Ocean satellite remote sensing data processing method and device and storage medium

Abstract

The invention relates to the technical field of ocean remote sensing data processing, in particular to an ocean satellite remote sensing data processing method and device and a storage medium, and the method comprises the steps: collecting ocean vortex data; identifying a mesoscale vortex based on the ocean vortex data, and filtering the flow field data of the mesoscale vortex; analyzing a sub-mesoscale process based on the filtered flow field data of the mesoscale vortex and the vortex image; constructing a frontal surface identification model based on the thermal inductance image and the flow field data of the mesoscale vortex so as to identify the frontal surface of the mesoscale vortex; classifying the sub-mesoscale vortexes according to the sub-mesoscale process and the flow field data and the height data of the mesoscale vortexes after filtering processing; constructing a vortex data set based on classification of sub-mesoscale vortexes, and optimizing an analysis process of a sub-mesoscale process; and outputting the classification of the sub-mesoscale vortex. According to the method, accurate classification analysis of sub-mesoscale vortexes is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean remote sensing data processing, and in particular to a method, device and storage medium for processing ocean satellite remote sensing data. Background Art

[0002] For mesoscale vortices, the main traditional identification methods include the SLA closed contour method, the OW parameter method, the VG fluid geometry algorithm, and the winding-angle algorithm; for mesoscale fronts, the gradient features are mainly extracted based on the Sobel operator. However, due to the artificial design of features or the expert-set thresholds, the characteristics of vortices and fronts cannot be accurately expressed, which greatly affects the identification accuracy and detection efficiency of mesoscale dynamic processes.

[0003] Chinese Patent Publication No.: CN110097075A discloses a method for classification and identification of ocean mesoscale eddies based on deep learning, including model construction, backward processing, and model algorithm adjustment. First, model construction adopts a variety of training strategies, based on the convolutional neural network algorithm, to train the mesoscale eddy classification model, establish an eddy classification model, and achieve efficient classification of mesoscale eddies. Second, backward processing, according to the probability density map output by the model, locate high-probability eddy pixels, merge and remove duplicate eddy images, and recycle incorrectly classified data. Finally, the model algorithm is adjusted, the incorrectly classified data is added to the training data set to retrain the model, establish a recognition model, and finally determine the location of the sea area where the eddy is located. This method realizes the identification of mesoscale eddies, but does not realize the analysis of sub-mesoscale processes. There are problems of low efficiency in the analysis of sub-mesoscale processes and inaccurate analysis of sub-mesoscale eddies. Summary of the Invention

[0004] The object of the present invention is to provide a method, device and storage medium for processing ocean satellite remote sensing data to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for processing ocean satellite remote sensing data, comprising:

[0007] Collect ocean eddy data;

[0008] Identify mesoscale vortices based on ocean vortex data and filter the flow field data of mesoscale vortices;

[0009] Analyze submesoscale processes based on filtered flow field data and vortex images of mesoscale vortices;

[0010] A front recognition model is constructed based on thermal images and flow field data of mesoscale vortices to identify the front of mesoscale vortices.

[0011] Submesoscale vortices are classified based on submesoscale processes, flow field data of filtered mesoscale vortices, and height data.

[0012] Construct an eddy dataset based on the classification of submesoscale eddies and optimize the analysis of submesoscale processes;

[0013] Outputs the classification of submesoscale eddies.

[0014] Furthermore, the ow parameter and ow threshold are calculated based on the flow field data and height data, and the ow parameter and ow threshold are compared to identify mesoscale vortices. If the ow parameter is greater than or equal to the ow threshold, the ocean vortex currently being analyzed is determined to be a non-mesoscale vortex; otherwise, the ocean vortex currently being analyzed is determined to be a mesoscale vortex.

[0015] Furthermore, starting from the upper left corner of the vortex image, the vortex image within the preset identification frame is sequentially analyzed by moving 1 pixel at a time, the average grayscale value of the vortex image within the preset identification frame is calculated, and the grayscale value of each pixel in the preset identification frame is corrected according to the grayscale value of each pixel of the vortex image within the preset identification frame and the average grayscale value, and the image composed of the pixels in the preset identification frame after correction is used as the feature image. If the grayscale value of the pixel of the vortex image within the preset identification frame is less than the average grayscale value of the vortex image within the preset identification frame, the grayscale value of the currently analyzed pixel is reduced by α; otherwise, the grayscale value of the currently analyzed pixel is increased by α; wherein α represents a grayscale adjustment parameter;

[0016] Vortex pixels are extracted based on the grayscale value of the feature image pixels. If the grayscale value of the feature image pixel is greater than or equal to the feature grayscale threshold, the current analysis feature pixel is extracted as the vortex pixel; otherwise, the current analysis feature pixel is not extracted.

