CN113589289B - Method and system for extracting dissymmetry of ionized layer convection image of SuperDARN radar - Google Patents

Method and system for extracting dissymmetry of ionized layer convection image of SuperDARN radar Download PDF

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CN113589289B
CN113589289B CN202110783539.9A CN202110783539A CN113589289B CN 113589289 B CN113589289 B CN 113589289B CN 202110783539 A CN202110783539 A CN 202110783539A CN 113589289 B CN113589289 B CN 113589289B
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CN113589289A (en
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刘二小
徐晨
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Hangzhou Dianzi University
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    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses a method and a system for extracting the dissymmetry of a super DARN ionosphere convective image, wherein the method comprises the following steps: step 1: acquiring SuperDARN radar observation data and OMNI satellite observation data and preprocessing; step 2: matching and aligning OMNI satellite observation data and SuperDARN radar observation data to form a data set; step 3: fitting an optimal curved surface by using a curved surface fitting method and drawing a convection image by an interpolation mode; step 4: calculating the slope and the corresponding inclination angle through the positions of the maximum potential point and the minimum potential point, and overturning the convection image; step 5: drawing a reversed convection image by using a curved surface fitting method; step 6: evaluating the original image and the turnover image through related parameters; step 7: and evaluating the asymmetry of the flow image by adopting an asymmetry index. The invention can be used to describe the asymmetry between the two hemispheres of a flow image.

Description

Method and system for extracting dissymmetry of ionized layer convection image of SuperDARN radar
Technical Field
The invention belongs to the technical field of image analysis of space weather, and particularly relates to a method and a system for extracting dissymmetry of a super DARN radar ionosphere convection image.
Background
Gao Wei ionospheric plasma convection is an important phenomenon of spatial weather and is also an important parameter in ionospheric research, which implies a series of important information for the energy transport of the solar wind-direction magnetic layer-ionosphere system. Many studies on ionospheric convection have focused on analyzing the dependence of ionospheric plasma convection on parameters of near-earth space, such as interplanetary magnetic field components, solar wind velocity, solar wind dynamic pressure, and geomagnetic activity index.
In the solar wind-magnetic layer-ionosphere-thermal layer coupling process, the electrodynamic process is the most important ligament. Research shows that the direction and the size of the inter-planetary magnetic field IMF are decisive factors for influencing the convection of an ionosphere, and the north-south component of the IMF determines the basic structure of the convection and the strength and the spatial scale of the convection, and the east-west component of the IMF mainly influences the morning and evening asymmetry of the convection. Thus, there is also a significant difference in the flow diagram structure. When IMF is southbound, the magnetic layer circulation is stronger due to the stronger coupling at the top of the magnetic layer, and the convective image tends to appear as a typical double vortex structure. When IMF is northbound, coupling is weaker and circulation is also weaker, but the convective image is more complex, often exhibiting a distorted double vortex structure or even a multiple vortex structure. When the IMF has an east-west component, the convective image introduces strong asymmetry on an original basis.
SuperDARN radar is one of the important means for detecting Gao Wei ionosphere plasma convection in the world at present and is also an important source of convection map data, and consists of high-frequency coherent scattering radar running in the 8-20MHz frequency band, and the field of view covers most of polar ionosphere and part of mid-latitude ionosphere of the northern hemisphere. The temporal resolution of SuperDARN radar is 2 minutes, the spatial resolution is 1 ° in latitude and 2 ° in longitude. Omni satellites are an important means of detecting geospatial parameters with a time resolution of 1 minute and are used as an extension to flowsheet data to facilitate better study of ionospheric flowsheet image uncertainty.
Gao Wei the ionosphere pair flow graph contains important parameters characterizing ionosphere ionodynamics, such as the cap potential CPCP, cap electric field Ef, eddy current spacing, and morning-to-evening asymmetry, which are important indicators of spatial weather changes. The structure of the convection diagram at different times is obviously different under the influence of the direction and the magnitude of the inter-planetary magnetic field IMF. In addition, the structure between the two hemispheres of the convective image is also affected by factors such as the solar zenith angle, IMF clock angle, etc., unlike simple inter-hemispheric mirroring. Therefore, the method is necessary to extract the asymmetry of the ionosphere convection image, and the asymmetry information of the ionosphere convection image can be effectively extracted through image alignment and image inversion, which has important significance for deep understanding of the solar wind-magnetic layer-ionosphere energy transmission process.
Disclosure of Invention
Based on the current situation in the art, in order to make up for the defects of the high latitude plasma convection image analysis method in the prior art, the invention provides a method and a system for extracting the dissymmetry of the ionized layer convection image of the SuperDARN.
