CN113176572A - Sea surface wave spectrum inversion method and system based on circular scanning SAR - Google Patents

Abstract

The invention discloses a sea surface wave spectrum inversion method and system based on ring-scanning SAR. The method includes: acquiring ring-scanning SAR data and performing block processing on the ring-scanning SAR data to obtain a plurality of sub-images; normalizing the sub-images Normalize the sub-image to obtain the normalized sub-image; perform cross-spectrum calculation and wave spectrum inversion on the normalized sub-image to obtain the wave spectrum data of the corresponding sub-image inversion; invert the wave spectrum of the sub-image The data are fused to obtain the final wave spectrum information. The system includes: data division module, normalization module, wave spectrum inversion module and fusion module. By using the present invention, the error caused by the nonlinearity of the azimuth direction of the one-way SAR can be effectively overcome, and the large-scale and high-time-precision observation of the wave spectrum can be realized. As a sea surface wave spectrum inversion method and system based on ring scanning SAR, the present invention can be widely used in the field of satellite remote sensing data processing.

Description

A method and system for sea surface wave spectrum inversion based on ring scan SAR

technical field

The invention relates to the field of satellite remote sensing data processing, in particular to a sea surface wave spectrum inversion method and system based on a ring scan SAR.

Background technique

Ocean waves are a type of typical wave phenomenon that occurs on the ocean surface and are the most common form of ocean dynamics. The study of wave motion is of great significance to marine engineering operations, marine development, marine fishing and aquaculture, marine disaster prediction and other activities. The information of ocean waves is usually expressed in the form of wave spectrum. There is a specific correlation between the energy and frequency of ocean waves. The wave spectrum represents the concentrated distribution of the energy of ocean waves in frequency and propagation direction. The wave spectrum can observe the wavelength, wave height, period and propagation direction of the waves in the sea area, which is intuitive and easy to read.

Synthetic Aperture Radar (SAR) is currently one of the spaceborne devices that can measure the wave spectrum on the ocean surface, and it is the most effective means for ocean wave observation. However, the swath and coverage of traditional SAR are limited, so they are not suitable for the observation requirements of large coverage areas in the open sea, and cannot meet the high temporal resolution requirements of large-scale wave spectrum measurements.

SUMMARY OF THE INVENTION

In order to solve the above technical problems, the present invention provides a sea surface wave spectrum inversion method and system based on ring scan SAR, which can improve the swath and coverage, and effectively overcome the error caused by the azimuth nonlinearity of single-looking SAR.

The first technical solution adopted by the present invention is: a sea surface wave spectrum inversion method based on ring scan SAR, comprising the following steps:

S1. Acquire ring-scan SAR data and perform block processing on the ring-scan SAR data to obtain multiple sub-images;

S2, normalize the sub-image to obtain a normalized sub-image;

S3, performing cross-spectrum calculation and wave spectrum inversion on the normalized sub-image to obtain wave spectrum data inversion of the corresponding sub-image;

S4, fuse the wave spectrum data inverted from the sub-image to obtain final wave spectrum information.

Further, the normalization processing on the sub-image is specifically nonlinear normalization processing, and the formula is as follows:

In the above formula, I ₀ (x, t) represents the original sub-image, x=(x, y) represents the two-dimensional coordinates of the image domain, x represents the distance direction, y is the azimuth direction, t represents time, and <I ₀ > represents The average statistic of I ₀ (x, _t ), Is (x, t) represents the normalized sub-image, and α ₁ and α ₂ represent the corresponding parameters.

Further, the step of performing cross-spectrum calculation and wave spectrum inversion on the normalized sub-image to obtain the wave spectrum data inversion of the corresponding sub-image specifically includes:

S31. Process the normalized image based on the cross-spectrum forward model to obtain the observed cross-spectrum;

S32. Obtain wind speed and direction information and generate a preliminary guessed wave spectrum;

S33. Perform forward calculation through the initial guessed wave spectrum to obtain the initial guessed cross-spectrum, that is, the simulated cross-spectrum;

S34. Based on the preset price function, observing the cross-spectrum and guessing the cross-spectrum, determine whether the convergence condition is reached;

S35, judging whether the convergence condition is reached, calculating the iterative step size and the directional derivative, and correcting the wave spectrum to obtain the corrected wave spectrum;

S36. Return to step S33, iterate until the convergence condition is reached, and obtain wave spectrum data corresponding to the sub-image inversion.

