CN113176572A - Sea surface wave spectrum inversion method and system based on circular scanning SAR - Google Patents
Sea surface wave spectrum inversion method and system based on circular scanning SAR Download PDFInfo
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Abstract
The invention discloses a sea surface wave spectrum inversion method and a system based on a circular scanning SAR, wherein the method comprises the following steps: acquiring circular scanning SAR data and carrying out block processing on the circular scanning SAR data to obtain a plurality of sub-images; normalizing the sub-image to obtain a normalized sub-image; performing cross spectrum calculation and wave spectrum inversion on the normalized subimages to obtain wave spectrum data corresponding to the subimage inversion; and fusing the wave spectrum data inverted by the subimages to obtain final wave spectrum information. The system comprises: the device comprises a data dividing module, a normalization module, a wave spectrum inversion module and a fusion module. By using the method, the error caused by the azimuth nonlinearity of the single-view-direction SAR can be effectively overcome, and the large-range high-time-precision observation of the wave spectrum is realized. The sea surface wave spectrum inversion method and system based on the circular scanning SAR can be widely applied to the field of satellite remote sensing data processing.
Description
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 circular scanning SAR.
Background
Sea waves are a typical type of wave phenomenon that occurs at the surface of the ocean, being one of the most common forms of ocean dynamics. The research on the wave motion has great significance on activities such as ocean engineering operation, ocean development, ocean fishing and breeding, ocean disaster prediction and the like. The information of sea waves is usually expressed in the form of a wave spectrum, which characterizes the concentrated distribution of the energy of the waves in frequency and propagation direction, with a specific correlation between the energy and the frequency of the waves. The wave spectrum can observe factors such as wavelength, wave height, period, propagation direction and the like of sea waves in the sea area, and has the characteristics of intuition and easy reading.
Synthetic Aperture Radar (SAR) is one of the current satellite-borne devices capable of measuring the wave spectrum of the ocean surface, and is the most effective means for wave observation. However, the swath and coverage range of the traditional SAR are limited, so that the SAR is not suitable for the observation requirement of large coverage area of open sea area, and the requirement of high time resolution of large-range wave spectrum measurement cannot be met.
Disclosure of Invention
In order to solve the technical problems, the invention provides a sea surface wave spectrum inversion method and a sea surface wave spectrum inversion system based on a circular scanning SAR, which improve the swath and the coverage range and effectively overcome the error caused by the azimuth nonlinearity of the single-view-direction SAR.
The first technical scheme adopted by the invention is as follows: a sea surface wave spectrum inversion method based on a circular scanning SAR comprises 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 final wave spectrum information.
Further, the normalization processing on the sub-images is specifically non-linear 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, and x represents the image fieldDistance direction, y azimuth direction, t time,<I0>represents I0Average statistic of (x, t), Is(x, t) represents the normalized sub-image, α1、α2Representing the corresponding parameter.
Further, the step of performing cross spectrum calculation and wave spectrum inversion on the normalized sub-images to obtain wave spectrum data corresponding to the sub-image inversion specifically includes:
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.
Further, the preset price function formula is as follows:
J＝∫[|Pql(k',t)-Pobs(k',t)|2+μ|S(k')-Sg(k')|2]·Wp(k')dk'
in the above formula, WpIs a non-negative weight function, Pobs(k', t) is the observation of the cross-spectra, Pql(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, Sg(k') is the initial guess spectrum, k is (k)x,ky) A two-dimensional wavenumber domain.
Further, 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.
Further, the step of fusing the wave spectrum data inverted by the sub-image to obtain final wave spectrum information specifically includes:
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.
Further, the final wave spectrum information s (k) is calculated as follows:
in the above formula, Hn(k)、Tn(k)、Sn(k) Respectively is the resolution point response function, the linear modulation coefficient and the wave spectrum of the nth sub-aperture image.
The second technical scheme adopted by the invention is as follows: a sea surface wave spectrum inversion system based on a circular scanning SAR comprises:
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.
The method and the system have the beneficial effects that: according to the invention, the wave spectrum is inverted through the cross spectrum, so that the problem that the wave direction of the wave spectrum is blurred at 180 degrees is solved; meanwhile, the azimuth displacement transfer function is corrected according to the characteristic of 360-degree rotation of the azimuth angle of the circular scanning SAR, and finally the inversion results of the sub-images in different visual directions are fused, so that the error caused by the azimuth nonlinearity of the single-visual-direction SAR is effectively overcome, and the observation of the large-range high time resolution of the wave spectrum is realized.