The invention adopts the following technical scheme:
the method for extracting the dissymmetry of the ionized layer convection image of the SuperDARN radar comprises the following steps:
step 1: acquiring SuperDARN radar observation data and OMNI satellite observation data and preprocessing;
step 2: matching and aligning OMNI satellite observation data and SuperDARN radar observation data to form a complete data set;
step 3: fitting an optimal curved surface by using a curved surface fitting method and drawing a convection image by an interpolation mode;
step 4: calculating the slope and the corresponding inclination angle through the positions of the maximum potential point and the minimum potential point, and overturning the convection image;
step 5: drawing the overturned convection image by using a curved surface fitting method again;
step 6: evaluating the original image and the turnover image through related parameters;
step 7: and evaluating the asymmetry of the flow image by adopting an asymmetry index.
Preferably, the pretreatment process in step 1 is as follows:
for OMNI satellite data, eliminating invalid data with the interplanetary magnetic field component greater than or equal to 9999, the solar wind speed greater than or equal to 99999 and the solar wind dynamic pressure greater than or equal to 99; for the SuperDARN radar data, data with the number of return points of the day greater than 300 and the number of flow charts of the day greater than 400 are selected.
Preferably, the matching process in step 2 is as follows:
step 2.1: acquiring an inter-satellite electric field IEF, inter-satellite magnetic field components By and Bz (under a GSM coordinate system), a solar wind speed Vs, a solar wind dynamic pressure P, an Alzhi Mach index Ma and a geomagnetic index AE measured By an OMNI satellite;
step 2.2: generating a clock angle theta, a Kan-Lee reconnection electric field Ekl, a 10.7 radio flux Rms and an interplanetary magnetic field joint component B according to the following formula t Four spatial parameters (under the GSM coordinate system) are expressed as follows:
Ekl＝V x ×Btgm×sin 2 (θ/2)
step 2.3: because the time resolution of the SuperDARN radar data and the OMNI satellite data are inconsistent, the SuperDARN is 2 minutes, and the OMNI satellite data is 1 minute, the intersection of the SuperDARN radar data and the OMNI satellite data is selected according to the time, corresponding parameters are connected, and meanwhile invalid data of a flow graph are cut.
Preferably, the clipping process in step 2.3 is as follows:
for the flow chart data with latitude lower than 51 °, deletion processing is performed on the flow chart data due to a large measurement error of the potential value thereof.
Preferably, in step 3, each of the convection images is a sequence with a length of 7059, but since the original data of the convection image is different from the size of the generated grid point coordinate matrix, the data of the corresponding points are generated by interpolation, and then the convection image is drawn by using a curved surface fitting method, with a size of 1201 x 1201.
Preferably, the step 4 is specifically as follows:
step 4.1: determining the slope and the corresponding inclination angle of the maximum potential point and the minimum potential point through the positions of the maximum potential point and the minimum potential point;
step 4.2: rotating the convection image by taking the origin as the center according to the angle of the inclination angle, interpolating by adopting a bilinear method during rotation, and cutting the rotated image to ensure that the size of the rotated image is equal to that of the original image;
step 4.3: and (3) turning the rotated image left and right, rotating the turned image left and right by taking the original point as the center according to the reverse direction of the angle of the inclination angle, interpolating by a bilinear method during rotation, cutting the rotated image, and enabling the size of the rotated image to be equal to that of the original image to obtain a final turned convection image.
Preferably, in step 5, the size of each convection image is still 1201×1201, and the flipped convection image is drawn by using a curved surface fitting method.
Preferably, the specific process of step 6 is as follows:
the method comprises the steps of evaluating an image by using four evaluation indexes, wherein the four evaluation indexes are respectively structure similarity SSIM, peak signal-to-noise ratio PSNR, root mean square error RMSE and average absolute error MAE; the formulas of SSIM, PSNR, RMSE and MAE are as follows:
SSIM＝[l(X,Y)] α ·[c(X,Y)] β ·[s(X,Y)] γ
wherein X represents an original image, and Y represents an inverted image;the mean value is used to estimate the luminance, ">Contrast is estimated by variance,/->The covariance is used to estimate the structural similarity, mu X ,μ Y Mean value of X and Y, sigma X ,σ Y Respectively representing standard deviations of X and Y; sigma (sigma) XY For covariance, C 1 ,C 2 ,C 3 Are all constant, and in order to avoid the case that the denominator is 0, C is usually taken 1 ＝(0.01*L) 2 ,C 2 ＝(0.03*L) 2 ,C 3 ＝C 2 2, l=255, α, β, γ > 0; m and n are the height and width of the image, MAX X Represents the maximum number of bits of X, and is typically MAX X ＝2 n -1, n=8; in calculating SSIM and PSNR, the range of streaming image potential data is normalized to [0,255]。
Preferably, the specific process of step 7 is as follows: put forward an asymmetry index delta Asym The asymmetry of the flow image is evaluated,
wherein C is a constant coefficient, taking c=200, a 1 ,a 2 ,a 3 ,a 4 As the weight coefficient, the weight coefficient a 1 ,a 2 ,a 3 ,a 4 Obtained by an entropy method.