[0017] Furthermore, the Rossby number is analyzed based on the flow field data of the vortex pixel points. The expression of the Rossby number is as follows:

[0018]

[0019] Where Ro represents the Rossby number, U represents the characteristic velocity, L represents the number of vortex pixels, c represents the number of vortex pixels, u(c) represents the latitudinal velocity of the vortex pixel, and v(c) represents the longitudinal velocity of the vortex pixel.

[0020] The feature image analysis process is iterated according to the Rossby number and the size of the preset recognition frame. When 0.8≤Ro≤1.2 and j>0, the feature image analysis process is iterated, j represents the size of the preset recognition frame, and the size of the preset recognition frame is reduced according to the number of iterations, so that the reduction amount of the preset recognition frame size is equal to 2×the number of iterations;

[0021] The sub-mesoscale process is analyzed based on the Rossby number. When the Rossby number satisfies ro1≤Ro≤ro2, it is determined that a sub-mesoscale process exists in the current mesoscale vortex; where ro1 represents the first Rossby number threshold and ro2 represents the second Rossby number threshold.

[0022] Furthermore, the grayscale values ​​of the pixels in the thermal image are compared with those of the adjacent pixels to identify the front, and the pixels satisfying G1(x1,y1) / G1(x2,y2)<β1 or G1(x1,y1) / G1(x2,y2)>β2 are extracted as the front pixels, and the set of the front pixels is taken as the front, where (x1,y1) represents the coordinates of the current analysis pixel in the thermal image, (x2,y2) represents the coordinates of the pixels adjacent to the current analysis pixel in the thermal image, G1 represents the grayscale value of the pixel, β1 represents the first thermal grayscale threshold, and β2 represents the second thermal grayscale threshold.

[0023] Furthermore, the thermal grayscale threshold is updated based on the flow field data of the filtered mesoscale vortex to update the front recognition model. When u(x3,y3)×u(x2,y2)<0 or v(x3,y3)×v(x2,y2)<0, the thermal grayscale threshold is updated to reduce the first thermal grayscale threshold by α and increase the second thermal grayscale threshold by α, where (x3,y3) represents the coordinates of the front pixel point.

[0024] The Rossby number threshold is adjusted according to the number of front pixels and the number of vortex image pixels. When Nx3 / [2×(X+Y)]≤γ, the Rossby number threshold is adjusted to reduce the first Rossby number threshold by α and increase the second Rossby number threshold by α; where Nx3 represents the number of front pixels and γ represents the front threshold.

[0025] Furthermore, the vortex direction coordinates are extracted based on the flow field data of the mesoscale vortex after filtering, and the coordinates satisfying u(x,y)=0 and v(x,y)≠0 or v(x,y)=0 and u(x,y)≠0 are extracted as the vortex direction coordinates;

[0026] Submesoscale vortices are classified according to vortex direction coordinates, submesoscale processes and height data. The classification of submesoscale vortices includes cyclones and anticyclones.

[0027] Furthermore, the vortex diameter is calculated based on the vortex direction coordinates. The expression of the vortex diameter is: q = {[(x4 max -x4 min ) 2 +(y4 xmax -y4 xmin ) 2 ] 1 / 2 +[(y4max -y4 min ) 2 +(x4 ymax -x4 ymin ) 2 ] 1 / 2} / 2, where q represents the vortex diameter, y4 xmax Indicates that the horizontal coordinate is x4 max The vertical coordinate at time y4 xmin Indicates that the horizontal coordinate is x4 min The vertical coordinate at time, x4 ymax Indicates that the vertical coordinate is y4 max The horizontal coordinate at time, x4 ymin Indicates that the vertical coordinate is y4 min The horizontal axis of time;

[0028] The Rossby number threshold adjustment process is optimized based on the vortex data set to optimize the analysis process of submesoscale processes. last When the Rossby number threshold is optimized, the adjustment of the Rossby number threshold is made so that the optimized first Rossby number threshold is equal to the adjusted first Rossby number threshold × q last / μ(q|q∈Q), so that the optimized second Rossby number threshold is equal to the adjusted second Rossby number threshold ×μ(q|q∈Q) / q last ; where μ() represents the average value of the data in brackets, Q represents the vortex data set, and q last Indicates the vortex diameter obtained in the last analysis.