Further, the preset price function formula is as follows:

J=∫[|P _ql (k',t)-P _obs (k',t)| ² +μ|S(k')-S _g (k')| ² ]·W _p (k')dk '

In the above formula, W _p is a non-negative weight function, P _obs (k', t) is the observed cross-spectrum, P _ql (k', t) is the initial guessed cross-spectrum, k' is the initial wavenumber domain, and μ is a Weighted scale coefficient, S(k') is the wave spectrum obtained by inversion, S _g (k') is the initial guess spectrum, and k is the (k _x , k _y ) two-dimensional wavenumber domain.

Further, the expression of the modified wave spectrum is as follows:

Sn ⁺¹ (k)= ^Sn (k)+β ₁ ΔS ₁ (k)+β ₂ ΔS ₂ (k)

In the above formula, ^Sn (k) is the wave spectrum obtained at the nth iteration, Sn ⁺¹ (k) is the wave spectrum obtained at the n+1th iteration, ΔS ₁ (k) and ΔS ₂ (k) represent the step long, β ₁ and β ₂ represent correction coefficients.

Further, the step of fusing the wave spectrum data inverted from the sub-images to obtain the final wave spectrum information specifically includes:

S41, calculate the point response function, the wave spectrum linear function and the wave spectrum corresponding to the sub-image;

S42. Perform weighted fusion of the corresponding point response function, the wave spectrum linear function and the wave spectrum to obtain a multi-angle fusion wave spectrum.

Further, the calculation formula of the final wave spectrum information S(k) is as follows:

In the above formula, H _n (k), T _n (k), and _Sn (k) are the resolution point response function, linear modulation coefficient and wave spectrum of the nth sub-aperture image, respectively.

The second technical solution adopted by the present invention is: a sea surface wave spectrum inversion system based on ring scan SAR, comprising:

The data division module is used to obtain the ring scan SAR data and perform block processing on the ring scan SAR data to obtain multiple sub-images;

The normalization module is used to normalize the sub-image to obtain the normalized sub-image;

The wave spectrum inversion module is used to perform cross-spectrum calculation and wave spectrum inversion on the normalized sub-images, and obtain the wave spectrum data of the corresponding sub-image inversion;

The fusion module fuses the wave spectrum data inverted from the sub-images to obtain the final wave spectrum information.

The beneficial effects of the method and system of the invention are as follows: the invention inverts the wave spectrum through the cross spectrum, which overcomes the problem of 180° ambiguity of the wave spectrum; Finally, the inversion results of sub-images in different viewing directions are fused, which effectively overcomes the error caused by the azimuth nonlinearity of single-looking SAR, and realizes the observation of the wave spectrum in a wide range with high time resolution.

Description of drawings

Fig. 1 is the general flow chart of a kind of sea surface wave spectrum inversion method based on ring scan SAR of the present invention;

2 is a schematic diagram of a ring scan SAR according to a specific embodiment of the present invention;

Fig. 3 is the conversion schematic diagram of the image global coordinate system to the local coordinate system of the specific embodiment of the present invention;

FIG. 4 is a schematic diagram of wave spectrum inversion according to a specific embodiment of the present invention.

FIG. 5 is a structural block diagram of a sea surface wave spectrum inversion system based on ring scan SAR according to the present invention.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The numbers of the steps in the following embodiments are only set for the convenience of description, and the sequence between the steps is not limited in any way, and the execution sequence of each step in the embodiments can be adapted according to the understanding of those skilled in the art Sexual adjustment.