Preferably, the entropy solution in step 7 is as follows: the entropy method is an assignment method for determining index weight coefficients according to the influence of the relative change degree of each index on the whole by calculating the information entropy of each index observation value. Calculating the characteristic specific gravity and the corresponding entropy of each index observation value, then calculating the difference coefficient of the index, and finally obtaining the indexA target weight coefficient; the larger the observed value difference of the index is, the larger the difference coefficient is, and the more important the index is; the weight coefficients corresponding to the four indexes are obtained by an entropy method and are respectively a 1 ＝0.0385,a 2 ＝0.1556,a 3 ＝0.3757,a 4 ＝0.4302。
The invention also discloses a system for extracting the dissymmetry of the ionized layer convection image of the SuperDARN radar, which comprises the following modules:
and the data acquisition and preprocessing module is used for: the method comprises the steps of acquiring and preprocessing SuperDARN radar observation data and OMNI satellite observation data;
the data matching and matching module is used for: the method comprises the steps of matching and aligning OMNI satellite observation data and SuperDARN radar observation data to form a data set;
drawing a convection image module: fitting an optimal curved surface by using a curved surface fitting method and drawing a convection image by an interpolation mode;
a convection image overturning module: calculating the slope and the corresponding inclination angle through the positions of the maximum potential point and the minimum potential point, and overturning the convection image;
drawing a reversed convection image module: drawing a reversed convection image by using a curved surface fitting method;
and an evaluation module: evaluating the original image and the turnover image through related parameters;
and an evaluation module: and evaluating the asymmetry of the flow image by adopting an asymmetry index.
Preferably, the preprocessing process in the data acquisition and preprocessing module is as follows: for OMNI satellite data, eliminating invalid data with the interplanetary magnetic field component greater than or equal to 9999, the solar wind speed greater than or equal to 99999 and the solar wind dynamic pressure greater than or equal to 99; for the SuperDARN radar data, data with the number of return points of the day greater than 300 and the number of flow charts of the day greater than 400 are selected.
Preferably, the matching process in the data matching module is as follows:
firstly, acquiring an inter-planet electric field IEF, inter-planet magnetic field components By and Bz (under a GSM coordinate system), a solar wind speed Vs, a solar wind dynamic pressure P, an Alzhi Mach index Ma and a geomagnetic index AE measured By an OMNI satellite;
generating a clock angle theta, a Kan-Lee reconnection electric field Ekl, a 10.7 radio flux Rms and an interplanetary magnetic field joint component B according to the following formula t Four spatial parameters (under the GSM coordinate system) are expressed as follows:
Ekl＝V x ×Btgm×sin 2 (θ/2)
and because the time resolution of the SuperDARN radar data and the OMNI satellite data is inconsistent, the SuperDARN is 2 minutes, and the OMNI satellite data is 1 minute, the intersection of the SuperDARN radar data and the OMNI satellite data is selected according to the time, corresponding parameters are connected, and meanwhile invalid data of a flow graph is cut.
Preferably, the clipping process is as follows: for the flow chart data with latitude lower than 51 °, deletion processing is performed on the flow chart data due to a large measurement error of the potential value thereof.
Preferably, in the drawing convective image module, each convective image is a sequence with a length of 7059, but because the original data of the convective image is different from the size of the generated grid point coordinate matrix, the data of the corresponding point is generated in an interpolation mode, and then the convective image is drawn by using a curved surface fitting method, wherein the size of the convective image is 1201 x 1201.
Preferably, the convection image flipping module is specifically as follows: determining the slope and the corresponding inclination angle of the maximum potential point and the minimum potential point through the positions of the maximum potential point and the minimum potential point; rotating the convection image with the origin as the center according to the angle of the inclination angle, interpolating by adopting a bilinear method during rotation, and cutting the rotated image to ensure that the size of the rotated image is equal to that of the original image; and then turning the rotated image left and right, rotating the turned image left and right by taking the original point as the center according to the reverse direction of the angle of the inclination angle, interpolating by a bilinear method during rotation, cutting the rotated image, and enabling the size of the rotated image to be equal to that of the original image to obtain a final turned convection image.
