CN111812646A - Method and system for inverting sea surface wind speed by utilizing synthetic aperture radar image - Google Patents
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Abstract
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.
Description
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*Ts+1.7227
wherein, TsW 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*Ts+1.7227
wherein, TsW 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.
Example one
As shown in fig. 1, the present embodiment provides a method for inverting sea surface wind speed by using synthetic aperture radar image, including:
s1, obtaining a synthetic aperture radar image containing wind stripe information, and performing radiation correction on the image;
the method utilizes the synthetic aperture radar image to invert the sea surface wind speed, and specifically utilizes the large-range coverage and high-resolution capability of the SAR image aiming at the observation requirements of the sea surface wind field in special areas such as open sea areas, coastal zones and the like, and obtains the information of the sea surface wind field based on the bright and dark stripe characteristics which are contained in the SAR image and appear on the image due to the modulation of the sea surface wind field, thereby calculating the sea surface wind speed. Therefore, in order to invert the sea surface wind speed, the invention firstly acquires a synthetic aperture radar image containing wind stripe information so as to calculate the wind speed based on the stripe information. Figure 2 shows a typical synthetic aperture radar image containing wind fringes.
After the SAR image is obtained, the image needs to be preprocessed, and the interference of atmosphere, solar altitude, terrain and the like is eliminated. The method firstly carries out radiation correction on the SAR image and converts the intensity information into a normalized backscattering coefficient. And carrying out radiation correction on the image, and converting the intensity value of the image into a normalized backscattering coefficient. 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.
It should be noted that the radiation correction formulas for satellite data in different formats are slightly different, and the present invention only provides one of the radiation correction formulas by way of example, and in a specific application, other radiation correction formulas may be selected according to specific needs, and are not limited herein.
S2, recalibrating the image after radiation correction;
the radiation calibration is that when a user needs to calculate the spectral reflectivity or spectral radiation brightness of a ground object, or needs to compare images acquired by different sensors at different times, the brightness gray value of the images must be converted into absolute radiation brightness. The image is subjected to radiation correction, and the brightness distribution is not uniform due to the incident angle, so that the image subjected to radiation correction is recalibrated by the method. Specifically, the formula for rescaling 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.
It is noted that the invention recalibrates the radiation corrected image using the calculated value of the backscattering model cmod5.n as the denominator. However, other geophysical mode function calculations may be substituted, such as CMOD4, CMOD _ IFR2, etc., and the corresponding wind speed, wind direction, angle of incidence, etc. may be selected as desired. And is not limited herein.
S3, converting the image after the recalibration into a gray image;
since the gray level co-occurrence matrix of the image needs to be calculated, the image after re-calibration needs to be converted into a gray level image. In consideration of the computational complexity of the gray level co-occurrence matrix, the gray level range of the invention is 0-15, and 16 orders are taken. The calculation formula for converting the normalized backscattering coefficient into gray scale is as follows:
wherein I (I, j) is the pixel value of the remote sensing image, gray (I, j) is the gray value of the transformed image, the range is 0-15, ImaxIs the maximum value in the image value range, IminAnd i, j is the minimum value in the image value range and is a positive integer.
Fig. 3 shows a schematic diagram of the conversion of the SAR image shown in fig. 2 into a grayscale image. In the process of graying the image, linear mapping is adopted, and other forms of graying schemes can achieve the same effect. Without limitation, those skilled in the art may select other graying calculation methods such as exponential transformation, power transformation, logarithmic transformation, etc., as needed.
S4, calculating a gray level co-occurrence matrix of the gray level image;
gray level co-occurrence matrix (GLCM) refers to a common method for describing texture by studying the spatial correlation properties of Gray levels. The gray level co-occurrence matrix is used to describe texture features. Since the texture is formed by the repeated appearance of the gray scale distribution at the spatial position, a certain gray scale relationship, i.e., a spatial correlation characteristic of the gray scale in the image, exists between two pixels spaced apart from each other in the image space.
