CN110398738B - Method for inverting sea surface wind speed by using remote sensing image - Google Patents
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Abstract
The invention provides a novel method for measuring sea surface wind speed by using a remote sensing image. The method comprises the steps of obtaining a remote sensing image containing wind stripes, and performing geometric correction and radiation correction; 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. The method aims at the requirement of monitoring the sea surface wind speed in a large range, utilizes the large-range covering capability of a remote sensing technology method, and utilizes the gray level co-occurrence matrix to carry out quantitative processing on the data of the remote sensing image based on the bright and dark stripe characteristics contained in the remote sensing image and presented by the modulation of the sea surface wind field on the sea surface roughness, including the intensity and the spatial distribution of the bright and dark stripes, so that the method is used for monitoring the sea surface wind speed, is an innovation of the remote sensing information technology for observing the sea surface wind speed, and has great practical value.
Description
Technical Field
The invention belongs to the field of remote sensing technology application and image processing, and particularly relates to a method for inverting sea surface wind speed as expected by utilizing gray information contained in a remote sensing 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 has the characteristics of all-time, 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 a geophysical mode function through the wind direction acquired from the image or external data. From the retrieved public data, no method for measuring sea surface wind speed by using SAR images per se completely consistent with the invention exists.
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 on the gray data of 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 utilizes the gray co-occurrence matrix to carry out analysis so as to obtain the information of the sea surface wind field for measuring the sea surface wind speed.
Disclosure of Invention
The invention aims to provide a novel method for measuring sea surface wind speed by utilizing SAR images.
The invention is realized by the following technical scheme:
a method for measuring sea surface wind speed by using remote sensing images is characterized by comprising the following steps:
(1) acquiring a remote sensing image containing wind stripes, and performing geometric correction and radiation correction;
(2) converting the normalized backscatter image to a grey scale image;
(3) calculating a gray level co-occurrence matrix of the gray level image in a specific direction (wind direction);
(4) 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;
(5) and inverting the wind speed according to the relation between the stable value of the energy and the wind speed.
The method for inverting the sea surface wind speed by using the remote sensing image is characterized in that the normalized backscattering image is converted into a gray level image in the step (2), so that the calculation of the gray level co-occurrence matrix becomes feasible. The conversion relation between the pixel value and the gray value of the remote sensing image 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 of the gray scale in the image, IminIs the minimum value of the gray scale in the image.
The method for inverting the sea surface wind speed by using the remote sensing image is characterized in that in the step (3), a gray level co-occurrence matrix of the gray level image in a specific direction (wind direction) is calculated, and unnecessary calculation is reduced by using the approximately consistent relation between the wind stripe direction and the wind direction.
The method for inverting the sea surface wind speed by using the remote sensing image is characterized in that in the step (4), a characteristic value is obtained according to the gray level co-occurrence matrix of the image obtained in the step (3), so that the image characteristic of the wind stripe can be quantitatively expressed. The formula for calculating the eigenvalues used here is:
wherein p (i, j) is an element of the gray level co-occurrence matrix, i, j is a positive integer, and T is energy. Then, a stable value of the energy is extracted, wherein the method for extracting the stable value is as follows: 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 this time is taken as a stable value of energy.
The method for inverting the sea surface wind speed by using the remote sensing image is characterized in that the relation between the characteristic value and the wind speed in the step (5) can be inverted into the sea surface wind speed by the characteristic value through the relation. The relationship between the characteristic value and the sea surface wind speed is as follows:
W＝-76*Ts+14
further, the radiation correction formula in step (1) is as follows:
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.
Middle TsW is the wind speed (in m/s) for a steady value of extracted energy.
Specifically, the sea surface wind field can cause the change of the roughness of the sea surface, images on the remote sensing image and presents the change as the wind stripe texture information of light and shade or distribution. The principle is as follows: and the sea surface roughness is modulated by the action of the wind stress on the sea surface in the sea surface wind field. The larger the sea surface roughness is, the larger the backscattering received by the sensor is; conversely, the backscattering received by the sensor is smaller, so that alternate bright and dark stripes can be generated on the image. In the remote sensing image, the information such as the positions, the intensities and the like of the bright and dark stripes mainly depends on the size and the distribution of a sea surface wind field, and a certain relation exists between the bright and dark stripes. Through the relationship, the texture information of wind strips in the remote sensing image can be utilized to directly extract and obtain sea surface wind field information for carrying out wind speed inversion.
