a SAR image, which is based on a vortex SAR imaging theory model to invert and extract dynamic characteristics of the vortex, and a quantitative relationship between a SAR image radar backscattering coefficient and a vortex physical model is constructed, which is different from previous methods of digital image processing and machine learning, which can only extract the position and radius characteristics of the vortex. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0037] Figure 1 A flowchart of the method for inverting marine vortex dynamic parameters in a SAR image in the embodiments of the present application is shown.

[0038] Figure 2 A comparison chart of ERS-2 SAR images before and after preprocessing in the embodiments of the present application is shown.

[0039] Figure 3 A flowchart of calculating vortex dynamic parameters by the Newton iteration method in the embodiments of the present application is shown.

[0040] Figure 4 A comparison chart of vortex profile radar backscattering value estimation results of ERS-2 SAR images by different methods in the embodiments of the present application is shown.

[0041] Figure 5 A structural schematic diagram of an electronic device in the embodiments of the present application is shown. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0043] At present, the vortex parameter in the SAR image is inverted based on the curve fitting method, and the vortex dynamic parameter inversion accuracy is poor due to the model noise mismatch.

[0044] Based on this, the application provides a method for inverting oceanic eddy dynamic parameters in a SAR image, as shown in the accompanying drawings, comprising the following steps: Figure 1

[0045] According to the sea wave-radar modulation transfer function and the eddy empirical velocity field model, an oceanic eddy radar image theoretical model is obtained;

[0046] According to the oceanic eddy radar image theoretical model and the SAR radar sampling result, an eddy hydrodynamic model is established;

[0047] According to the eddy physical parameters in the SAR image, the eddy hydrodynamic model is combined to obtain eddy dynamic parameters, wherein the physical parameters include the eddy center position, the suction intensity, the viscosity coefficient and the velocity circulation, and the dynamic parameters include the maximum rotation speed radius, the maximum rotation angular velocity and the maximum eddy intensity.

[0048] In some other embodiments, as shown in the accompanying drawings, the method for inverting oceanic eddy dynamic parameters in a SAR image further comprises the following steps: Figure 1

[0049] The obtained SAR image is preprocessed.

[0050] In some other embodiments, the oceanic eddy radar image theoretical model is obtained according to the sea wave-radar modulation transfer function and the eddy empirical velocity field model, comprising the following steps:

[0051] According to the radar parameters and the sea wave-radar modulation transfer function, an initial value of the eddy parameter estimation is obtained;

[0052] The initial value of the eddy parameter estimation is input into the eddy empirical velocity field model to obtain a wave number spectrum expression of the sea wave field;

[0053] According to the wave number spectrum expression of the sea wave field and the Bragg scattering theory, the oceanic eddy radar image theoretical model is obtained.

[0054] In some other embodiments, the eddy hydrodynamic model is established according to the oceanic eddy radar image theoretical model and the SAR radar sampling result, comprising the following steps:

[0055] At least two signal sequences obtained by the SAR radar sampling points are obtained, wherein a profile at any position of the eddy can be characterized by the signal sequences obtained by the SAR radar sampling;

[0056] According to the at least two signal sequences, the oceanic eddy radar image theoretical model is combined to obtain a sea surface scattering value joint probability density function;

[0057] ​​According to the sea surface scattering value joint probability density function, and in combination with maximum likelihood estimation theory, the vortex water dynamic model is obtained.

[0058] In some other embodiments, the vortex dynamic parameter is obtained according to the vortex physical parameter in the SAR image and in combination with the vortex water dynamic model, and the method comprises the following steps.

[0059] The vortex physical parameter is extracted from the SAR image.

[0060] According to the physical parameter and in combination with Newton iteration method, the vortex parameter estimation value is obtained after multiple iterations.

[0061] According to the vortex parameter estimation value and the vortex water dynamic model, the vortex dynamic parameter is obtained.

[0062] In some other embodiments, the SAR image is preprocessed, and the method comprises the following steps.

[0063] The SAR image is geometrically corrected by combining a geometric correction method and a nearest neighbor resampling method.

[0064] The SAR image is subjected to speckle noise suppression by using a Gamma MAP filtering method.

[0065] Another aspect of the present application provides a system for inverting a marine vortex dynamic parameter in a SAR image, which comprises the following modules.