Preferably, in the drawing the flipped convective image module, the size of each convective image is still 1201×1201, and the flipped convective image is drawn by using a curved surface fitting method.
Preferably, the evaluation module is specifically as follows: the method comprises the steps of evaluating an image by using four evaluation indexes, wherein the four evaluation indexes are respectively structure similarity SSIM, peak signal-to-noise ratio PSNR, root mean square error RMSE and average absolute error MAE; the formulas of SSIM, PSNR, RMSE and MAE are as follows:
SSIM＝[l(X,Y)] α ·[c(X,Y)] β ·[s(X,Y)] γ
wherein X represents an original image, and Y represents an inverted image;the mean value is used to estimate the luminance, ">Contrast is estimated by variance,/->The covariance is used to estimate the structural similarity, mu X ,μ Y Mean value of X and Y, sigma X ,σ Y Respectively representing standard deviations of X and Y; sigma (sigma) XY For covariance, C 1 ,C 2 ,C 3 Are all constant, and in order to avoid the case that the denominator is 0, C is usually taken 1 ＝(0.01*L) 2 ,C 2 ＝(0.03*L) 2 ,C 3 ＝C 2 2, l=255, α, β, γ > 0; m and n are the height and width of the image, MAX X Represents the maximum number of bits of X, and is typically MAX X ＝2 n -1, n=8; in calculating SSIM and PSNR, the range of streaming image potential data is normalized to [0,255]。
Preferably, the evaluation module is specifically as follows: put forward an asymmetry index delta Asym The asymmetry of the flow image is evaluated,
wherein C is a constant coefficient, taking c=200, a 1 ,a 2 ,a 3 ,a 4 As the weight coefficient, the weight coefficient a 1 ,a 2 ,a 3 ,a 4 Obtained by an entropy method.
Preferably, the entropy solution is as follows: the entropy method is an assignment method for determining index weight coefficients according to the influence of the relative change degree of each index on the whole by calculating the information entropy of each index observation value. Firstly calculating the characteristic proportion and the corresponding entropy of each index observation value, then calculating the difference coefficient of the index, and finally obtaining the weight coefficient of the index; the larger the observed value difference of the index is, the larger the difference coefficient is, and the more important the index is; the weight coefficients corresponding to the four indexes are obtained by an entropy method and are respectively a 1 ＝0.0385,a 2 ＝0.1556,a 3 ＝0.3757,a 4 ＝0.4302。
The invention has the following advantages: the invention discloses a method and a system for extracting the dissymmetry of a super DARN ionosphere convection image, which construct a data set according to super DARN radar observation data and OMNI satellite observation data; fitting and interpolating by using a two-dimensional surface fitting technology, and drawing Gao Wei an ionosphere convection image. Calculating the slope and the corresponding inclination angle of the maximum potential point and the minimum potential point through the positions of the maximum potential point and the minimum potential point, drawing a reversed convection image through a curve fitting technology by rotating the image and turning the image, and providing a new asymmetry index to evaluate the asymmetry of the convection image, so that the asymmetry between two hemispheres of the convection image is described.
Drawings
FIG. 1 is a schematic diagram of the general steps of the method of the present invention.
FIG. 2 is a schematic flow chart of a preferred embodiment of the present invention.
Fig. 3 is a diagram showing the result of flipping an image according to the present invention.
Fig. 4 is a schematic diagram showing the result of the evaluation index of the flip image according to the present invention.
Fig. 5 (a), 5 (b) and 5 (c) are schematic diagrams showing the results of the asymmetry index according to the present invention.
Fig. 6 is a block diagram of the system of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a method for dissymmetry of a flow chart based on a SuperDARN ionosphere, as shown in fig. 1-2, the method comprises the following parts: respectively obtaining data, preprocessing the data, matching the data, generating a convection image, overturning the convection image, drawing the overturned convection image, obtaining evaluation parameters and providing a new asymmetry index. The data analysis of the ionosphere convection image is realized, and the specific steps are as follows:
step 1: firstly, acquiring 2 months of SuperDARN radar observation data and OMNI satellite observation data in 2014, respectively preprocessing the data, and removing invalid data comprising 9999, 99999 and 99; meanwhile, for the observation data of the SuperDARN, the data with the number of return points larger than 300 and the number of daily flow chart sheets larger than 400 are selected to form a reliable data set of the SuperDARN and the OMNI satellite.
