阅读说明：本技术 一种利用合成孔径雷达图像反演海面风速的方法及系统 (Method and system for inverting sea surface wind speed by utilizing synthetic aperture radar image ) 是由 郑罡 周立章 王焱 陈鹏 于 2020-07-01 设计创作，主要内容包括：本发明公开了一种利用合成孔径雷达图像反演海面风速的方法及系统,包括步骤：S1、获得含有风条纹信息的合成孔径雷达图像,对所述图像进行辐射校正,将强度信息转化为归一化后向散射系数；S2、对辐射校正后的图像进行重新标定；S3、将所述重新标定后的图像转化为灰度图像；S4、计算所述灰度图像的灰度共生矩阵；S5、计算所述灰度共生矩阵的特征值；S6、基于所述灰度共生矩阵的特征值、及灰度共生矩阵的特征值与风速间的关系,计算海面风速。本发明对辐射校正后的图像进行重新标定,以避免当SAR图像辐射定标的加性因子不准时反演效果差的问题,提高风速计算的准确性。(The invention discloses a method and a system for inverting sea surface wind speed by utilizing synthetic aperture radar images, which comprises the following steps: s1, obtaining a synthetic aperture radar image containing wind stripe information, performing radiation correction on the image, and converting intensity information into a normalized backscattering coefficient; s2, recalibrating the image after radiation correction; s3, converting the image after the recalibration into a gray image; s4, calculating a gray level co-occurrence matrix of the gray level image; s5, calculating a characteristic value of the gray level co-occurrence matrix; and S6, calculating the sea surface wind speed based on the characteristic value of the gray level co-occurrence matrix and the relation between the characteristic value of the gray level co-occurrence matrix and the wind speed. The method and the device perform recalibration on the image after radiation correction so as to avoid the problem of poor inversion effect when the additive factor of the SAR image radiometric calibration is not accurate and improve the accuracy of wind speed calculation.)

1. A method for inverting sea surface wind speed by utilizing synthetic aperture radar images is characterized by comprising the following steps:

s1, obtaining a synthetic aperture radar image containing wind stripe information, performing radiation correction on the image, and converting intensity information into a normalized backscattering coefficient;

s2, recalibrating the image after radiation correction;

s3, converting the image after the recalibration into a gray image;

s4, calculating a gray level co-occurrence matrix of the gray level image;

s5, calculating a characteristic value of the gray level co-occurrence matrix;

and S6, calculating the sea surface wind speed based on the characteristic value of the gray level co-occurrence matrix and the relation between the characteristic value of the gray level co-occurrence matrix and the wind speed.

2. The method of inverting sea surface wind speed of claim 1, wherein the recalibration formula is as follows:

R＝σ₀/S(θ)

wherein σ₀For normalization of backscattering coefficient, S (theta) is a calculated value of the backscattering model CMOD5.N at a wind speed of 10m/S and an incident angle theta at 45 DEG in the wind direction, and theta is the calculated sigma₀And R is the pixel value after the re-calibration corresponding to the incident angle at the pixel.

3. The method of inverting sea surface wind speed of claim 1, wherein the normalized backscattering coefficient is specifically:

I＝10×lg[(X+A₁)/A₂]+10×lg[sin(θ)]

wherein I is the normalized backscattering coefficient, X is the intensity, A₁Is an offset amount, A₂For gain, θ is the angle of incidence.

4. The method for inverting sea surface wind speed according to claim 1, wherein the step S4 is specifically:

finding 4 GLCMs whose relative positions are integers and closest to the target position to be found, and respectively marking as G₁₁、G₁₂、G₂₁、G₂₂The corresponding satisfied relative positions are (floor (d · cos Φ), floor (d · sin Φ)), (ceiling (d · cos Φ), floor (d · sin Φ)), (floor (d · cos Φ), ceiling (d · sin Φ)), (ceiling (d · cos Φ), ceiling (d · sin Φ)), where d is the step size, Φ is the angle, floor represents the rounding-down, ceiling represents the rounding-up, specifically: the GLCM with the relative position (d · cos Φ, d · sin Φ) is obtained by bilinear interpolation, and the formula is as follows:

