CN105404897A - Method for cleaning solar photosphere bright spot in astronomical image - Google Patents
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Abstract
The present invention relates to a method for cleaning a solar photosphere bright spot in an astronomical image and belongs to the fields of astronomical technique, image processing and data mining. The method comprises the steps of: firstly, identifying a bright spot by using a method that combines a Laplacian filter and morphological dilation and tracking the bright spot by using a method of three-dimensional communication; then, extracting a plurality of eigenvalues, with a low correlation. of the bright spot, and after the eigenvalue data is standardized, performing dimension reduction according to a contribution rate by using a principal component analysis method; and finally, cleaning the bright spot data subjected to dimension reduction by using a DBSCAN method so as to remove a non-bright spot structure. According to the method for cleaning the solar photosphere bright spot in the astronomical image provided by the present invention, it is unprecedented and effective to apply the DBSCAN method to cleaning the solar photosphere bright spot data, and the method largely solves the problem of error identification existing in a traditional identification method of solar photosphere bright spot. Meanwhile, more accurate bright spot data is cleaned for small-scale magnetic field researches and further researches on problems such as corona heating.
Description
Technical field
The present invention relates to a kind of method of cleaning solar photosphere bright spot in astronomic graph picture, belong to astronomical technology, image procossing and Data Mining.
Background technology
Solar photosphere surface has been covered with rice granular structure, and the structure having some shinny in the dark footpath of the grain of rice, is called photosphere bright spot.Generally believe, photosphere bright spot and solar magnetic field have substantial connection, the research of solar magnetic field can be promoted by research photosphere bright spot, promote the research of the Solar Physics phenomenon such as plasma and Coronal heating of more deep layer and Geng Re, thus forecast that solar magnetic field is movable, to carry out space weather forecast and the forecast to magnetic field of the earth better in advance.
Identify that bright spot mainly have employed the several methods such as threshold method, region-growing method and morphology at present on 2d.The gray level of image is divided into several part by arranging one or several threshold value by threshold method, thinks that the pixel belonging to same part is same object; Region-growing method is from prime area, to have adjacent in ejusdem generis pixel or other region merge region up till now thus progressively growth region, until do not have can till the point of merger or other zonule; Morphology goes the correspondingly-shaped measured and extract in image to reach the object to graphical analysis and identification with the structural element with certain form.But some shinny broken rices are all inevitably mistakenly identified as bright spot by these methods in identification.
Data cleansing is the emerging technology occurred along with the development of data mining, refers to and to find from data centralization and to correct " dirty data ", from data file, namely detect mistake and inconsistent data, and rejecting or revise them, to improve the quality of data.Clustering method is just very suitable for data cleansing.Clustering method is the statistical analysis technique of group research object being divided into relative homogeneity.Its objective is the relation found between data, similar is classified as a class, and different is a class.Wherein, the DBSCAN method of density based is a kind of clustering method being applicable to large data collection efficiently.
Summary of the invention
The invention provides a kind of method of cleaning solar photosphere bright spot in astronomic graph picture, DBSCAN clustering method in data mining is applied to the data cleansing of photosphere bright spot, clean out non-highlight structure, for solve tional identification bright spot method in there is the problem by mistake identified.
The method that the present invention cleans solar photosphere bright spot in astronomic graph picture is achieved in that
First pre-service is carried out to sequence image, then adopt a kind of method that Laplce's filtering and morphological dilations combine to identify bright spot; Bright spot is followed the tracks of again by the method for three-dimensional UNICOM; Then the data of the lower eigenwert of multiple degrees of correlation of bright spot as cleaning are extracted; Then, z-score standardization is done to the data of cleaning; Adopt principal component analysis (PCA), according to contribution rate, the standardized data of higher-dimension is carried out dimensionality reduction again; Finally, by DBSCAN method, the data after dimensionality reduction are cleaned, thus reject non-highlight structure.