Step 2: because the time resolution of the SuperDARN radar data is 2 minutes and the time resolution of the OMNI satellite data is 1 minute, intersection is needed to be taken between the OMNI satellite data and the SuperDARN radar data according to the minimum resolution, then the SuperDARN radar observation data and the OMNI satellite data are aligned according to the date and time, and invalid data with the latitude less than 51 DEG electric potential value of 0 is removed to form a final data set.
Step 3: and obtaining potential values of different longitudes and latitudes at different times through data preprocessing, fitting and interpolating by using a two-dimensional curved surface fitting method, and drawing Gao Wei ionosphere convection images.
Step 4: firstly, determining the slope and the corresponding inclination angle of the maximum potential point and the minimum potential point through the positions of the maximum potential point and the minimum potential point, then rotating a convection image with an origin as a center according to the inclination angle, interpolating by a bilinear method during rotation, and cutting the rotated image to ensure that the size of the rotated image is equal to that of an original image; then, the rotated image is turned left and right, the turned left and right image is rotated around the origin as the center according to the opposite direction of the angle of the inclination angle, interpolation is still carried out by adopting a bilinear method during rotation, and the rotated image is cut, so that the size of the rotated image is equal to that of the original image, and a final turned convection image is obtained.
Step 5: and drawing the reversed convection image by using a curved surface fitting method. Specifically, the size of each convection image is 1201×1201, and the inverted convection image is drawn by using a curved surface fitting method.
Step 6: the image is evaluated using four evaluation indexes, namely structural similarity SSIM, peak signal to noise ratio PSNR, root mean square error RMSE and mean absolute error MAE. The formulas of SSIM, PSNR, RMSE and MAE are as follows:
SSIM＝[l(X,Y)] α ·[c(X,Y)] β ·[s(X,Y)] γ
where X represents the original image and Y represents the flipped image.The mean value is used to estimate the luminance, ">Contrast is estimated by variance,/->The covariance is used to estimate the structural similarity, mu X ,μ Y Mean value of X and Y, sigma X ,σ Y The standard deviations of X and Y are shown, respectively. Sigma (sigma) XY For covariance, C 1 ,C 2 ,C 3 Are all constant, and in order to avoid the case that the denominator is 0, C is usually taken 1 ＝(0.01*L) 2 ,C 2 ＝(0.03*L) 2 ,C 3 ＝C 2 L=255, α, β, γ > 0.m and n are the height and width of the image, MAX X Represents the maximum number of bits of X, and is typically MAX X ＝2 n -1, n=8. In calculating SSIM and PSNR, the range of streaming image potential data must be normalized to [0,255 first]。
The difference between the original image and the turnover image is analyzed, as shown in fig. 3, the graph b is a turnover convection graph image, and the structural similarity SSIM index of the turnover image obtained by the method and the convection image obtained by the super DARN actual measurement is good and can reach more than 0.75; the peak signal-to-noise ratio PSNR may be substantially greater than 15dB; the difference of the original image potential value and the potential value of the inverted image is reflected by the root mean square error RMSE and the average absolute error MAE.
Step 7: in order to further analyze the difference between the original image and the turned image, a new asymmetry index delta is provided Asym Asymmetry of the flow image was evaluated.
Wherein, C is a constant coefficient, C=200, a is taken in the invention 1 ,a 2 ,a 3 ,a 4 As the weight coefficient, the weight coefficient a in the invention 1 ,a 2 ,a 3 ,a 4 Obtained by an entropy method. The entropy method is an assignment method for determining index weight coefficients according to the influence of the relative change degree of each index on the whole by calculating the information entropy of each index observation value. In the invention, the characteristic proportion and the corresponding entropy of each index observation value are calculated, then the difference coefficient of the index is calculated, and finally the weight coefficient of the index is obtained. The larger the observed value difference of the index, the larger the difference coefficient, and the more important the index. The weight coefficients corresponding to the four indexes can be obtained by an entropy method and are respectively a 1 ＝0.0385,a 2 ＝0.1556,a 3 ＝0.3757,a 4 ＝0.4302。
As a result, as shown in fig. 5 (a), 5 (b) and 5 (c), symmetry between the left and right hemispheres in fig. 5 (a) is good, and the asymmetry index δ is shown schematically Asym =18.86 is also smaller; the symmetry between the left and right hemispheres in FIG. 5 (b) is general, while the asymmetry index delta Asym =23.59; in FIG. 5 (c), the symmetry between the left and right hemispheres is poor, and the asymmetry index delta Asym Also, =26.07 is larger. This example illustrates an asymmetry index delta Asym Can objectively reflect the asymmetry of the convection image, and the asymmetry index delta is obtained when the left hemisphere and the right hemisphere are more symmetrical Asym Smaller, when the left and right hemispheres are more asymmetric, the asymmetry index delta Asym The larger.