[0066] A marine vortex radar image theoretical model construction module is configured to obtain a marine vortex radar image theoretical model according to a sea wave-radar modulation transfer function and a vortex empirical velocity field model.

[0067] A vortex water dynamic model construction module is configured to establish a vortex water dynamic model according to the marine vortex radar image theoretical model and a SAR radar sampling result.

[0068] A vortex dynamic parameter calculation module is configured to obtain a vortex dynamic parameter according to a vortex physical parameter in a SAR image and in combination with the vortex water dynamic model, wherein the physical parameter comprises a vortex center position, an intake intensity, a viscosity coefficient and a velocity circulation, and the dynamic parameter comprises a vortex maximum rotation speed radius, a maximum rotation angular velocity and a maximum vortex intensity.

[0069] In some other embodiments, the system for inverting a marine vortex dynamic parameter in a SAR image further comprises the following modules.

[0070] A SAR image preprocessing module is configured to preprocess an acquired SAR image.

[0071] It can be understood that the present application discloses a method for retrieving oceanic vortex dynamic parameters in SAR image, and the main steps include: firstly, pre-processing the oceanic vortex SAR image, mainly including radiation calibration, geometric correction and speckle suppression; secondly, based on the sea wave-radar modulation transfer function and the vortex hydrodynamic model, a quantitative retrieval model of the SAR image oceanic vortex parameters is established, and the quantitative relationship between the vortex hydrodynamic parameters and the radar backscattering cross section is obtained; then, based on the constructed quantitative retrieval model of the SAR image oceanic vortex parameters, the sea surface scattering value joint probability density function is established, and the maximum likelihood estimation method is used to construct the SAR image oceanic vortex hydrodynamic parameter retrieval method; finally, the vortex center position, suction intensity, viscosity coefficient, velocity circulation and other dynamic parameters are extracted from the SAR image by using the Newton iteration method, and after the optimal solution of the parameters is obtained through multiple iterations, the vortex maximum rotation speed radius, maximum rotation angular velocity, maximum vortex intensity and other dynamic parameters are calculated based on the vortex hydrodynamic model.

[0072] In order to make the object, technical scheme and advantages of the present application clearer, the present application is further described in detail below combined with specific examples, and the main steps are as follows:

[0073] Step S1: Figure 1 The SAR image oceanic vortex dynamic parameter retrieval method flowchart provided by the present application firstly performs SAR image preprocessing. Control points are selected from the SAR image, and the SAR vortex image used is geometrically corrected by the image to image (image to image) geometric correction mode and the nearest neighbor resampling (Nearest Neighbor) method in ENVI. In order to ensure the resolution of the SAR image, a filtering method is used for speckle noise suppression. Since the main feature of the oceanic vortex in the SAR image is the edge feature, considering the ability to retain edge information and the ability to suppress speckle noise in uniform areas, the Gamma MAP filtering method is used to process the SAR image. Figure 2 The SAR image is smoother and the influence of speckle noise is weaker after Gamma MAP filtering, and the edge features of the vortex in the image are well maintained.

[0074] Step S2: Constructing a theoretical model of the oceanic vortex radar image, and deriving the analytical expression of the oceanic vortex radar image. According to the Bragg resonance scattering theory, the radar echo signal intensity of the ocean is represented by the unit radar backscattering cross section. For a given radar wave number and incident angle, the intensity of the radar echo signal only depends on the two-dimensional wave number spectrum density ψ of the sea surface wave field. The ψ function modulated by the vortex flow field can be expressed as:

[0075]

[0076] where U is the average flow velocity, C g is the group velocity, k = 2π / λ is the wave number of the sea surface wave. Assuming that the high frequency part of the wave spectrum is in equilibrium, the wave spectrum does not change with time and is spatially uniform, the wave number spectrum expression of the capillary-gravity wave band can be obtained as

[0077]

[0078] where m, m3 are coefficients, u * is the friction wind speed, c is the phase velocity of the wave, γ is the viscosity of seawater, ω is the angular frequency of the sea surface wave, and k is the wave number of the sea surface wave. For the high frequency wave band, the ocean can be regarded as deep water. In this case, the tensor calculation gives

[0079]

[0080] where V θ and V r are the tangential and radial velocities of the ocean vortex in the polar coordinate system, and φ is the wave direction.