Ring-scan SAR is a new system of curved radar that scans while flying. While the radar carrier is flying forward at a speed, the antenna quickly scans at a constant speed with the vertical ground as the axis, forming an approximate annular imaging area on the ground. Multiple annular regions are superimposed to form an ultra-wide observation swath. The schematic diagram of the ring scan SAR is shown in Figure 2.

1, the present invention provides a sea surface wave spectrum inversion method based on ring scan SAR, the method includes the following steps:

S1. Acquire ring-scan SAR data and perform block processing on the ring-scan SAR data to obtain multiple sub-images;

S2, normalize the sub-image to obtain a normalized sub-image;

S3, performing cross-spectrum calculation and wave spectrum inversion on the normalized sub-image to obtain wave spectrum data inversion of the corresponding sub-image;

S4, fuse the wave spectrum data inverted from the sub-image to obtain the final wave spectrum.

Further as a preferred embodiment of the method, the step of obtaining the ring-scan SAR data and performing block processing on the ring-scan SAR data to obtain a plurality of sub-images specifically includes:

S11. Obtain ring scan SAR data;

S12. Divide the ring-scan SAR data into a plurality of sub-images according to the azimuth angle, and convert the sub-images from the global coordinate system to the local slant range-azimuth coordinate system.

Specifically, the transfer function from the waves to the cross-spectrum has a great relationship with the viewing angle, and the data of different viewing angles cannot be processed uniformly. Therefore, the ring scan SAR image needs to be processed in blocks. Since the viewing angle of each sub-image is small and the transfer functions in the sub-images are approximately the same, the sub-images can be processed uniformly.

For ring scan SAR, the number of sub-image pixels and the direction of the coordinate axis will change with the azimuth angle. In order to uniformly process different azimuth angles, the inversion results of each angle need to be interpolated to the same resolution. and rotate to a uniform azimuth. Since the azimuth direction is approximately along the beam scanning direction, for the convenience of processing, the local image is converted from the global coordinate system to the local slant range-azimuth coordinate system, as shown in Figure 3, the converted azimuth direction is perpendicular to the radar viewing direction direction.

Further as a preferred embodiment of the method, the normalization processing on the sub-image is specifically nonlinear normalization processing, and the formula is as follows:

In the above formula, I ₀ (x, t) represents the original sub-image, x=(x, y) represents the two-dimensional coordinates of the image domain, x represents the distance direction, y is the azimuth direction, t represents time, and <I ₀ > represents The average statistics of I ₀ (x, _t ), Is (x, t) represents the normalized sub-image, and α ₁ and α ₂ represent corresponding parameters.

Specifically, since the radar signal conforms to the exponential distribution, the nonlinear modulation is more in line with the real situation of the sea surface radar signal, and the gray distribution of the image after nonlinear normalization is obviously more symmetrical.

Further as a preferred embodiment of the method, referring to FIG. 4 , the step of performing cross-spectrum calculation and wave spectrum inversion on the normalized sub-image to obtain the wave spectrum data corresponding to the inversion of the sub-image specifically includes:

S31. Process the normalized image based on the cross-spectrum forward model to obtain the observed cross-spectrum;

S32. Obtain wind speed and direction information and generate a preliminary guessed wave spectrum;

S33. Perform forward calculation on the initial guessed wave spectrum to obtain the initial guessed cross-spectrum, that is, the simulated cross-spectrum;

Specifically, the cross-spectral function can be expressed as:

P(k,t)=∫dxe- ^ikx G(x,t,k)-δ(k)

Among them, k is the (k _x , k _y ) two-dimensional wavenumber field, and G(x, t, k) is:

Among them, ρ _ab (x, t), μ _ab (x, t) are correlation coefficients, ab represents x, y and I, x, y and I represent ξ _x , ξ _y and I ₀ <I ₀ > ^-1 respectively , the formula is:

μ _ab (x,t)=ρ _ab (x,t)-ρ _ab (0,0)

where S(k) is the wave spectrum, O is the origin, and _Nab (k,t) is the transfer function of T _a (k) and T _b (k):

g为重力加速度。T_a(k)和T_b(k)代表距离向位移T_x(k)，后向散射T_I(k)传递函数和方位向位移传递函数T_y(k)。T_x(k)和T_I(k)表示为：where ωt represents the phase shift of the wavenumber component with frequency during the time interval t,

g is the acceleration of gravity. T _a (k) and T _b (k) represent the range displacement T _x (k), the backscatter T _I (k) transfer function and the azimuth displacement transfer function _Ty (k). T _x (k) and T _I (k) are expressed as:

where θ is the angle of incidence, β is the wind growth rate of Bragg waves, and ω is the angular velocity of the antenna rotation.