Example 2
As shown in fig. 6, a system for extracting the asymmetry of the ionosphere convective image of the super darn radar according to the present embodiment includes the following modules:
and the data acquisition and preprocessing module is used for: the method comprises the steps of acquiring and preprocessing SuperDARN radar observation data and OMNI satellite observation data; the pretreatment process is as follows: for OMNI satellite data, eliminating invalid data with the interplanetary magnetic field component greater than or equal to 9999, the solar wind speed greater than or equal to 99999 and the solar wind dynamic pressure greater than or equal to 99; for the SuperDARN radar data, data with the number of return points of the day greater than 300 and the number of flow charts of the day greater than 400 are selected.
The data matching and matching module is used for: the method comprises the steps of matching and aligning OMNI satellite observation data and SuperDARN radar observation data to form a data set; the matching process is as follows:
firstly, acquiring an inter-planet electric field IEF, inter-planet magnetic field components By and Bz (under a GSM coordinate system), a solar wind speed Vs, a solar wind dynamic pressure P, an Alzhi Mach index Ma and a geomagnetic index AE measured By an OMNI satellite;
generating a clock angle theta, a Kan-Lee reconnection electric field Ekl, a 10.7 radio flux Rms and an interplanetary magnetic field joint component B according to the following formula t Four spatial parameters (under the GSM coordinate system) are expressed as follows:
Ekl＝V x ×Btgm×sin 2 (θ/2)
because the time resolution of the SuperDARN radar data and the OMNI satellite data are inconsistent, the SuperDARN is 2 minutes, and the OMNI satellite data is 1 minute, the intersection of the SuperDARN radar data and the OMNI satellite data is selected according to the time, corresponding parameters are connected, and meanwhile invalid data of a flow graph are cut. The cutting process is as follows: for the flow chart data with latitude lower than 51 °, deletion processing is performed on the flow chart data due to a large measurement error of the potential value thereof.
Drawing a convection image module: and fitting an optimal curved surface by using a curved surface fitting method and drawing a convection image by an interpolation mode. Each convection image is a sequence with the length of 7059, but because the original data of the convection image is different from the size of the generated grid point coordinate matrix, the data of the corresponding point is generated in an interpolation mode, and then the convection image is drawn by using a curved surface fitting method, wherein the size of the convection image is 1201 x 1201.
A convection image overturning module: and calculating the slope and the corresponding inclination angle through the positions of the maximum potential point and the minimum potential point, and turning over the convection image. Determining the slope and the corresponding inclination angle of the maximum potential point and the minimum potential point through the positions of the maximum potential point and the minimum potential point; rotating the convection image with the origin as the center according to the angle of the inclination angle, interpolating by adopting a bilinear method during rotation, and cutting the rotated image to ensure that the size of the rotated image is equal to that of the original image; and then turning the rotated image left and right, rotating the turned image left and right by taking the original point as the center according to the reverse direction of the angle of the inclination angle, interpolating by a bilinear method during rotation, cutting the rotated image, and enabling the size of the rotated image to be equal to that of the original image to obtain a final turned convection image.
Drawing a reversed convection image module: and drawing the reversed convection image by using a curved surface fitting method. The size of each convection image is still 1201 x 1201, and the flipped convection image is drawn by using a curved surface fitting method.