The method determines the wind speed based on the texture of the SAR image, so a gray level co-occurrence matrix of the gray level image is needed to describe the texture characteristics of the SAR image. Specifically, assuming that the step size of the GLCM to be solved is d and the angle is Φ, that is, the relative positions of the pixels are (d · cos Φ, d · sin Φ), a specific method for solving the gray level co-occurrence matrix G thereof is as follows:
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 relative positions corresponding to the requirements 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)). Wherein, 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).
S5, calculating a characteristic value of the gray level co-occurrence matrix;
and calculating the characteristic values of the gray level co-occurrence matrix under different step lengths according to a Halick formula. The calculation formula is as follows:
wherein p (i, j) is an element of the gray level co-occurrence matrix, i, j is a positive integer, and T is entropy. Then, a stable value of the entropy is extracted, where the stable value is extracted by: the standard deviation and the mean of each 8 data points are calculated, and when the standard deviation of the 8 points is less than 1% of the mean, the mean of the 8 points at the moment is taken as a stable value of entropy.
Fig. 4 shows the entropy of the SAR image in the wind direction as a function of the step size, and as can be seen from fig. 4, the entropy in the wind direction starts to decrease rapidly as the step size increases until it tends to stabilize. For the same step size, the smaller the wind speed, the greater its corresponding entropy. FIG. 5 shows a scatter plot of the steady value of entropy extracted in the wind direction versus the wind speed and its fit. As can be seen from FIG. 5, the entropy stability is strong at different wind speeds.
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.
And after the characteristic value of the gray level co-occurrence matrix is calculated, 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 relationship between the eigenvalue of the gray level co-occurrence matrix and the wind speed is specifically as follows:
W＝4.4707*Ts+1.7227
wherein, TsAs the entropy of extractionW is the wind speed (in m/s).
In order to accurately evaluate the performance of the inversion wind speed of the application, the invention compares the inverted wind speed with the re-analysis wind speed of the European middle-term Weather forecast center (ECMWF), which is an international organization including 34 national supports and is an international Weather forecast research and business organization unique to the world today. Specifically, a comparison graph of the wind speed obtained through calculation of the fitting relation and the ECMWF reanalysis wind speed is compared with a wind speed inversion result when the radiometric calibration additive factor is not in time and the comparison graph of the ECMWF reanalysis wind speed. Fig. 6 and 7 respectively show a comparison graph of wind speed obtained by calculating a fitting relation and ECMWF reanalysis wind speed and a comparison graph of a wind speed inversion result and ECMWF reanalysis wind speed when the radiometric calibration additive factor of the application is not aligned. As can be seen from the graph, the wind speed inversion result of the radiometric calibration method with the non-punctual additive factor has high consistency with the re-analysis data of the ECMWF, and is basically free from interference, which shows that the good wind speed effect can still be obtained through the calculation of the fitting relation when the additive factor is not punctual.
Example two
As shown in fig. 8, the present embodiment provides a system for inverting sea surface wind speed by using synthetic aperture radar image, including:
the radiation correction module is used for obtaining a synthetic aperture radar image containing wind stripe information and carrying out radiation correction on the image;
the method utilizes the synthetic aperture radar image to invert the sea surface wind speed, and specifically utilizes the large-range coverage and high-resolution capability of the SAR image aiming at the observation requirements of the sea surface wind field in special areas such as open sea areas, coastal zones and the like, and obtains the information of the sea surface wind field based on the bright and dark stripe characteristics which are contained in the SAR image and appear on the image due to the modulation of the sea surface wind field, thereby calculating the sea surface wind speed. Therefore, in order to invert the sea surface wind speed, the invention firstly acquires a synthetic aperture radar image containing wind stripe information so as to calculate the wind speed based on the stripe information.
After the SAR image is obtained, the image needs to be preprocessed, and the interference of atmosphere, solar altitude, terrain and the like is eliminated. The method firstly carries out radiation correction on the SAR image and converts the intensity information into a normalized backscattering coefficient. And carrying out radiation correction on the image, and converting the intensity value of the image into a normalized backscattering coefficient. 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.
It should be noted that the radiation correction formulas for satellite data in different formats are slightly different, and the present invention only provides one of the radiation correction formulas by way of example, and in a specific application, other radiation correction formulas may be selected according to specific needs, and are not limited herein.