For the side view, the azimuth displacement transfer function _Ty (k) is:

Among them, R is the slant range distance, and v is the flight speed of the radar carrier.

However, for the ring-scan SAR, the azimuth displacement is not consistent at different azimuth angles. The azimuth displacement transfer function of the ring-scan SAR needs to be improved as follows:

为方位角。in,

is the azimuth angle.

S34. Based on the preset price function, observing the cross-spectrum and guessing the cross-spectrum, determine whether the convergence condition is reached;

S35, judging whether the convergence condition is reached, calculating the iterative step size and the directional derivative, and correcting the wave spectrum to obtain the corrected wave spectrum;

S36. Return to step S33, iterate until the convergence condition is reached, and obtain wave spectrum data corresponding to the sub-image inversion.

Specifically, for the complex problem of wave spectrum, since the steepest gradient method has slow iteration and is more sensitive to the selection of step size, it is easy to fall into a local minimum, so it is improved to a quasi-Newton method, which iterates quickly and is relatively An ideal quadratic price function often leads to an optimal solution in one or two steps. Therefore, here, for the two gradient directions, the optimal step size is calculated, where the first step size is the local gradient, and the second step size is the difference between the local iteration and the wave spectrum obtained by the previous iteration:

Then at the n-th iteration, the derivatives of the cross-spectrum to these two directions are:

where P ⁿ (k) is the cross spectrum obtained by the nth iteration. The iterative wave spectrum is:

Sn ⁺¹ (k)=S ⁿ (k)+α ₁ ΔS ₁ (k)+α ₂ ΔS ₂ (k)

The optimal step size can be obtained by minimizing:

Solve the following equations to find α ₁ and α ₂ by finding an approximate minimum:

After simplification, it can be expressed as:

A·β=B

B＝[B₁ B₂]^T。where β=[β ₁ β ₂ ] ^T ,

B=[B ₁ B ₂ ] ^T .

The four elements of matrix A are: A ₁₁ =∫[|dP ₁ ⁿ (k)| ² +μ|ΔS ₁ (k)| ² ]W(k)dk,

The two elements of matrix B are:

This step size can be solved by the following equation:

β=A ^-1 B

Further as a preferred embodiment of this method, the preset price function formula is as follows:

J=∫[|P _ql (k',t)-P _obs (k',t)| ² +μ|S(k')-S _g (k')| ² ]·W _p (k')dk '

In the above formula, W _p is a non-negative weight function, P _obs (k', t) is the observed cross-spectrum, P _ql (k', t) is the initial guessed cross-spectrum, k' is the initial wavenumber domain, and μ is a Weighted scale factor, S(k') is the wave spectrum obtained by inversion, and S _g (k') is the initial guess spectrum.

Specifically, due to the serious nonlinear effect of SAR images in the wave spectrum in the high-frequency region, and the low resolution of the ring-scan SAR, the inversion accuracy of the high-frequency wind and wave region is limited, so the cost function of the inversion also needs reference. The initial guess of the wave spectrum obtained by the wind wave model, so the price function can be changed to:

J=∫[|P _ql (k',t)-P _obs (k',t)| ² +μ|S(k')-S _g (k')| ² ]·W _p (k')dk '

Among them, μ is a weighted proportional coefficient, S(k') is the wave spectrum obtained by inversion, and _Sg (k') is the initial guess spectrum.