[0081] Under the assumption of Sullivan, the tangential and radial velocities of the vortex can be expressed as:

[0082]

[0083] Substituting formula (4) into formulas (2) and (3), the wave number spectrum function of the sea surface wave field modulated by the ocean vortex flow field can be obtained as

[0084]

[0085] Substituting formula (5) into the Bragg scattering model, the expression of the radar backscattering cross section per unit area can be obtained. Transforming to the direct coordinate system, the theoretical model of the radar image of the ocean vortex in the rectangular coordinate system is obtained, and the expression is as follows:

[0086]

[0087] where k0 is the wave number of the radar wave, θ is the incident angle, m3 is a dimensionless constant, k = 2π / λ is the wave number of the sea surface wave, ω is the angular frequency of the sea surface wave, a is the suction intensity, u is the viscosity coefficient, G0 is the velocity circulation constant, and σ0 is the sea surface radar backscattering cross section. According to the relationship between the vortex hydrodynamic parameters and the radar backscattering cross section in formula (7), the hydrodynamic parameters of the SAR image ocean vortex can be inversed.

[0088] Step S3: The SAR image is affected by the coherent speckle and the additive thermal noise at the same time, and the noisy scattering intensity y i at each position point x iGamma distribution. After receiving echo for full aperture imaging, any two pixels on SAR image can be considered statistically independent. The profile of vortex at a certain position can be regarded as a signal sequence obtained by SAR sampling. Assuming that each sampling point is independent, the joint probability density function of the sampling sequence can be expressed as

[0089]

[0090] where y i (i = 1, 2, …, n) is the i-th sampling point on the vortex image sampling sequence, and n is the sequence length. According to formula (6), the following is obtained According to the SAR system parameters, the following is obtained.

[0091] According to the maximum likelihood estimation theory, for a certain value of the parameter θ, the probability P(y|θ)dy that the observation vector falls within a small region is considered. The θ corresponding to the maximum value of P(y|θ)dy is taken as the estimated value of the vortex hydrodynamic parameter That is

[0092]

[0093]

[0094]

[0095] where, is the estimated value of the vortex parameter ,

[0096] Let be in the neighborhood of the parameter estimate , the second-order Taylor series expansion of is

[0097]

[0098] Solving the minimum value of formula (11) obtains

[0099]

[0100] If the Hessian matrix is non-singular, then solving the linear equations of formula (12) can obtain the estimated value of the vortex hydrodynamic parameter as

[0101]

[0102] Substitute formula (9) and (10) into formula (12), for the convenience of simplification and unified representation, let (x0, y0, a, υ, Γ0, B) = (a, b, c, d, e, f), respectively, to get Matrix and Matrix is

[0103]

[0104]

[0105] Substitute the calculated matrix and into formula (18) to obtain the estimated value of the vortex hydrodynamic parameter.

[0106] Step S4: Extract the vortex dynamic parameter from the SAR image by using the Newton iteration method, Figure 3 is the flow chart of the iteration algorithm. Since there are estimated parameters on both sides of formula (13), it cannot be further simplified. Therefore, the analytical solution cannot be directly obtained by solving The Newton iteration method is used to approximate the solution, and the recursive expression is:

[0107]

[0108] In the formula, and are the first-order partial derivative, the second-order partial derivative of the objective function and the kth iteration result of the estimated parameter, respectively. After k iterations, the final parameter estimation value is obtained.

[0109] The iteration solving steps are summarized as follows:

[0110] (1) Select as the initial value of the estimated parameter , and give the iteration termination error

[0111] (2) Calculate the first-order derivative vector of the current point If , stop iteration, and take the current point as the estimated point of the parameter Otherwise, continue to execute the following steps;

[0112] (3) Calculate the second-order derivative matrix of the current point and solve the linear equation set to get the iteration increment d k

[0113]

[0114] (4) According to the current point and iteration increment d k Calculate next estimation point

[0115]

[0116] The optimal solution of the vortex dynamic parameters, including the vortex center position (x0, y0), the suction strength a, the viscosity coefficient u, and the velocity circulation constant G0, can be calculated after multiple iterations. The tangential velocity of the vortex can be calculated by substituting the above-mentioned parameters into equation (4).

[0117] The maximum rotation speed radius of the vortex can be obtained by solving the maximum value of equation (4).