The method comprises the steps of converting a remote sensing normalized backscatter image into a gray level image by utilizing wind stripe characteristic information, including the strength of wind stripes and the spatial distribution characteristics of the wind stripes, presented in a remote sensing image by a sea surface wind field, and acquiring sea surface wind speed information by utilizing a gray level co-occurrence matrix and a characteristic value thereof and by virtue of the relationship between the characteristic value and sea surface wind speed.
The invention has the beneficial effects that:
the large-scale monitoring of sea surface wind fields is a need for researching the interaction between the marine environment and sea air, and is a foundation for developing and utilizing the sea. The observation range of the traditional means (ships, buoys and the like) is very limited, the requirements of large-range observation and application are difficult to meet, and the manpower consumption and the economic cost are high. Remote sensing monitoring is a very effective monitoring means for sea surface wind field. Aiming at the requirements of large-range sea surface wind field monitoring and wind resource evaluation, the invention utilizes the large-range coverage capability of a remote sensing technology method, converts a remote sensing image into a gray level image based on bright and dark stripe characteristics contained in the remote sensing image and presented by the modulation of a sea surface roughness caused by a sea surface wind field, including the strength of the bright and dark stripes and the spatial distribution characteristics, acquires the wind speed information of the sea surface by utilizing a gray level co-occurrence matrix and a characteristic value and by virtue of the relation between the characteristic value and the sea surface wind speed, is used for large-range sea surface wind field monitoring and wind resource evaluation, is an innovation of the remote sensing information technology for sea surface wind field observation, and has great practical value.
Drawings
FIG. 1 is a technical roadmap for measuring sea surface wind speed using remote sensing images;
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 energy extracted in the wind direction for a typical wind streak remote sensing image;
FIG. 5 is a scatter plot and fit of the energy extracted in the wind direction versus the wind speed;
FIG. 6 is a plot of wind speed calculated as a fit to a re-analyzed wind speed.
Detailed Description
The following detailed description of the invention is made with reference to the accompanying drawings:
example 1
According to the method for measuring sea surface wind speed by using remote sensing images, disclosed by the invention, an experiment is carried out, a technical route is shown in figure 1, and the method specifically comprises the following steps:
(1) obtaining a remote sensing image containing wind stripe information, carrying out radiation correction on the image, converting intensity information into a normalized backscattering coefficient, and then carrying out geometric correction to enable the normalized backscattering coefficient to correspond to an actual geographic position: and selecting a remote sensing image with higher resolution, and checking the definition and integrity of the wind stripes of the image. And carrying out radiation correction on the image, and converting the intensity value of the image into a normalized backscattering coefficient. The image is then geometrically corrected to correspond to the actual geographic location.
The radiation correction formulas of the satellite data with different formats are slightly different, and the calculation formula of the radiation correction formula given here is as follows:
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.
The typical geometric correction method is a polynomial correction method, the overall deformation of the remote sensing image is regarded as the comprehensive effect result of translation, scaling, rotation, deflection and higher basic deformation, so the coordinate relation between corresponding points of the image before and after correction can be expressed by a polynomial, and due to the uncertainty of parameters of the satellite and the like, the geometric correction formulas are different when different satellite data are used.
(2) Converting the image of the normalized backscattering coefficient to a grayscale image:
since a gray level co-occurrence matrix of the image needs to be calculated, the image of the normalized backscattering coefficient needs to be converted into a gray level image. Considering the computational complexity of the gray level co-occurrence matrix, the gray level range is 0-15, and 16 orders are total. 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 normalized backscattering coefficient image, gray (I, j) is the gray value of the transformed image, the range is 0-15, and I (I, j) is the gray value of the transformed imagemaxIs the maximum value of the gray scale in the image, IminIs the minimum value of the gray scale in the image.
(3) Calculating a gray level co-occurrence matrix of the image:
according to the definition of the gray level co-occurrence matrix, calculating the gray level co-occurrence matrix of the image (referring to the calculation method of any gray level co-occurrence matrix based on matrix element interpolation and the calculation method provided in the application), assuming that the step length of the GLCM to be solved is d and the angle is phi, namely the relative position of the pixel is (d · cos phi, d · sin phi), the specific method for solving the gray level co-occurrence matrix G is as follows:
calculating 4 GLCMs with integral relative positions and closest to the target position to be calculated, and respectively recording as C11、C12、C21、C22The relative positions of the corresponding satisfies are respectively
(floor(d·cosφ)，floor(d·sinφ))、
(ceiling(d·cosφ)，floor(d·sinφ))、
(floor(d·cosΦ)，ceiling(d·sinΦ))、
(ceiling (d · cos Φ), ceiling (d · sin Φ)). Wherein floor represents rounding down and ceiling represents rounding up, and
G11(m,n；d,Φ)＝G11(m,n；floor(d·cosΦ),floor(d·sinΦ))
G12、G21、G22and similarly are available. Finding the relative position by bilinear interpolation
GLCM of (d · cos Φ, d · sin Φ), formula:
C(m，n)＝(1-a)(1-b)C11(m，n)+a(1-b)C12(m，n)+(1-a)bC21(m，n)+abC22(m，n)
wherein G is11,G12,G21,G22Four position pairs being nearest neighborsThe gray level co-occurrence matrix is defined as a d · cos Φ -floor (d · cos Φ), and b · d · sin Φ -floor (d · sin Φ).