Further as a preferred embodiment of this method, the expression of the modified wave spectrum is as follows:

Sn ⁺¹ (k)=S ⁿ (k)+α ₁ ΔS ₁ (k)+α ₂ ΔS ₂ (k)

In the above formula, ^Sn (k) is the wave spectrum obtained at the nth iteration, Sn ⁺¹ (k) is the wave spectrum obtained at the n+1th iteration, ΔS ₁ (k) and ΔS ₂ (k) represent the step long, β ₁ and β ₂ represent correction coefficients.

Further as a preferred embodiment of this method, the step of fusing the wave spectrum data inverted from the sub-images to obtain the final wave spectrum information specifically includes:

S41, calculate the point response function, the wave spectrum linear function and the wave spectrum corresponding to the sub-image;

S42. Perform weighted fusion of the corresponding point response function, the wave spectrum linear function and the wave spectrum to obtain a multi-angle fusion wave spectrum.

Specifically, after the wave spectrum inversion is performed on sub-images in different azimuths, the inversion results need to be fused to obtain the final wave spectrum. Because the transfer functions of ring-scan SAR imaging are different in different azimuths, and the resolutions in different azimuths are also different, SAR images in different azimuths have different sensitivities to wave spectra in different directions. Therefore, the fusion strategy weights the wavenumber by different sensitivities, and the azimuth with high sensitivity is given a higher weighting value.

Using a linear approximation, the relationship between the wave spectrum and the image spectrum can be expressed as:

P(k)≈H(k)[S(k)|T(k)| ² +S(-k)|T(-k)| ² ]

T(k)为波浪谱的线性传递函数：where H(k) is the given point response function

T(k) is the linear transfer function of the wave spectrum:

T(k)≈T _rb +T _vb +T _t +T _h

where T _rb , T _vb , T _t , and _Th are distance focusing, velocity focusing, tilt and hydrodynamic modulation, respectively.

Further as a preferred embodiment of this method, the calculation formula of the final wave spectrum information S(k) is as follows:

In the above formula, H _n (k), T _n (k), and _Sn (k) are the resolution point response function, linear modulation coefficient and wave spectrum of the nth sub-aperture image, respectively.

As shown in Figure 5, a sea surface wave spectrum inversion system based on ring scan SAR includes:

The data division module is used to obtain the ring scan SAR data and perform block processing on the ring scan SAR data to obtain multiple sub-images;

The normalization module is used to normalize the sub-image to obtain the normalized sub-image;

The wave spectrum inversion module is used to perform cross-spectrum calculation and wave spectrum inversion on the normalized sub-images, and obtain the wave spectrum data of the corresponding sub-image inversion;

The fusion module fuses the wave spectrum data inverted from the sub-images to obtain the final wave spectrum information.

The contents in the above method embodiments are all applicable to the present system embodiments, the specific functions implemented by the present system embodiments are the same as the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

The preferred implementation of the present invention has been specifically described above, but the present invention is not limited to the described embodiments, and those skilled in the art can make various equivalent deformations or replacements without departing from the spirit of the present invention, These equivalent modifications or substitutions are all included within the scope defined by the claims of the present application.

Claims (9)

1. A sea surface wave spectrum inversion method based on a circular scanning SAR is characterized by comprising the following steps:

s1, acquiring circular scanning SAR data and carrying out blocking processing on the circular scanning SAR data to obtain a plurality of sub-images;

s2, normalizing the sub-images to obtain normalized sub-images;

s3, performing cross spectrum calculation and wave spectrum inversion on the normalized sub-images to obtain wave spectrum data corresponding to the sub-image inversion;

and S4, fusing the wave spectrum data inverted by the sub-images to obtain a final wave spectrum.

2. The method for inverting the sea surface wave spectrum based on the circular scanning SAR according to claim 1, wherein the step of obtaining the circular scanning SAR data and processing the circular scanning SAR data in blocks to obtain a plurality of sub-images specifically comprises:

s11, acquiring circular scanning SAR data;

s12, dividing the annular scanning SAR data into a plurality of sub-images according to azimuth angles, and converting the sub-images from a global coordinate system to a local slant range-azimuth coordinate system.