[0118]

[0119] According to equation (4), the maximum rotation angular velocity of the vortex is

[0120]

[0121] The calculation expression of the maximum vortex strength is

[0122] K = 2πr0V θmax (31)

[0123] In the formula, K is the maximum vortex strength of the vortex, r0 is the maximum rotation speed radius of the vortex, V θmax is the maximum tangential velocity of the vortex.

[0124]

[0125] Table 1

[0126] Table 1 shows the inversion results of the vortex dynamic parameters in the ERS-2 SAR image. In order to quantitatively compare the estimation accuracy of the present method, 50 groups of vortex cross-section data are extracted from the vortex area of the SAR image, and the dynamic parameters of the vortex are inverted by using the present method, the curve fitting method and the least squares fitting method, respectively. The performance of different methods is evaluated by comparing the relative standard deviation RSD. Compared with the curve fitting method and the least squares fitting method, the relative standard deviation of the vortex parameters inverted by the present method is the smallest, the least squares fitting method is the second, and the relative standard deviation of the parameters of the curve fitting method is the largest. Therefore, it is proved that the SAR image ocean vortex dynamic parameter inversion method proposed in the present application has better effect and stronger robustness.

[0127] Figure 4The estimation results of the radar backscattering values of the vortex profile of the ERS-2 SAR image by different methods are given, wherein the red curve is the estimation result of the method, the green curve is the estimation result of the least square fitting method, and the blue curve is the estimation result of the curve fitting method. It can be seen from the comparison that the overall fitting results of the curve fitting method and the least square fitting method are roughly consistent with the change trend of the actual radar backscattering values, but the fitting effect in the peak value area and the peak valley area of the vortex is weaker than that of the method, the least square fitting method is the second, and the curve fitting method is the worst. In addition, at the starting position of the vortex, the least square fitting method and the curve fitting method cannot well fit the backscattering value change of the vortex, while the method can obtain a good estimation curve, because the method is constructed based on the theoretical model of the ocean vortex radar image, fully considers the imaging characteristics of the vortex in the SAR image, can well predict the bright and dark feature change of the vortex, and thus can obtain an estimation curve that is more consistent with the actual radar backscattering value, so that the best estimation effect can be achieved.

[0128] From the above description, it can be known that the SAR image ocean vortex dynamic parameter extraction method provided by the application is based on the vortex SAR imaging theoretical model to extract the dynamic characteristics of the vortex, and a quantitative relationship between the SAR image radar backscattering coefficient and the vortex physical model is constructed, which is different from the previous digital image processing and machine learning methods that can only extract the position and radius characteristics of the vortex. The vortex dynamic parameters obtained by the method include vortex center position, suction intensity, viscosity coefficient, velocity circulation, maximum rotation speed radius of the vortex, maximum angular velocity, maximum vortex intensity and the like. In addition, compared with the existing curve fitting method and least square fitting method, the radar backscattering value obtained by the method is most consistent with the actual radar backscattering value. This is because the curve fitting method and the least square fitting method are suitable for the case of only additive thermal noise, and thus the estimation accuracy is limited. The method fully considers the multiplicative coherent speckle noise of the SAR image, and the relative standard deviation of the vortex dynamic parameters obtained by 50 times of Monte Carlo repeated experiments is the smallest, the estimation accuracy is higher, and the robustness is stronger.

[0129] From the hardware level, in order to solve the problem of poor vortex dynamic parameter inversion accuracy caused by model noise mismatch in the SAR image vortex parameter inversion based on the curve fitting method, an embodiment of an electronic device for implementing all or part of the contents of the SAR image ocean vortex dynamic parameter inversion method is provided, and the electronic device specifically includes the following contents:

[0130] Figure 5 A schematic block diagram of the system structure of the electronic device 9600 of the embodiment of the application. As shown in Figure 5As shown, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that the Figure 5 is exemplary; other types of structures can also be used to supplement or replace the structure to achieve telecommunication functions or other functions.