G₁₁(m，n；d，Ф)＝G₁₁(m,n；floor(d·cosφ)，floor(d·sinφ))

G12(m，n；d，Ф)＝G12(m,n；ceiling(d·cosФ)，floor(d·sinφ))

G21(m，n；d，Ф)＝G21(m,n；floor(d·cosφ)，ceiling(d·sinФ))

G22(m，n；d，Ф)＝G22(m,n；ceiling(d·cosФ)，ceiling(d·sinФ))

wherein G is₁₁、G₁₂、G₂₁、G₂₂The gray level co-occurrence matrix is a gray level co-occurrence matrix corresponding to four nearest neighbor positions, wherein m is d.cos phi-floor (d.cos phi), and n is d.sin phi-floor (d.sin phi).

5. The method for inverting the sea surface wind speed according to claim 1, wherein the relationship between the eigenvalue of the gray level co-occurrence matrix and the wind speed is specifically:

W＝4.4707*T_s+1.7227

wherein, T_sW is the stable value of the extracted entropy, and W is the wind speed in m/s.

6. A system for inverting sea surface wind velocity using synthetic aperture radar images, comprising:

the radiation correction module is used for obtaining a synthetic aperture radar image containing wind stripe information, performing radiation correction on the image, and converting intensity information into a normalized backscattering coefficient;

the recalibration module is used for recalibrating the image after the radiation correction;

the graying module is used for converting the image after the recalibration into a grayscale image;

the first calculation module is used for calculating a gray level co-occurrence matrix of the gray level image;

the second calculation module is used for calculating the characteristic value of the gray level co-occurrence matrix;

and the third calculation module is used for calculating the sea surface wind speed based on the characteristic value of the gray level co-occurrence matrix and the relation between the characteristic value of the gray level co-occurrence matrix and the wind speed.

7. The system for inverting sea surface wind speed of claim 6, wherein the recalibration formula is as follows:

R＝σ₀/S(θ)

wherein σ₀For normalization of backscattering coefficient, S (theta) is a calculated value of the backscattering model CMOD5.N at a wind speed of 10m/S and an incident angle theta at 45 DEG in the wind direction, and theta is the calculated sigma₀And R is the pixel value after the re-calibration corresponding to the incident angle at the pixel.

8. The system for inverting sea surface wind speed of claim 6, wherein the normalized backscattering coefficient is specifically:

I＝10×lg[(X+A₁)/A₂]+10×lg[sin(θ)]

wherein I is the normalized backscattering coefficient, X is the intensity, A₁Is an offset amount, A₂For gain, θ is the angle of incidence.

9. The system for inverting sea surface wind speed according to claim 6, wherein the first calculation module is specifically:

finding 4 GLCMs whose relative positions are integers and closest to the target position to be found, and respectively marking as G₁₁、G₁₂、G₂₁、G₂₂The corresponding satisfied relative positions are (floor (d · cos Φ), floor (d · sin Φ)), (ceiling (d · cos Φ), floor (d · sin Φ)), (floor (d · cos Φ), ceiling (d · sin Φ)), (ceiling (d · cos Φ), ceiling (d · sin Φ)), where d is the step size, Φ is the angle, floor represents the rounding-down, ceiling represents the rounding-up, specifically: the GLCM with the relative position (d · cos Φ, d · sin Φ) is obtained by bilinear interpolation, and the formula is as follows:

G₁₁(m，n；d，Ф)＝G₁₁(m,n；floor(d·cosφ)，floor(d·sinφ))

G12(m，n；d，Ф)＝G12(m,n；ceiling(d·cosФ)，floor(d·sinφ))

G21(m，n；d，Ф)＝G21(m,n；floor(d·cosφ)，ceiling(d·sinФ))

G22(m，n；d，Ф)＝G22(m,n；ceiling(d·cosФ)，ceiling(d·sinФ))

wherein G is₁₁、G₁₂、G₂₁、G₂₂The gray level co-occurrence matrix is a gray level co-occurrence matrix corresponding to four nearest neighbor positions, wherein m is d.cos phi-floor (d.cos phi), and n is d.sin phi-floor (d.sin phi).