[0029] On the other hand, the present invention also provides a device for processing ocean satellite remote sensing data, comprising:

[0030] Data acquisition module, used to collect ocean eddy data;

[0031] A vortex identification module is used to identify mesoscale vortices based on ocean vortex data and filter the flow field data of mesoscale vortices;

[0032] The vortex analysis module is used to analyze sub-mesoscale processes based on the flow field data and vortex images of filtered mesoscale vortices;

[0033] A front recognition module is used to build a front recognition model based on thermal images and flow field data of mesoscale vortices to identify the front of the mesoscale vortex;

[0034] The vortex classification module is used to classify submesoscale vortices based on submesoscale processes, the flow field data of filtered mesoscale vortices, and the height data;

[0035] Build an optimization module to construct eddy datasets based on the classification of submesoscale eddies and optimize the analysis of submesoscale processes;

[0036] Data output module, used to output the classification of submesoscale vortices.

[0037] On the other hand, the present invention also provides a storage medium, characterized in that it stores instructions, which, when run on a computer, enable the computer to execute the method for processing ocean satellite remote sensing data as described in any one of the above.

[0038] The beneficial effects of the present invention are as follows: through multi-dimensional data processing and dynamic optimization mechanism, the accuracy and efficiency of sub-mesoscale vortex analysis are significantly improved. By adopting the OW parameter method in combination with flow field and height data to identify mesoscale vortices, and eliminating high-fluctuation noise through the distance filtering formula, a high-quality data foundation is provided for sub-mesoscale analysis. By dynamically adjusting the recognition frame size based on the Rossby number and combining the dual threshold design of the thermal image front recognition model, dynamic capture of front features and real-time updating of the model are achieved to solve the problem of traditional threshold setting deviation. By integrating multiple parameters such as vortex coordinates, hemispherical position, height data and flow direction, cyclone and anticyclone classification rules are constructed to ensure the physical rationality of sub-mesoscale vortex classification. The vortex diameter is used to construct a data set, and the Rossby number threshold is optimized through mean comparison feedback to continuously improve the detection accuracy of sub-mesoscale processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 Flowchart of the method for processing ocean satellite remote sensing data in this embodiment.

[0041] Figure 2 Flowchart of the method for identifying medium-scale vortices in this embodiment.

[0042] Figure 3 Flowchart of the analysis method of the sub-mesoscale process in this embodiment.

[0043] Figure 4 This is a flow chart of the method for constructing the front identification model of this embodiment.

[0044] Figure 5 Flowchart of the optimization method for the sub-mesoscale process of this embodiment.

[0045] Figure 6Schematic diagram of the structure of the apparatus for processing ocean satellite remote sensing data according to this embodiment. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the following detailed description is illustrative rather than restrictive and should not be used to limit the scope of protection of the present invention.

[0047] It should be noted that, although the terms "first," "second," and "third" may be used to describe the embodiments of the present application, the description should not be limited to these terms. These terms are merely used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, "first" may also be referred to as "second," and similarly, "second" may also be referred to as "first."

[0048] See also Figure 1 As shown in FIG, the method for processing ocean satellite remote sensing data of this embodiment includes:

[0049] Step S1, collecting ocean vortex data, the ocean vortex data includes flow field data, height data, thermal images and vortex images, the flow field data includes longitudinal flow velocity and latitudinal flow velocity, the longitudinal flow velocity refers to the flow velocity in the vortex flow velocity parallel to the longitudinal direction, the positive and negative of the longitudinal flow velocity indicates the direction of the flow velocity on the meridian, pointing to the North Pole is positive, and pointing to the South Pole is negative, the latitudinal flow velocity refers to the flow velocity in the vortex flow velocity parallel to the latitude direction, the positive and negative of the latitudinal flow velocity indicates the direction of the flow velocity on the latitude, pointing to the east is positive, and pointing to the west is negative, the thermal image is a thermal imaging image, different colors are used to represent the temperature conditions of each part of the vortex image, and the temperature gradient should be less than or equal to 0.5°C, and the flow field data, height data, thermal images and vortex images are collected by obtaining data transmitted by high-resolution and high-frequency ocean satellites.

[0050] Please continue reading Figure 1 As shown, the method for processing ocean satellite remote sensing data also includes:

[0051] Step S2: Identify mesoscale vortices based on the ocean vortex data and perform filtering on the flow field data of the mesoscale vortex.

[0052] See also Figure 2 As shown in FIG, it is a method for identifying mesoscale vortices, including:

[0053] Step S21: identifying the vortex center based on the flow field data.

[0054] Specifically, in step S21 described in this embodiment, the longitudinal flow velocity and latitudinal flow velocity of the flow field data adjacent to the flow field data of the current analysis point in the flow field data are calculated, and the longitudinal flow velocity and latitudinal flow velocity of the flow field data of the current analysis point are respectively subtracted from the longitudinal flow velocity and latitudinal flow velocity of the flow field data of the current analysis point, and the difference results are summed and counted, and the analysis point with the largest sum result is taken as the vortex center.