[0131] In an embodiment, the ocean vortex dynamic parameter inversion function can be integrated into the central processor. Wherein, the central processor can be configured to control as follows:

[0132] According to the sea wave-radar modulation transfer function and the vortex empirical velocity field model, the ocean vortex radar image theoretical model is obtained;

[0133] According to the ocean vortex radar image theoretical model and the SAR radar sampling result, a vortex hydrodynamic model is established;

[0134] According to the vortex physical parameters in the SAR image, combined with the vortex hydrodynamic model, the vortex dynamic parameters are obtained, wherein the physical parameters include vortex center position, suction intensity, viscosity coefficient, velocity circulation, and the dynamic parameters include vortex maximum rotation speed radius, maximum rotation angular velocity, and maximum vortex intensity.

[0135] From the above description, it can be known that the present application provides an electronic device, which extracts the dynamic characteristics of the vortex based on the vortex SAR imaging theoretical model inversion, and constructs the quantitative relationship between the SAR image radar backscattering coefficient and the vortex physical model, which is different from the previous digital image processing and machine learning methods that can only extract the position and radius characteristics of the vortex. The vortex dynamic parameters obtained by the method include vortex center position, suction intensity, viscosity coefficient, velocity circulation, vortex maximum rotation speed radius, maximum rotation angular velocity, and maximum vortex intensity.

[0136] In another embodiment, the SAR image ocean vortex dynamic parameter inversion system can be configured separately from the central processor 9100, for example, the SAR image ocean vortex dynamic parameter inversion system can be configured as a chip connected with the central processor 9100, and the vortex dynamic parameter inversion function is realized through the control of the central processor.

[0137] As shown in Figure 5 The electronic device 9600 can also include a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily include all the components shown in Figure 5 ; in addition, the electronic device 9600 can also include components not shown in Figure 5 , which can refer to the prior art.

[0138] like Figure 5 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0139] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0140] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0141] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0142] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0143] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0144] Based on different communication technologies, multiple communication modules 9110, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc., can be provided in the same electronic device. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132, thereby enabling typical telecommunication functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling the recording of audio on the local device via the microphone 9132 and enabling the playing of stored audio on the local device via the speaker 9131.

[0145] The embodiment of the present application also provides a computer readable storage medium capable of implementing all steps of the method for inverting marine eddy dynamic parameters in a SAR image in the above-mentioned embodiment. The computer readable storage medium stores a computer program. When the processor executes the computer program, all steps of the method for inverting marine eddy dynamic parameters in a SAR image in the above-mentioned embodiment are implemented, for example, the processor executes the following steps:

[0146] According to the sea wave-radar modulation transfer function and the eddy empirical velocity field model, a marine eddy radar image theoretical model is obtained.

[0147] According to the marine eddy radar image theoretical model and the SAR radar sampling result, an eddy hydrodynamic model is established.

[0148] According to the eddy physical parameters in the SAR image and the eddy hydrodynamic model, eddy dynamic parameters are obtained, wherein the physical parameters include the eddy center position, the suction intensity, the viscosity coefficient and the velocity circulation, and the dynamic parameters include the maximum rotation speed radius of the eddy, the maximum rotation angular velocity and the maximum eddy intensity.

[0149] As known from the above description, the present application provides a computer readable storage medium. Based on the eddy SAR imaging theoretical model, the dynamic characteristics of the eddy are inverted and extracted, and a quantitative relationship between the SAR image radar backscattering coefficient and the eddy physical model is constructed, which is different from the previous digital image processing and machine learning methods that can only extract the position and radius characteristics of the eddy. The eddy dynamic parameters obtained by the method include the eddy center position, the suction intensity, the viscosity coefficient, the velocity circulation, the maximum rotation speed radius of the eddy, the maximum rotation angular velocity and the maximum eddy intensity.

[0150] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the operations described herein. The software implementation can be for example, in the form of a computer program product which can include software agents or objects embedded in a computer readable storage medium. The computer readable storage medium can be a floppy disk, flexible disk, hard disk, USB (universal serial bus), RAM (random-access memory), flash memory, magnetic tape, or any other form of a computer readable storage medium.