10. The system for inverting sea surface wind speed according to claim 6, wherein the relationship between the eigenvalues of the gray level co-occurrence matrix and the wind speed is specifically:

W＝4.4707*T_s+1.7227

wherein, T_sW is the stable value of the extracted entropy, and W is the wind speed in m/s.

Technical Field

The invention relates to the field of sea surface wind speed calculation, in particular to a method and a system for inverting sea surface wind speed by utilizing a synthetic aperture radar image.

Background

The sea surface wind speed inversion is an important link for exploring and researching oceans and the interaction of ocean and qi, is a necessary foundation for developing and utilizing oceans, is an urgent need of oceanographic research nowadays, and has very important significance for ocean forecast and disaster prevention and reduction. Before the wind speed is observed by using a satellite-borne instrument, the wind speed is mainly measured by an observation station and a ship, although the measurement precision is higher, the observation range is very limited, and the requirements of large-range observation and application are difficult to meet. After the appearance of satellite-borne sensors (altimeters, scatterometers and radiometers), a wide range of measurements of sea surface wind speed was achieved. Wherein, the satellite altimeter can only measure the wind speed of the point under the satellite; microwave scatterometers have achieved large-scale, commercial applications of sea-surface wind field observation, but their spatial resolution is usually 25-50 km; the microwave radiometer has also realized the business detection of sea surface wind field, but the measurement requirement to calibration accuracy and polarization is higher. Meanwhile, the scatterometer and the radiometer cannot measure wind fields within dozens of kilometers of the offshore area and near the island, and cannot meet the requirement of measuring sea surface high-resolution wind fields in certain specific areas.

The satellite-borne Synthetic Aperture Radar (SAR) has the characteristics of all-weather and high-resolution marine remote sensing observation, and can provide effective support for sea surface wind field inversion. The method is particularly suitable for observing a coastal zone and an island region by utilizing SAR (synthetic aperture radar) to invert the sea surface wind field, can overcome the defects of a microwave scatterometer and a radiometer, and avoids the on-site observation by investing a large amount of manpower and material resources. The existing method for inverting the sea surface wind field by utilizing the SAR image mainly calculates the wind speed by combining the geophysical mode function with the wind direction acquired from the image or external data, fails to fully discover the information contained in the SAR image, needs to use the external function or data, and is very sensitive to the calibration accuracy of the SAR data.

The invention patent application with publication number CN 110398738A discloses a method for inverting sea surface wind speed by using remote sensing images, which carries out geometric correction and radiation correction by obtaining remote sensing images containing wind stripes; converting the normalized backscatter image to a grey scale image; calculating a gray level co-occurrence matrix of the gray level image in a specific direction (wind direction); extracting a stable value of the gray level co-occurrence matrix of the image according to the characteristic value (energy) of the gray level co-occurrence matrix of the image; and inverting the wind speed according to the relation between the stable value of the energy and the wind speed.

Although the inversion of the sea surface wind speed is carried out according to the information contained in the SAR image, the inversion effect is poor when the additive factor of the SAR image radiometric calibration is inaccurate. Therefore, how to ensure the effect of inverting the wind speed when the additive factor of radiometric calibration is inaccurate is an urgent problem to be solved in the field.

Disclosure of Invention

The invention aims to provide a method and a system for inverting sea surface wind speed by utilizing a synthetic aperture radar image, aiming at the defects of the prior art. And recalibrating the image after the radiation correction so as to avoid the problem of poor inversion effect when the additive factor of the SAR image radiation calibration is not accurate and improve the accuracy of wind speed calculation.