In described cleaning astronomic graph picture, the concrete steps of the method for solar photosphere bright spot are as follows:
Step1, bright spot data identification; First pre-service is carried out to sequence image, comprise the alignment schemes aligned sequence image adopting maximum local correlation, normalization sequence image, smoothed image; Then the photosphere bright spot of every width figure in the method recognition sequence image adopting Laplce's filtering and morphological dilations to combine;
Step2, bright spot data tracking; After each width figure of sequence image identifies photosphere bright spot, adopt three-dimensional method for communicating to carry out three-dimensional to photosphere bright spot in the three-dimensional space-time cube be made up of all bright spots identified in sequence image again to follow the tracks of, wherein, three-dimensional space-time cube (x, y, t) in, x and y is the coordinate of two-dimensional image, and t is time coordinate;
Step3, bright spot data characteristics are extracted; Identify and follow the tracks of after bright spot, the degree of correlation extracting bright spot is lower and can represent the data of multiple eigenwerts as cleaning of each side such as the optical strength of bright spot, form and motion;
Step4, bright spot data normalization; After obtaining cleaning data, adopt z-score methodological standardization bright spot data, turning to average by data standard is 0, and variance is the normal distribution of 1;
Step5, bright spot Data Dimensionality Reduction; Adopt principal component analysis (PCA) to carry out dimension-reduction treatment to the cleaning data after standardization, select the dimension that can be down to according to contribution rate;
Step6, bright spot data cleansing; Data acquisition DBSCAN clustering method after dimensionality reduction is cleaned, thus rejects non-highlight structure.
Further preferred, the ratio that the data attribute of the cleaning of described step Step3 comprises equivalent diameter, intensity, excentricity, bright spot edge are in dark footpath, speed, mode of motion and coefficient of diffusion eigenwert.
Preferred further, the contribution rate of described step Step5 adopts more than 90%.
The invention has the beneficial effects as follows:
Clustering method can find the relation between data, and similar is classified as a class, and different is classified as a class; The DBSCAN method of density based not only has stronger noise resisting ability strong, can also process the cluster of arbitrary shape.Cleaning problem DBSCAN method being applied to photosphere bright spot data is unprecedented, effective, there is the problem by mistake identified in the recognition methods that the method largely solves traditional photosphere bright spot; Meanwhile, for data more are accurately cleaned out in the research of the problem such as Magnetic Field Research and further Coronal heating of small scale.
Accompanying drawing explanation
Fig. 1 is the overview flow chart that the present invention cleans the method for solar photosphere bright spot;
Fig. 2 is the full resolution pricture that the present invention is observed at G-band by sunrise/Solar Optical Telescope;
Fig. 3 is the result figure that the bright spot of the method identification that the present invention's Laplce's filtering and morphological dilations technology combine marks in former figure;
Fig. 4 is the graph of a relation that bright spot data principal component analysis (PCA) of the present invention analyzes contribution rate and major component, and wherein: horizontal ordinate representation dimension, ordinate represents contribution rate;
Fig. 5 is the result figure of the present invention DBSCAN method cleaning, and wherein, X, Y, Z axis represents the three-dimensional data after dimensionality reduction respectively; Solid round in figure represents bright spot, and rice font represents non-highlight structure.
Embodiment
Embodiment 1: as Figure 1-5, a kind of method of cleaning solar photosphere bright spot in astronomic graph picture, first carries out pre-service to sequence image, then adopts a kind of method that Laplce's filtering and morphological dilations combine to identify bright spot; Bright spot is followed the tracks of again by the method for three-dimensional UNICOM; Then the data of the lower eigenwert of multiple degrees of correlation of bright spot as cleaning are extracted; Then, z-score standardization is done to the data of cleaning; Adopt principal component analysis (PCA), according to contribution rate, the standardized data of higher-dimension is carried out dimensionality reduction again; Finally, by DBSCAN method, the data after dimensionality reduction are cleaned, thus reject non-highlight structure.
In described cleaning astronomic graph picture, the concrete steps of the method for solar photosphere bright spot are as follows:
Step1, bright spot data identification; First pre-service is carried out to sequence image, comprise the alignment schemes aligned sequence image adopting maximum local correlation, normalization sequence image, smoothed image; Then the photosphere bright spot of every width figure in the method recognition sequence image adopting Laplce's filtering and morphological dilations to combine;
Step2, bright spot data tracking; After each width figure of sequence image identifies photosphere bright spot, adopt three-dimensional method for communicating to carry out three-dimensional to photosphere bright spot in the three-dimensional space-time cube be made up of all bright spots identified in sequence image again to follow the tracks of, wherein, three-dimensional space-time cube (x, y, t) in, x and y is the coordinate of two-dimensional image, and t is time coordinate;
Step3, bright spot data characteristics are extracted; Identify and follow the tracks of after bright spot, the degree of correlation extracting bright spot is lower and can represent the data of multiple eigenwerts as cleaning of each side such as the optical strength of bright spot, form and motion;
Step4, bright spot data normalization; After obtaining cleaning data, adopt z-score methodological standardization bright spot data, turning to average by data standard is 0, and variance is the normal distribution of 1;
Step5, bright spot Data Dimensionality Reduction; Adopt principal component analysis (PCA) to carry out dimension-reduction treatment to the cleaning data after standardization, select the dimension that can be down to according to contribution rate;
Step6, bright spot data cleansing; Data acquisition DBSCAN clustering method after dimensionality reduction is cleaned, thus rejects non-highlight structure.