Drawings
FIG. 1 is a general flow chart of a sea surface wave spectrum inversion method based on a circular scanning SAR of the present invention;
FIG. 2 is a schematic diagram of a swept-ring SAR in accordance with an embodiment of the present invention;
FIG. 3 is a diagram illustrating the transformation of a global coordinate system to a local coordinate system of an image according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the inversion of the wavespectra according to an embodiment of the present invention.
FIG. 5 is a structural block diagram of a sea surface wave spectrum inversion system based on a circular scanning SAR.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
The annular scanning SAR is a new system curve radar which flies and scans in an annular mode simultaneously, an antenna quickly scans at a constant speed by taking the vertical ground as an axis while a radar carrier flies forwards at a speed, an approximately annular imaging area is formed on the ground, an ultra-wide observation swath is formed by overlapping a plurality of annular areas, and the schematic diagram of the annular scanning SAR is shown in figure 2.
Referring to fig. 1, the invention provides a sea surface wave spectrum inversion method based on a circular scanning SAR, which comprises 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.
Further, as a preferred embodiment of the method, the step of obtaining the circular scanning SAR data and performing block processing on the circular scanning SAR data to obtain a plurality of sub-images specifically includes:
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.
Specifically, the transfer function from sea waves to cross spectra has a large relationship with the viewing angle, and data of different viewing angles cannot be processed uniformly. Therefore, the annular scanning SAR image needs to be subjected to block processing, and as the viewing angle of each sub-image is small and the transfer functions in the sub-images are approximately consistent, the sub-images can be uniformly processed.
For the circular scanning SAR, the number of sub-image pixels and the direction of the coordinate axis change with the change of the azimuth angle, and in order to perform uniform processing on different azimuth angles, the inversion results of each angle need to be interpolated to the same resolution and rotated to the uniform azimuth angle. Since the azimuth is approximately along the beam scan direction, the local image is converted from the global coordinate system to a local slant-azimuth coordinate system for ease of processing, as shown in fig. 3, the converted azimuth is directed in a direction perpendicular to the radar look direction.
Further, as a preferred embodiment of the method, the normalization processing on the sub-images is specifically a nonlinear normalization processing, and a formula is as follows:
in the above formula, I0(x, t) represents the original sub-imageWhere x is (x, y) representing the two-dimensional coordinates of the image field, x represents the range direction, y the azimuth direction, t represents time,<I0>represents I0Average statistic of (x, t), Is(x, t) represents the normalized sub-image, α1、α2Representing the corresponding parameter.
Specifically, as the radar signals conform to exponential distribution, the nonlinear modulation more conforms to the real situation of sea level radar signals, and the gray level distribution of the image after nonlinear normalization is obviously more symmetrical.
As a further preferred embodiment of the method, referring to fig. 4, the step of performing cross spectrum calculation and wave spectrum inversion on the normalized sub-images to obtain wave spectrum data corresponding to the sub-image inversion specifically includes:
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 on the primarily guessed wave spectrum to obtain a primarily guessed cross spectrum, namely a simulated cross spectrum;
specifically, the cross-spectrum function can be expressed as:
P(k,t)＝∫dxe-ikxG(x,t,k)-δ(k)
wherein k is (k)x,ky) Two-dimensional wavenumber domain, G (x, t, k) is:
where ρ isab(x,t)、μab(x, t) is a correlation coefficient, ab represents x, y and I, and x, y and I respectively represent xix,ξyAnd I0<I0>-1The formula is as follows:
μab(x,t)＝ρab(x,t)-ρab(O,0)
wherein S (k) is a wave spectrum, O is an origin, and N isab(k, T) is Ta(k) And Tb(k) The transfer function of (c):
where ω t represents the phase shift of the wave number component with frequency over the time interval t,
g is the acceleration of gravity. T isa(k) And Tb(k) Representing the displacement in the direction of the distance Tx(k) Backscatter TI(k) Transfer function and azimuthal displacement transfer function Ty(k)。Tx(k) And TI(k) Expressed as:
where θ is an incident angle, β is a wind growth rate of the bragg wave, and ω is an antenna rotation angular velocity.
For a front side view, the azimuthal displacement transfer function Ty(k) Comprises the following steps:
wherein R is the slant distance, and v is the flying speed of the radar carrier.
However, for the ring-scan SAR, the azimuthal displacement is not consistent at different azimuthal angles, and the azimuthal displacement transfer function of the ring-scan SAR needs to be improved as follows:
wherein,
is the azimuth angle.