In order to achieve the purpose, the invention adopts the following technical scheme:

a method for inverting sea surface wind speed by utilizing a synthetic aperture radar image comprises the following steps:

s1, obtaining a synthetic aperture radar image containing wind stripe information, performing radiation correction on the image, and converting intensity information into a normalized backscattering coefficient;

s2, recalibrating the image after radiation correction;

s3, converting the image after the recalibration into a gray image;

s4, calculating a gray level co-occurrence matrix of the gray level image;

s5, calculating a characteristic value of the gray level co-occurrence matrix;

and S6, calculating the sea surface wind speed based on the characteristic value of the gray level co-occurrence matrix and the relation between the characteristic value of the gray level co-occurrence matrix and the wind speed.

Further, the formula of the recalibration is as follows:

R＝σ₀/S(θ)

wherein σ₀For normalizing backscattering coefficient, S (theta) is a calculated value of the backscattering model CMOD5.N at the wind speed of 10m/S and the incident angle of theta under the wind direction of 45 degrees, and theta is the calculated valueσ₀And R is the pixel value after the re-calibration corresponding to the incident angle at the pixel.

Further, the normalized backscattering coefficient is specifically:

I＝10×lg[(X+A₁)/A₂]+10×lg[sin(θ)]

wherein I is the normalized backscattering coefficient, X is the intensity, A₁Is an offset amount, A₂For gain, θ is the angle of incidence.

Further, the step S4 is specifically:

finding 4 GLCMs whose relative positions are integers and closest to the target position to be found, and respectively marking as G₁₁、G₁₂、G₂₁、G₂₂The corresponding satisfied relative positions are (floor (d · cos Φ), floor (d · sin Φ)), (ceiling (d · cos Φ), floor (d · sin Φ)), (floor (d · cos Φ), ceiling (d · sin Φ)), (ceiling (d · cos Φ), ceiling (d · sin Φ)), where d is the step size, Φ is the angle, floor represents the rounding-down, ceiling represents the rounding-up, specifically: the GLCM with the relative position (d · cos Φ, d · sin Φ) is obtained by bilinear interpolation, and the formula is as follows:

G₁₁(m，n；d，Ф)＝G₁₁(m,n；floor(d·cosφ)，floor(d·sinφ))

G12(m，n；d，Ф)＝G12(m,n；ceiling(d·cosФ)，floor(d·sinφ))

G21(m，n；d，Ф)＝G21(m,n；floor(d·cosφ)，ceiling(d·sinФ))

G22(m，n；d，Ф)＝G22(m,n；ceiling(d·cosФ)，ceiling(d·sinФ))

wherein G is₁₁、G₁₂、G₂₁、G₂₂The gray level co-occurrence matrix is a gray level co-occurrence matrix corresponding to four nearest neighbor positions, wherein m is d.cos phi-floor (d.cos phi), and n is d.sin phi-floor (d.sin phi).

Further, the relationship between the eigenvalue of the gray level co-occurrence matrix and the wind speed is specifically as follows:

W＝4.4707*T_s+1.7227

wherein, T_sW is the stable value of the extracted entropy, and W is the wind speed in m/s.

The invention also provides a system for inverting the sea surface wind speed by utilizing the synthetic aperture radar image, which comprises the following steps:

the radiation correction module is used for obtaining a synthetic aperture radar image containing wind stripe information, performing radiation correction on the image, and converting intensity information into a normalized backscattering coefficient;

the recalibration module is used for recalibrating the image after the radiation correction;

the graying module is used for converting the image after the recalibration into a grayscale image;

the first calculation module is used for calculating a gray level co-occurrence matrix of the gray level image;

the second calculation module is used for calculating the characteristic value of the gray level co-occurrence matrix;

and the third calculation module is used for calculating the sea surface wind speed based on the characteristic value of the gray level co-occurrence matrix and the relation between the characteristic value of the gray level co-occurrence matrix and the wind speed.

Further, the formula of the recalibration is as follows:

R＝σ₀/S(θ)

wherein σ₀For normalization of backscattering coefficient, S (theta) is a calculated value of the backscattering model CMOD5.N at a wind speed of 10m/S and an incident angle theta at 45 DEG in the wind direction, and theta is the calculated sigma₀And R is the pixel value after the re-calibration corresponding to the incident angle at the pixel.