Further preferred, the ratio that the data attribute of the cleaning of described step Step3 comprises equivalent diameter, intensity, excentricity, bright spot edge are in dark footpath, speed, mode of motion and coefficient of diffusion eigenwert.
Embodiment 2: as Figure 1-5, a kind of method of cleaning solar photosphere bright spot in astronomic graph picture, first carries out pre-service to sequence image, then adopts a kind of method that Laplce's filtering and morphological dilations combine to identify bright spot; Bright spot is followed the tracks of again by the method for three-dimensional UNICOM; Then the data of the lower eigenwert of multiple degrees of correlation of bright spot as cleaning are extracted; Then, z-score standardization is done to the data of cleaning; Adopt principal component analysis (PCA), according to contribution rate, the standardized data of higher-dimension is carried out dimensionality reduction again; Finally, by DBSCAN method, the data after dimensionality reduction are cleaned, thus reject non-highlight structure.
In described cleaning astronomic graph picture, the concrete steps of the method for solar photosphere bright spot are as follows:
Step 1, bright spot data identification; First with alignment schemes aligned sequence image (in this example, this sequence image derives from the full resolution pricture that sunrise/Solar Optical Telescope observes at G-band, has 758 width figure), normalization sequence image, the smoothed image of maximum local correlation; Then respectively Laplce's filtering is carried out to each width figure in sequence image and by threshold method preliminary election bright spot; The edge finally adopting morphological dilations method to calculate the bright spot of each preliminary election is in the ratio in dark footpath, and the edge filtering out 70% is arranged in the bright spot in dark footpath.As the first panel height resolution image in Fig. 2 this sequence image that to be sunrise/Solar Optical Telescope observe at G-band, Fig. 3 shows the result that bright spot that this step identifies marks at Fig. 2, position as shown in white point in figure;
Step 2, bright spot data tracking; By the bright spot structure three-dimensional space-time cube identified in whole sequence image, with 26 field method for communicating, photosphere bright spot is followed the tracks of.Wherein, in the cubical three-dimensional system of coordinate of three-dimensional space-time (x, y, t), x and y is the planimetric coordinates of two dimensional image, and t is the time coordinate of sequence image.
26 field method for communicating are described below: suppose that p and q is two points in three-dimensional cube, be expressed as: p=(p
x; p
y; p
z), q=(q
x; q
y; q
z), if p and q meets simultaneously | p
x-qx|≤1, | p
y-q
y|≤1 He | p
z-q
z|≤1, then claim p and q to meet 26 field UNICOMs.Pixel UNICOM on 26 adjacent positions of target pixel points on three dimension directions can be a region by 26 UNICOMs, so be a kind of better mode following the tracks of the evolution of bright spot in three-dimensional space-time cube;
The eigenwert of step 3, extraction bright spot.The ratio that the eigenwert extracted comprises the equivalent diameter of bright spot, intensity, excentricity, bright spot edge are in dark footpath, speed, mode of motion and coefficient of diffusion.These eigenwerts as cleaning bright spot according to being relatively rational because these seven attributes correlations are low, and many-sided features such as the optical strength of bright spot, form and motion can be represented.Seven eigenwerts are defined as follows:
Equivalent diameter: using all pixels corresponding for each bright spot as area, is equivalent to circle and is calculated its equivalent diameter.
Maximum intensity compares: the intensity describing bright spot by the maximum intensity of bright spot divided by the mean intensity of view picture figure.
Excentricity: the shape describing bright spot by the distance between oval bifocal divided by long axis length.Excentricity is larger, illustrate more be partial to long oval, otherwise then illustrate and be more partial to circle.
Bright spot edge is in the ratio in dark footpath: a key property of bright spot is that it is positioned at the dark footpath of the darker grain of rice, therefore extracts the ratio that each bright spot edge is positioned at dark footpath.
Speed: calculate the displacement between every two frames, to calculate the speed of bright spot according to the centroid position of bright spot within its lifetime on each two field picture.
Mode of motion: defining variable m, m=Displacement/TotalLength.Its implication is that displacement (Displacement) is divided by movement locus length and (TotalLength).Displacement is the displacement between photosphere bright spot head and the tail barycenter, and its formula is as follows:
Wherein, X (1) and Y (1) is for bright spot is in the center-of-mass coordinate of start frame image, and X (n) and Y (n) is for bright spot is in the center-of-mass coordinate of end frame image.