The recalibration module is used for recalibrating the image after the radiation correction;
the radiation calibration is that when a user needs to calculate the spectral reflectivity or spectral radiation brightness of a ground object, or needs to compare images acquired by different sensors at different times, the brightness gray value of the images must be converted into absolute radiation brightness. When the radiation calibration is not accurate, the inversion result of the wind speed is poor, and the wind speed calculation is inaccurate. Therefore, the invention recalibrates the image after radiation correction to avoid the problem of uneven brightness caused by the incidence angle when the SAR image is subjected to radiation calibration, and improves the accuracy of wind speed calculation. Specifically, the formula for rescaling 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.
It is noted that the invention recalibrates the radiation corrected image using the calculated value of the backscattering model cmod5.n as the denominator. However, other geophysical mode function calculations may be substituted, such as CMOD4, CMOD _ IFR2, etc., and the corresponding wind speed, wind direction, angle of incidence, etc. may be selected as desired. And is not limited herein.
The graying module is used for converting the image after the recalibration into a grayscale image;
since the gray level co-occurrence matrix of the image needs to be calculated, the image after re-calibration needs to be converted into a gray level image. In consideration of the computational complexity of the gray level co-occurrence matrix, the gray level range of the invention is 0-15, and 16 orders are taken. The calculation formula for converting the normalized backscattering coefficient into gray scale is as follows:
wherein I (I, j) is the pixel value of the remote sensing image, gray (I, j) is the gray value of the transformed image, the range is 0-15, ImaxIs the maximum value in the image value range, IminAnd i, j is the minimum value in the image value range and is a positive integer.
In the process of graying the image, linear mapping is adopted, and other forms of graying schemes can achieve the same effect. Without limitation, those skilled in the art may select other graying calculation methods such as exponential transformation, power transformation, logarithmic transformation, etc., as needed.
The first calculation module is used for calculating a gray level co-occurrence matrix of the gray level image;
gray level co-occurrence matrix (GLCM) refers to a common method for describing texture by studying the spatial correlation properties of Gray levels. The gray level co-occurrence matrix is used to describe texture features. Since the texture is formed by the repeated appearance of the gray scale distribution at the spatial position, a certain gray scale relationship, i.e., a spatial correlation characteristic of the gray scale in the image, exists between two pixels spaced apart from each other in the image space.
The method determines the wind speed based on the texture of the SAR image, so a gray level co-occurrence matrix of the gray level image is needed to describe the texture characteristics of the SAR image. Specifically, assuming that the step size of the GLCM to be solved is d and the angle is Φ, that is, the relative positions of the pixels are (d · cos Φ, d · sin Φ), a specific method for solving the gray level co-occurrence matrix G thereof is as follows:
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 relative positions corresponding to the requirements 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)). Wherein, 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).
The second calculation module is used for calculating the characteristic value of the gray level co-occurrence matrix;
and calculating the characteristic values of the gray level co-occurrence matrix under different step lengths according to a Halick formula. The calculation formula is as follows:
wherein p (i, j) is an element of the gray level co-occurrence matrix, i, j is a positive integer, and T is entropy. Then, a stable value of the entropy is extracted, where the stable value is extracted by: the standard deviation and the mean of each 8 data points are calculated, and when the standard deviation of the 8 points is less than 1% of the mean, the mean of the 8 points at the moment is taken as a stable value of entropy.
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.
And after the characteristic value of the gray level co-occurrence matrix is calculated, 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 relationship between the eigenvalue of the gray level co-occurrence matrix and the wind speed is specifically as follows:
W＝4.4707*Ts+1.7227
wherein, TsW is the stable value of the extracted entropy and is the wind speed (in m/s).
Therefore, 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 that the brightness is not uniform when the SAR image radiation calibration is influenced by the incident angle and improve the accuracy of wind speed calculation. Meanwhile, the wind speed is not inverted by directly using the scattering coefficient in the algorithm, so that a good wind speed inversion result can be obtained when the additive factor of radiometric calibration is not aligned.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
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*Ts+1.7227
wherein, TsW 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*Ts+1.7227
wherein, TsW is the stable value of the extracted entropy, and W is the wind speed in m/s.
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