[0151] The present application is described in relation to flow charts and / or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It is understood that each block of the flow charts and / or block diagrams, and combinations of blocks in the flow charts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow charts and / or block diagrams block or blocks. Figure 1 one or more functions specified by one or more blocks Figure 1 one or more functions specified by one or more blocks

[0152] These computer program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function specified in the flow charts and / or block diagrams block or blocks. Figure 1 one or more functions specified by one or more blocks Figure 1 one or more functions specified by one or more blocks

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow charts and / or block diagrams block or blocks. Figure 1 one or more functions specified by one or more blocks Figure 1 one or more functions specified by one or more blocks

[0154] The principles and implementation of the present application are described in the detailed description of the application. The above description of the embodiments is only intended to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation and application range can be changed; in summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for retrieving dynamic parameters of oceanic eddies in SAR images, characterized in that, The method comprises the steps of: According to the sea wave-radar modulation transfer function and the vortex empirical velocity field model, the wave number spectrum expression of the sea surface wave field is obtained, and the theoretical model of the marine vortex radar image is obtained based on the wave number spectrum expression of the sea surface wave field; According to the marine vortex radar image theoretical model and the SAR radar sampling result, the vortex dynamic parameter estimation value is obtained; The vortex physical parameters are extracted from the SAR image, and the final vortex dynamic parameter estimation value is obtained through multiple iterations according to the vortex physical parameters and the vortex dynamic parameter estimation value and in combination with the Newton iteration method, wherein the vortex physical parameters include the vortex center position, the suction intensity, the viscosity coefficient and the velocity circulation, and the vortex dynamic parameters include the vortex maximum rotating speed radius, the maximum rotating angular velocity and the maximum vortex intensity.

2. The method according to claim 1, wherein, The marine vortex dynamic parameter inversion method of the SAR image further comprises: The obtained SAR image is preprocessed.

3. The method according to claim 1, wherein, The marine vortex radar image theoretical model is obtained according to the radar parameter and the sea wave-radar modulation transfer function, and comprises the steps of: The vortex parameter initial estimation value is obtained according to the radar parameter and the sea wave-radar modulation transfer function; The vortex parameter initial estimation value is input into the vortex empirical velocity field model to obtain the wave number spectrum expression of the sea surface wave field; The marine vortex radar image theoretical model is obtained according to the wave number spectrum expression of the sea surface wave field and the Bragg scattering theory.

4. The method according to claim 1, wherein, The vortex dynamic parameter estimation value is obtained according to the marine vortex radar image theoretical model and the SAR radar sampling result, and comprises the steps of: At least two signal sequences obtained by the SAR radar sampling points are obtained, wherein the profile of the vortex at any position can be characterized by the signal sequence obtained by the SAR radar sampling; The sea surface scattering value joint probability density function is obtained according to the at least two signal sequences and in combination with the marine vortex radar image theoretical model; The vortex dynamic parameter estimation value is obtained according to the joint probability density function and in combination with the maximum likelihood estimation theory.

5. The method according to claim 2, wherein, The obtained SAR image is preprocessed, and comprises the steps of: The SAR image is geometrically corrected by combining the geometric correction method and the nearest neighbor resampling method; The Gamma MAP filtering method is used to suppress the speckle noise of the SAR image. 6.A system for oceanic eddy kinetic parameter inversion in SAR images, characterized in that, The method comprises the steps of: The marine vortex radar image theoretical model construction module: according to the sea wave-radar modulation transfer function and the vortex empirical velocity field model, the wave number spectrum expression of the sea surface wave field is obtained, and the theoretical model of the marine vortex radar image is obtained based on the wave number spectrum expression of the sea surface wave field; The vortex hydrodynamic model construction module: according to the marine vortex radar image theoretical model and the SAR radar sampling result, the vortex dynamic parameter estimation value is obtained; The vortex dynamic parameter calculation module: the vortex physical parameters are extracted from the SAR image, and the final vortex dynamic parameter estimation value is obtained through multiple iterations according to the vortex physical parameters and the vortex dynamic parameter estimation value and in combination with the Newton iteration method, wherein the vortex physical parameters include the vortex center position, the suction intensity, the viscosity coefficient and the velocity circulation, and the vortex dynamic parameters include the vortex maximum rotating speed radius, the maximum rotating angular velocity and the maximum vortex intensity.

7. The system for retrieving the oceanic vortex dynamic parameters in SAR images according to claim 6, characterized in that, The oceanic vortex dynamic parameter inversion system in the SAR image further comprises: The SAR image preprocessing module pre-processes the acquired SAR image.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the oceanic vortex dynamic parameter inversion method in any one of claims 1 to 5.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the oceanic vortex dynamic parameter inversion method in any one of claims 1 to 5.

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