Movement locus length and be displacement sum between every two two field pictures, its formula is as follows:
Wherein, X (t) and Y (t) is for bright spot is in the center-of-mass coordinate of t two field picture, and X (t+1) and Y (t+1) is for bright spot is in the center-of-mass coordinate of t+1 two field picture.
According to definition, m can be used for the movement locus of quantitative description bright spot, and its value scope is 0 to 1.If m equals 1, then mean that the movement locus of bright spot is straight line; If m equals 0, then represent that bright spot gets back to initial point again from starting point.Close to 1, m more means that bright spot is being moved along the track close to straight line, more close to 0,
Then mean that the track of bright spot is similar to the motion of pitch of the laps.
Coefficient of diffusion: coefficient of diffusion describes the diffusion area of bright spot and the relation of time, and it is defined as (Δ l)
2=C τ
γ, wherein (Δ l)
2the position of any time and square displacement of initial position in the lifetime that represent bright spot, γ is coefficient of diffusion, and τ is the lifetime of bright spot.Diffusion coefficient value is larger, and mean that the area spread within the unit interval is larger, vice versa;
Step 4, bright spot data normalization; After obtaining cleaning data, adopt z-score methodological standardization data, make it meet standardized normal distribution, namely average is 0, and standard deviation is 1.In seven eigenwerts, only need to carry out standardization to equivalent diameter, intensity, speed and coefficient of diffusion, because the ratio that excentricity, mode of motion and edge are positioned at dark footpath has been all nondimensional data.Respectively to equivalent diameter, intensity, speed and coefficient of diffusion computation of mean values and standard deviation, then carry out standardization with formula as follows:
Wherein x is data to be normalized, and μ is average, and σ is standard deviation, x
*for the data after standardization;
Step 5, bright spot Data Dimensionality Reduction; Principal component analysis (PCA) is adopted to carry out dimensionality reduction to seven of bright spot eigenwerts.Principal component analysis (PCA) a kind of utilizes the internal association structure of characteristic without supervision feature extraction dimension reduction method, by linear transformation the characteristic of multidimensional is transformed to that dimension is less comprises original feature most information and separate characteristic.Because various features data do not exist artificial association, the result of cleaning can be made more reasonable.The reduction process of principal component analysis (PCA) is described below:
In principal component analysis (PCA), seven eigenwerts are called 7 degree of freedom data.First a characteristic matrix is constructed, shown in following formula by 7 eigenwerts of the bright spot after standardization:
Wherein, p represents the number of bright spot, the dimension of n representation feature data, X
nprepresent the n dimension data of p bright spot, X is characteristic matrix.Then calculate the covariance matrix of characteristic, obtain the relation between each dimension data; Calculate proper vector and eigenwert by covariance matrix, by descending for eigenwert arrangement, the importance information providing composition selects dimensionality reduction number of targets k, is finally multiplied by raw data matrix with the front k row of covariance matrix, namely obtains the data matrix after dimensionality reduction.Wherein, the selection of k is determined by analyzing contribution rate, contribution rate represents that the major significance that defined major component is born in whole data analysis accounts for great proportion, when getting a front k major component and replacing original all variablees, the size of contribution rate of accumulative total has reacted the reliability of this replacement, contribution rate of accumulative total is larger, and reliability is larger; Otherwise then reliability is less.
By principal component analysis (PCA) as shown in Figure 4, wherein abscissa line represents dimension to characteristic after 7 standardization of bright spot, and the longitudinal axis represents contribution rate, and column represents the contribution rate of every one dimension, and curve represents contribution rate of accumulative total.As can be seen from the figure, the contribution rate of the first dimension is the 46%, second dimension is 25%, and the third dimension is 19%.When two dimension is down in selection, its contribution rate is accumulative reaches 71%, reaches 90% when being down to three-dimensional.The contribution rate of 90% means that the data of this three-dimensional can represent the meaning of raw data 90%, and therefore 7 degree of freedom data selection is down to three-dimensional by us;
Step 6, bright spot data cleansing; Adopt DBSCAN clustering method cleaning bright spot data.DBSCAN is a kind of density clustering method, the method have enough highdensity Region dividing for bunch, and can find the cluster of arbitrary shape, its definition bunch is a maximum set for the point of density based.Need to do to give a definition before describing the method:
1 (ε-neighborhood)--the region in given object radius ε is called the ε-field of this object in definition.
Definition 2 (kernel objects) if--the ε-field of an object at least comprises a minimal amount MinPts object, then claim this object to be kernel object.
Definition 3 (direct density can reach)--a given object set D, if p is in the ε-neighborhood of q, and q is a kernel object, and we say that object p is that direct density can reach from object q.