And an evaluation module: the original image and the flipped image are evaluated by the relevant parameters. The method comprises the steps of evaluating an image by using four evaluation indexes, wherein the four evaluation indexes are respectively structure similarity SSIM, peak signal-to-noise ratio PSNR, root mean square error RMSE and average absolute error MAE; the formulas of SSIM, PSNR, RMSE and MAE are as follows:
SSIM＝[l(X,Y)] α ·[c(X,Y)] β ·[s(X,Y)] γ
wherein X represents an original image, and Y represents an inverted image;the mean value is used to estimate the luminance, ">Contrast is estimated by variance,/->The covariance is used to estimate the structural similarity, mu X ,μ Y Mean value of X and Y, sigma X ,σ Y Respectively representing standard deviations of X and Y; sigma (sigma) XY For covariance, C 1 ,C 2 ,C 3 Are all constant, and in order to avoid the case that the denominator is 0, C is usually taken 1 ＝(0.01*L) 2 ,C 2 ＝(0.03*L) 2 ,C 3 ＝C 2 2, l=255, α, β, γ > 0; m and n are the height and width of the image, MAX X Representation ofThe maximum number of bits of X is typically MAX X ＝2 n -1, n=8; in calculating SSIM and PSNR, the range of streaming image potential data is normalized to [0,255]。
And an evaluation module: and evaluating the asymmetry of the flow image by adopting an asymmetry index. Put forward an asymmetry index delta Asym The asymmetry of the flow image is evaluated,
wherein C is a constant coefficient, taking c=200, a 1 ,a 2 ,a 3 ,a 4 As the weight coefficient, the weight coefficient a 1 ,a 2 ,a 3 ,a 4 Obtained by an entropy method. The entropy method solving process is as follows: the entropy method is an assignment method for determining index weight coefficients according to the influence of the relative change degree of each index on the whole by calculating the information entropy of each index observation value. Firstly calculating the characteristic proportion and the corresponding entropy of each index observation value, then calculating the difference coefficient of the index, and finally obtaining the weight coefficient of the index; the larger the observed value difference of the index is, the larger the difference coefficient is, and the more important the index is; the weight coefficients corresponding to the four indexes are obtained by an entropy method and are respectively a 1 ＝0.0385,a 2 ＝0.1556,a 3 ＝0.3757,a 4 ＝0.4302。
In summary, the invention discloses a method for extracting the dissymmetry of a super DARN radar ionosphere convective image, which comprises the following steps: constructing a data set according to the SuperDARN radar observation data and the OMNI satellite observation data; fitting and interpolating by using a two-dimensional curved surface fitting method, and drawing Gao Wei ionosphere convection images; calculating the slope and the corresponding inclination angle of the maximum potential point and the minimum potential point through the positions of the maximum potential point and the minimum potential point, and drawing the overturned convection image by using a curved surface fitting method in a mode of rotating the image and overturning the image, so that the structural difference and the potential difference of the overturned image and the original image are obtained; a new asymmetry index is proposed to evaluate the asymmetry of the flow image. The invention extracts the dissymmetry of the ionized layer convection image based on the SuperDARN radar by utilizing image rotation and image overturning, and can describe the dissymmetry between two hemispheres of the convection image by a customer.

Claims (8)

1. A method for extracting the asymmetry of a super darn radar ionosphere convective image, comprising the steps of:
step 1: acquiring SuperDARN radar observation data and OMNI satellite observation data and preprocessing;
step 2: matching and aligning OMNI satellite observation data and SuperDARN radar observation data to form a data set;
step 3: fitting an optimal curved surface by using a curved surface fitting method and drawing a convection image by an interpolation mode;
step 4: calculating a slope and a corresponding inclination angle by the positions of the maximum potential point and the minimum potential point; rotating the convection image by taking the origin as the center according to the angle of the inclination angle, interpolating by adopting a bilinear method during rotation, and cutting the rotated image to ensure that the size of the rotated image is equal to that of the original image; the method comprises the steps of turning a rotated image left and right, rotating the turned image left and right by taking an origin as a center according to the reverse direction of the angle of inclination, interpolating by a bilinear method during rotation, cutting the rotated image, and enabling the size of the rotated image to be equal to that of an original image to obtain a final turned convection image;
step 5: drawing a reversed convection image by using a curved surface fitting method;
step 6: evaluating an original image and a turnover image through related parameters, wherein the related parameters are structural similarity SSIM, peak signal-to-noise ratio PSNR, root mean square error RMSE and average absolute error MAE;
step 7: using an asymmetry index delta Asym The asymmetry of the flow image was evaluated:
wherein C is a constant coefficient, taking c=200, a 1 ,a 2 ,a 3 ,a 4 As the weight coefficient, the weight coefficient a 1 ,a 2 ,a 3 ,a 4 Obtained by an entropy method.
2. The method of extracting the supersdarn radar ionospheric convective image asymmetry of claim 1, wherein: the pretreatment process in step 1 is as follows:
for OMNI satellite data, eliminating invalid data with the interplanetary magnetic field component greater than or equal to 9999, the solar wind speed greater than or equal to 99999 and the solar wind dynamic pressure greater than or equal to 99; for the SuperDARN radar data, data with the number of return points of the day greater than 300 and the number of flow charts of the day greater than 400 are selected.