(4) Calculating and extracting 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:
where p (i, j) is an element of the gray level co-occurrence matrix and T is energy. Then, a stable value of the energy is extracted, wherein the method for extracting the stable value is as follows: 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 this time is taken as a stable value of energy.
(5) And (4) calculating the wind speed according to the characteristic value obtained in the step (4) based on the relation between the characteristic value of the gray level co-occurrence matrix and the wind speed. The calculation formula is as follows:
W＝-76*Ts+14
wherein T issW is the wind speed (in m/s) for a steady value of extracted energy.
The scatter diagram of the energy and wind speed extracted from the wind direction by the image containing the wind streak and the fitting relationship thereof are shown in fig. 5, and fig. 6 is a comparison diagram of the wind speed calculated by the fitting relationship and the ECMWF reanalysis wind speed. The comparison result shows that the wind speed calculated by the method has higher consistency with re-analysis data of the ECMWF, the correlation is very high, the root mean square error is only 1.44m/s, and the wind speed inversion effect is good.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are included in the scope of the present invention.
Claims (3)
1. A method for inverting sea surface wind speed by using remote sensing images comprises the following steps:
(1) carrying out radiation correction and geometric correction on the remote sensing image containing the wind stripes; the radiation correction refers to converting the intensity value of the image into a normalized backscattering coefficient; the geometric correction refers to utilizing a typical polynomial geometric correction method to enable the image to correspond to the actual geographic position of the ground;
(2) converting the normalized backscattering coefficient image into a gray level image;
(3) calculating a gray level co-occurrence matrix of the gray level image in the wind direction;
(4) calculating a characteristic value according to the gray level co-occurrence matrix of the image; the characteristic value is energy T, and the calculation formula is as follows:
T＝∑i∑j[p(i，j)]2；
wherein p (i, j) is an element of the gray level co-occurrence matrix, i, j is a positive integer, and T is energy; then, a stable value of the energy is extracted, wherein the method for extracting the stable value is as follows: calculating the standard deviation and the mean value of each 8 data points, and taking the mean value of the 8 points as a stable value of energy when the standard deviation of the 8 points is less than 1% of the mean value;
(5) inverting the wind speed according to the relation between the stable value of the energy and the wind speed; the specific formula is as follows:
W＝-76*Ts+14
wherein T isSW is the steady value of the extracted energy, and is the wind speed in m/s.
2. The method for inverting the sea surface wind speed by using the remote sensing image as claimed in claim 1, wherein in the step (3), when the wind stripe direction is consistent with the wind direction, a gray level co-occurrence matrix of the gray level image in the wind direction is calculated.
3. The method for inverting the sea surface wind speed by using the remote sensing image as claimed in claim 1, wherein the formula of the radiation correction in the step (1) is as follows:
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, A2Is a gainAnd θ is the angle of incidence.
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| CN111812646A (en) * | 2020-07-01 | 2020-10-23 | 自然资源部第二海洋研究所 | Method and system for inverting sea surface wind speed by utilizing synthetic aperture radar image |
| CN111862005B (en) * | 2020-07-01 | 2023-11-17 | 自然资源部第二海洋研究所 | Method and system for precisely positioning tropical cyclone center by utilizing synthetic radar image |
| CN112801332B (en) * | 2020-11-18 | 2024-03-26 | 国网江苏省电力有限公司江阴市供电分公司 | Short-term wind speed prediction method based on gray level co-occurrence matrix |
| CN113037310B (en) * | 2021-03-03 | 2022-01-11 | 深圳市富创优越科技有限公司 | Transceiver for boats and ships with discernment early warning function |
| CN114324973B (en) * | 2022-03-17 | 2022-06-07 | 南方海洋科学与工程广东省实验室(广州) | Typhoon wind speed inversion method and device, electronic equipment and storage medium |
| CN115009472A (en) * | 2022-05-06 | 2022-09-06 | 大连环信科技有限公司 | Movable wind energy collection cluster system with wind energy gathering guidance |
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