Definition 4 (density can reach) if--have a data object sequence p
1, p
2..., p
n∈ D, wherein p
1=q, p
n=p, and p
i+ 1 can reach from the direct density of pi, then claim p can reach from q about ε and MinPts density.
Definition 5 (density be connected) if--there is a data object O and p and q can be reached about ε and MinPts density from O, then title p with q is connected about ε with MinPts density.
The flow process of DBSCAN method is described below: first by checking that the ε-neighborhood of each point in database finds cluster.If the ε-neighborhood of a some p includes more than MinPts point, then build using p as kernel object new a bunch.Then, DBSCAN finds the object that can reach from the direct density of these kernel objects repeatedly, and some density can reach and bunch to merge simultaneously.When not having new point can be added to any bunch, this process terminates.
As shown in Figure 5, wherein, X, Y and Z axis represent the coordinate range of three characteristics after dimensionality reduction to the result of DBSCAN method cleaning respectively, solidly roundly represent bright spot, and rice font represents non-highlight structure.As can be seen from the figure, the non-highlight structure of rice font representative is obviously away from bright spot in three-dimensional plot, reaches the object of cleaning bright spot data, effectively eliminates non-highlight structure.Bright spot data more are accurately cleaned out in the research that Magnetic Field Research for small scale studies the problems such as Coronal heating further.
By reference to the accompanying drawings the specific embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned embodiment, in the ken that those of ordinary skill in the art possess, various change can also be made under the prerequisite not departing from present inventive concept.
Claims (4)
1. clean a method for solar photosphere bright spot in astronomic graph picture, it is characterized in that: first adopt a kind of method that Laplce's filtering and morphological dilations combine to identify bright spot; Bright spot is followed the tracks of again by the method for three-dimensional UNICOM; Then the data of the lower eigenwert of multiple degrees of correlation of bright spot as cleaning are extracted; Then, z-score standardization is done to the data of cleaning; Adopt principal component analysis (PCA) that the data after standardization are reduced dimension according to contribution rate again; Finally, by DBSCAN method, the data after dimensionality reduction are cleaned, thus reject non-highlight structure.
2. the method for solar photosphere bright spot in cleaning astronomic graph picture according to claim 1, is characterized in that:
In described cleaning astronomic graph picture, the concrete steps of the method for solar photosphere bright spot are as follows:
Step1, bright spot data identification; First pre-service is carried out to sequence image, comprise the alignment schemes aligned sequence image adopting maximum local correlation, normalization sequence image, smoothed image; Then the photosphere bright spot of every width figure in the method recognition sequence image adopting Laplce's filtering and morphological dilations to combine;
Step2, bright spot data tracking; After each width figure of sequence image identifies photosphere bright spot, adopt three-dimensional method for communicating to carry out three-dimensional to photosphere bright spot in the three-dimensional space-time cube be made up of all bright spots identified in sequence image again to follow the tracks of, wherein, three-dimensional space-time cube (x, y, t) in, x and y is the coordinate of two-dimensional image, and t is time coordinate;
Step3, bright spot data characteristics are extracted; Identify and follow the tracks of after bright spot, the degree of correlation extracting bright spot is lower and can represent the data of multiple eigenwerts as cleaning of each side such as the optical strength of bright spot, form and motion;
Step4, bright spot data normalization; After obtaining cleaning data, adopt z-score methodological standardization bright spot data, turning to average by data standard is 0, and variance is the normal distribution of 1;
Step5, bright spot Data Dimensionality Reduction; Adopt principal component analysis (PCA) to carry out dimension-reduction treatment to the cleaning data after standardization, select the dimension that can be down to according to contribution rate;
Step6, bright spot data cleansing; Data acquisition DBSCAN clustering method after dimensionality reduction is cleaned, thus rejects non-highlight structure.
3. the method for solar photosphere bright spot in cleaning astronomic graph picture according to claim 2, is characterized in that: the ratio that the data attribute of the cleaning of described step Step3 comprises equivalent diameter, intensity, excentricity, bright spot edge are in dark footpath, speed, mode of motion and coefficient of diffusion eigenwert.
4. the method for solar photosphere bright spot in cleaning astronomic graph picture according to claim 2, is characterized in that: the contribution rate of described step Step5 adopts more than 90%.
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| CN111127512A (en) * | 2019-11-29 | 2020-05-08 | 昆明理工大学 | Method for detecting and tracking solar activity area in full-sun image |
| CN111127512B (en) * | 2019-11-29 | 2022-06-07 | 昆明理工大学 | Method for detecting and tracking solar activity area in full-sun image |
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