Further, the normalized backscattering coefficient is specifically:

I＝10×lg[(X+A₁)/A₂]+10×lg[sin(θ)]

wherein I is the normalized backscattering coefficient, X is the intensity, A₁Is an offset amount, A₂For gain, θ is the angle of incidence.

Further, the first calculating module is specifically:

finding 4 GLCMs whose relative positions are integers and closest to the target position to be found, and respectively marking as G₁₁、G₁₂、G₂₁、G₂₂Corresponding to the relative position of the satisfyOther examples are (floor (d · cos Φ), floor (d · sin Φ)), (ceiling (d · cos Φ), floor (d · sin Φ)), (floor (d · sin Φ)), ceiling (d · cos Φ), ceiling (d · sin Φ)), (ceiling (d · cos Φ), ceiling (d · sin Φ)), where d is the step size, Φ is the angle, floor represents rounding down, ceiling represents rounding up, specifically: the GLCM with the relative position (d · cos Φ, d · sin Φ) is obtained by bilinear interpolation, and the formula is as follows:

G₁₁(m，n；d，Ф)＝G₁₁(m,n；floor(d·cosφ)，floor(d·sinφ))

G12(m，n；d，Ф)＝G12(m,n；ceiling(d·cosФ)，floor(d·sinφ))

G21(m，n；d，Ф)＝G21(m,n；floor(d·cosφ)，ceiling(d·sinФ))

G22(m，n；d，Ф)＝G22(m,n；ceiling(d·cosФ)，ceiling(d·sinФ))

wherein G is₁₁、G₁₂、G₂₁、G₂₂The gray level co-occurrence matrix is a gray level co-occurrence matrix corresponding to four nearest neighbor positions, wherein m is d.cos phi-floor (d.cos phi), and n is d.sin phi-floor (d.sin phi).

Further, the relationship between the eigenvalue of the gray level co-occurrence matrix and the wind speed is specifically as follows:

W＝4.4707*T_s+1.7227

wherein, T_sW is the stable value of the extracted entropy, and W is the wind speed in m/s.

The method aims at the sea surface wind field observation requirements of special areas such as open sea areas, coastal zones and the like, utilizes the large-range coverage and high-resolution capability of the SAR image, carries out quantitative processing and graying on the SAR image based on the bright and dark stripe characteristics contained in the SAR image and presented on the image due to the modulation of the sea surface wind field, and then utilizes the gray level co-occurrence matrix to carry out analysis, thereby obtaining the information of the sea surface wind field of the imaging area for measuring the sea surface wind speed. By means of the relation between the characteristic value and the sea surface wind speed, the sea surface wind speed is inverted, and the method can be used for large-range sea surface wind field monitoring and wind resource assessment. The method and the device perform recalibration on the image after radiation correction so as to avoid the problem of poor inversion effect when the additive factor of the SAR image radiometric calibration is not accurate and improve the accuracy of wind speed calculation.

Drawings

FIG. 1 is a flowchart illustrating a method for inverting sea surface wind speed using synthetic aperture radar images according to an embodiment;

FIG. 2 is a typical remote sensing image containing wind streaks;

FIG. 3 is a grayscale image into which a typical wind streak remote sensing image is converted;

FIG. 4 is the entropy of a typical wind streak remote sensing image as a function of step size in the wind direction;

FIG. 5 is a scatter plot of the stable value of entropy extracted in the wind direction versus the wind speed and its fit;

FIG. 6 is a graph of wind speed calculated by fitting a relationship to an ECMWF reanalyzed wind speed;

FIG. 7 is a plot of wind speed inversion results with radiometric calibration additive factor misalignment versus ECMWF reanalyzed wind speeds;

fig. 8 is a structural diagram of a system for inverting sea surface wind speed by using synthetic aperture radar images according to the second embodiment.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.

The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.