3. The method of extracting the supersdarn radar ionospheric convective image asymmetry of claim 1, wherein: the matching process in step 2 is as follows:
step 2.1: acquiring an inter-satellite electric field IEF, inter-satellite magnetic field components By and Bz, a solar wind speed Vs, a solar wind dynamic pressure P, an Alzhi Mach index Ma and a geomagnetic index AE measured By an OMNI satellite;
step 2.2: generating a clock angle theta, a Kan-Lee reconnection electric field Ekl, a 10.7 radio flux Rms and an interplanetary magnetic field joint component B according to the following formula t Four spatial parameters, the formula is:
Ekl＝V x ×Btgm×sin 2 (θ/2)
step 2.3: and selecting an intersection of the SuperDARN radar data and the OMNI satellite data according to the time, connecting corresponding parameters, and cutting invalid data of the flow graph.
4. The method of extracting the supersdarn radar ionospheric convective image asymmetry of claim 1, wherein: in step 3, each convection image is a sequence with a length of 7059, data of corresponding points are generated through interpolation, and then the convection images are drawn through a curved surface fitting method, wherein the size of each convection image is 1201 x 1201.
5. The method of extracting the supersdarn radar ionospheric convective image asymmetry of claim 1, wherein: in step 5, the size of each convection image is 1201 x 1201, and the flipped convection image is drawn by using a curved surface fitting method.
6. The method of extracting the supersdarn radar ionospheric convective image asymmetry of claim 1, wherein: the specific process of the step 6 is as follows:
the method comprises the steps of evaluating an image by using four evaluation indexes, wherein the four evaluation indexes are respectively structure similarity SSIM, peak signal-to-noise ratio PSNR, root mean square error RMSE and average absolute error MAE; the formulas of SSIM, PSNR, RMSE and MAE are as follows:
SSIM＝[l(X,Y)] α ·[c(X,Y)] β ·[s(X,Y)] γ
wherein X represents an original image, and Y represents an inverted image;the average value is used to estimate the luminance,contrast is estimated by variance,/->The covariance is used to estimate the structural similarity, mu X ,μ Y Mean value of X and Y, sigma X ,σ Y Respectively representing standard deviations of X and Y; sigma (sigma) XY For covariance, C 1 ,C 2 ,C 3 All are constant, take C 1 ＝(0.01*L) 2 ,C 2 ＝(0.03*L) 2 ,C 3 ＝C 2 2, l=255, α, β, γ > 0; m and n are the height and width of the image, MAX X Represents the maximum number of bits of X, taking MAX X ＝2 n -1, n=8; in calculating SSIM and PSNR, the range of streaming image potential data is normalized to [0,255]。
7. The method of extracting the supersdarn radar ionospheric convective image asymmetry of claim 1, wherein: the entropy method solving process in the step 7 is as follows: firstly calculating the characteristic proportion and the corresponding entropy of each index observation value, then calculating the difference coefficient of the index, and finally obtaining the weight coefficient of the index; the weight coefficients corresponding to the four indexes are obtained by an entropy method and are respectively a 1 ＝0.0385,a 2 ＝0.1556,a 3 ＝0.3757,a 4 ＝0.4302。
8. A system for extracting the asymmetry of a SuperDARN radar ionosphere convective image, comprising the following modules:
and the data acquisition and preprocessing module is used for: the method comprises the steps of acquiring and preprocessing SuperDARN radar observation data and OMNI satellite observation data;
the data matching and matching module is used for: the method comprises the steps of matching and aligning OMNI satellite observation data and SuperDARN radar observation data to form a data set;
drawing a convection image module: fitting an optimal curved surface by using a curved surface fitting method and flowing the image by an interpolation mode;
a convection image overturning module: calculating the slope and the corresponding inclination angle through the positions of the maximum potential point and the minimum potential point, rotating the convection image with the origin as the center according to the angle of the inclination angle, interpolating by a bilinear method during rotation, and cutting the rotated image to ensure that the size of the rotated image is equal to that of the original image; the method comprises the steps of turning a rotated image left and right, rotating the turned image left and right by taking an origin as a center according to the reverse direction of the angle of inclination, interpolating by a bilinear method during rotation, cutting the rotated image, and enabling the size of the rotated image to be equal to that of an original image to obtain a final turned convection image;
drawing a reversed convection image module: drawing a reversed convection image by using a curved surface fitting method;
and an evaluation module: evaluating an original image and a turnover image through related parameters, wherein the related parameters are structural similarity SSIM, peak signal-to-noise ratio PSNR, root mean square error RMSE and average absolute error MAE;
and an evaluation module: using an asymmetry index delta Asym The asymmetry of the flow image was evaluated:
wherein C is a constant coefficient, taking c=200, a 1 ,a 2 ,a 3 ,a 4 As the weight coefficient, the weight coefficient a 1 ,a 2 ,a 3 ,a 4 By passing throughAnd obtaining by an entropy method.
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