3. The sea surface wave spectrum inversion method based on the circular scanning SAR as claimed in claim 2, characterized in that the normalization processing on the subimages is specifically a nonlinear normalization processing, and the formula is as follows:

in the above formula, I₀(x, t) represents the original sub-image, x ═ x, y represents the two-dimensional coordinates of the image field, x represents the distance direction, y represents the azimuth direction, t represents the time,<I₀>represents I₀Average statistic of (x, t), I_s(x, t) represents the normalized sub-image, α₁、α₂Representing the corresponding parameter.

4. The method for inverting the sea surface wave spectrum based on the circular scanning SAR according to claim 3, wherein the step of performing cross spectrum calculation and wave spectrum inversion on the normalized subimages to obtain wave spectrum data corresponding to the subimage inversion specifically comprises:

s31, processing the normalized image based on the cross spectrum forward modeling model to obtain an observation cross spectrum;

s32, acquiring wind speed and wind direction information and generating a primary guess wave spectrum;

s33, carrying out forward calculation through the primary guess wave spectrum to obtain a primary guess cross spectrum, namely a simulation cross spectrum;

s34, based on the preset price function, the observation cross spectrum and the initial guess cross spectrum, judging whether the convergence condition is reached;

s35, judging whether a convergence condition is reached, calculating an iteration step length and a direction derivative, and correcting the wave spectrum to obtain a corrected wave spectrum;

and S36, returning to the step S33, and iterating until a convergence condition is reached to obtain wave spectrum data inverted by the corresponding sub-image.

5. The method of claim 4, wherein the preset price function formula is as follows:

J＝∫[|P_ql(k',t)-P_obs(k',t)|²+μ|S(k')-S_g(k')|²]·W_p(k')dk'

in the above formula, W_pIs a non-negative weight function, P_obs(k', t) is the observation of the cross-spectra, P_ql(k ', t) is the initial guess cross spectrum, k ' is the initial wavenumber domain, mu is a weighting scale coefficient, S (k ') is the wave spectrum obtained by inversion, S_g(k') is the initial guess spectrum, k is (k)_x,k_y) A two-dimensional wavenumber domain.

6. The method for inverting the sea surface wave spectrum based on the circular scanning SAR according to claim 5, wherein the expression of the modified wave spectrum is as follows:

Sⁿ⁺¹(k)＝Sⁿ(k)+β₁ΔS₁(k)+β₂ΔS₂(k)

in the above formula, Sⁿ(k) For the wave spectrum, S, obtained for the nth iterationⁿ⁺¹(k) Wave spectrum, Δ S, for the n +1 th iteration₁(k) And Δ S₂(k) Represents the step size, β₁And beta₂Representing the correction factor.

7. The method for inverting the sea surface wave spectrum based on the circular scanning SAR according to claim 5, wherein the step of fusing the wave spectrum data inverted by the sub-image to obtain a final wave spectrum specifically comprises:

s41, calculating a point response function, a wave spectral linear function and a wave spectrum corresponding to the sub-images;

and S42, carrying out weighted fusion on the corresponding point response function, the wave spectral linear function and the wave spectrum to obtain the wave spectrum after multi-angle fusion.

8. The method for sea surface wave spectrum inversion based on ring-scan SAR as claimed in claim 7, wherein the final wave spectrum information S (k) is calculated by the following formula:

in the above formula, H_n(k)、T_n(k)、S_n(k) Respectively is the resolution point response function, the linear modulation coefficient and the wave spectrum of the nth sub-aperture image.

9. A sea surface wave spectrum inversion system based on a circular scanning SAR is characterized by comprising:

the data dividing module is used for acquiring the circular scanning SAR data and carrying out block processing on the circular scanning SAR data to obtain a plurality of sub-images;

the normalization module is used for performing normalization processing on the sub-image to obtain a normalized sub-image;

the wave spectrum inversion module is used for performing cross spectrum calculation and wave spectrum inversion on the normalized sub-images to obtain wave spectrum data corresponding to the sub-image inversion;

and the fusion module is used for fusing the wave spectrum data inverted by the subimages to obtain the final wave spectrum information.

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