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.
Specifically, for the complex problem of the wave spectrum, the steepest gradient method is slow in iteration, sensitive to step length selection and prone to falling into a local minimum value, so that the method is improved to a quasi-Newton method, the method is fast in iteration, and an optimal solution can be obtained in one step and two steps for an ideal quadratic price function. Therefore, here, for two gradient directions, an optimal step size is calculated, where the first step size is the local gradient and the second step size is the difference between the wave spectrum obtained from the local iteration and the wave spectrum obtained from the last iteration:
then at the nth iteration, the derivatives of the cross spectrum for these two directions are:
wherein P isn(k) And (4) obtaining a cross spectrum for the iteration of the nth step. The wave spectrum after iteration is:
Sn+1(k)＝Sn(k)+α1ΔS1(k)+α2ΔS2(k)
the optimal step size can be obtained by minimizing the following equation:
solving the following equation for alpha by solving for an approximate minimum1And alpha2：
After simplification can be expressed as:
A·β＝B
wherein β ═ β1 β2]T，
B＝[B1 B2]T。
The four elements of matrix a are: a. the11＝∫[|dP1 n(k)|2+μ|ΔS1(k)|2]W(k)dk，
The two elements of matrix B are:
such step size can be solved by the following equation:
β＝A-1B
further as a preferred embodiment of the method, the preset price function formula is as follows:
J＝∫[|Pql(k',t)-Pobs(k',t)|2+μ|S(k')-Sg(k')|2]·Wp(k')dk'
in the above formula, WpIs a non-negative weight function, Pobs(k', t) is the observation of the cross-spectra, Pql(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, Sg(k') is the initial guess spectrum.
Specifically, because the SAR image has a severe nonlinear effect on the wave spectrum in the high-frequency region, and in addition, the circularly scanned SAR tends to have a lower resolution, and the accuracy of inversion of the high-frequency wave region is limited, the inverted cost function also needs to refer to the initially guessed wave spectrum obtained according to the wave model, so that the price function can be changed into:
J＝∫[|Pql(k',t)-Pobs(k',t)|2+μ|S(k')-Sg(k')|2]·Wp(k')dk'
wherein mu is a weighting scale coefficient, S (k') is a wave spectrum obtained by inversion, Sg(k') is the initial guess spectrum.
Further as a preferred embodiment of the method, the expression of the modified wave spectrum is as follows:
Sn+1(k)＝Sn(k)+α1ΔS1(k)+α2ΔS2(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.
Further, as a preferred embodiment of the method, the step of fusing the wave spectrum data inverted by the sub-image to obtain final wave spectrum information specifically includes:
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.
Specifically, after the inversion of the wave spectrum is performed on the sub-images in different azimuth directions, the inversion results need to be fused to obtain the final wave spectrum. Because the transfer function of the ring-scan SAR imaging is different in different azimuth directions and the resolutions of the different azimuth directions are different, the sensitivity of the SAR images in different azimuth directions to wave spectrums in different directions is different. Therefore, the fusion strategy weights the wave numbers by different sensitivities, and the direction with high sensitivity is given higher weight.
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]
wherein H (k) is a given point response function
T (k) is the linear transfer function of the wave spectrum:
T(k)≈Trb+Tvb+Tt+Th
wherein T isrb、Tvb、Tt、ThRespectively distance convergence, velocity convergence, tilt and hydrodynamic modulation.
Further as a preferred embodiment of the method, the calculation formula of the final wave spectrum information s (k) is as follows:
in the above formula, Hn(k)、Tn(k)、Sn(k) Respectively is the resolution point response function, the linear modulation coefficient and the wave spectrum of the nth sub-aperture image.
As shown in fig. 5, a sea surface wave spectrum inversion system based on circular scanning SAR includes:
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.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present system embodiment are also the same as those achieved by the above method embodiments.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
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), Is(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＝∫[|Pql(k',t)-Pobs(k',t)|2+μ|S(k')-Sg(k')|2]·Wp(k')dk'
in the above formula, WpIs a non-negative weight function, Pobs(k', t) is the observation of the cross-spectra, Pql(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, Sg(k') is the initial guess spectrum, k is (k)x,ky) 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, Hn(k)、Tn(k)、Sn(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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| CN116258787B (en) * | 2023-05-15 | 2023-10-17 | 自然资源部第一海洋研究所 | Wave direction spectrum algorithm suitable for wave image |
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