[0055] Specifically, in step S21 described in this embodiment, the flow field data is analyzed to find the point with the largest change in the flow field data, and the point is used as the vortex center, thereby improving the analysis efficiency of the sub-mesoscale process and the accuracy of the sub-mesoscale vortex analysis.

[0056] Please continue reading Figure 2 As shown, the method for identifying a mesoscale vortex further includes:

[0057] Step S22: establishing a spatial rectangular scale based on the vortex center, flow field data, height data, and vortex image.

[0058] Specifically, in step S22 described in this embodiment, the center of the vortex in the vortex image is taken as the origin, the latitude direction is the x-axis, the longitude direction is the y-axis, the direction perpendicular to the vortex image and passing through the origin is the z-axis, and the unit length is 1 pixel. A spatial rectangular coordinate system is established, the x-axis increases from west to east, and the y-axis increases from south to north. The z-axis height coordinate represents the height data of the ocean vortex, and the x and y coordinate points represent the position data of each point in the vortex image.

[0059] Specifically, in step S22 described in this embodiment, a spatial rectangular coordinate system is established by analyzing the vortex image and height data, so that the flow field data and height data of each part in the vortex image are represented more clearly, thereby improving the accuracy of the analysis of the flow field data and height data, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0060] Please continue reading Figure 2 As shown, the method for identifying a mesoscale vortex further includes:

[0061] Step S23: Identify mesoscale vortices based on the flow field data and the height data.

[0062] Specifically, in step S23 of this embodiment, the ow parameter and the ow threshold are calculated based on the flow field data and the height data. The expression of the ow parameter is as follows:

[0063] W=4(v x u y -u x v y ) 2 +(u x +v y) 2 ;

[0064]

[0065] f = 2 × Ω × sinφ;

[0066] Where W represents the ow parameter, u represents the zonal velocity, v represents the meridional velocity, the subscript x represents the differential in the x-axis direction, the subscript y represents the differential in the y-axis direction, g represents the acceleration due to gravity, f represents the acceleration parameter, h represents the height data, Ω represents the angular velocity of the Earth's rotation, and φ represents the latitude;

[0067] The expression of the ow threshold is: w=0.2×σW, where w represents the ow threshold, and σW represents the standard deviation of the ow parameter.

[0068] Specifically, in step S23 described in this embodiment, the ow parameter and the ow threshold are compared to identify mesoscale vortices. If the ow parameter is greater than or equal to the ow threshold, the ocean vortex currently being analyzed is determined to be a non-mesoscale vortex; otherwise, the ocean vortex currently being analyzed is determined to be a mesoscale vortex.

[0069] Specifically, in step S23 described in this embodiment, the flow field data and height data are analyzed to analyze the ow parameter, and the ow parameter is correlated with the size of the flow field data and the height data, so as to analyze whether the ow parameter of each vortex meets the threshold, and then analyze the mesoscale vortex in the vortex image, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0070] Please continue reading Figure 2 As shown, the method for identifying a mesoscale vortex further includes:

[0071] Step S24: filtering the flow field data of the mesoscale vortex.

[0072] Specifically, in step S24 of this embodiment, the flow field data of the mesoscale vortex is filtered using a filtering formula, and the filtering formula is: D(u,v)=[(uX / 2) 2 +(vY / 2) 2 ] 1 / 2 , where D(u,v) represents the distance from the flow field data point to the vortex center, X represents the number of pixels on the x-axis in the vortex image, and Y represents the number of pixels on the y-axis in the vortex image.

[0073] Specifically, in step S24 described in this embodiment, the flow field data of the mesoscale vortex is filtered to remove data with high fluctuation frequencies in the flow field data and optimize the data details, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0074] Please continue reading Figure 1 As shown, the method for processing ocean satellite remote sensing data also includes:

[0075] Step S3: analyzing the sub-mesoscale process based on the flow field data of the mesoscale vortex after filtering and the vortex image.

[0076] See also Figure 3 As shown in Figure 2, it is an analytical method for sub-mesoscale processes, including:

[0077] In step S31 , feature analysis is performed on the vortex image according to a preset recognition frame to obtain multiple sets of feature images.

[0078] Specifically, in this embodiment, the preset identification frame adopts a pixel frame of 7×7 pixels in size. It can be understood that in this embodiment, there is no specific limitation on the size of the preset identification frame, and those skilled in the art can set it freely. For example, the preset identification frame size can also be set to a pixel frame of 5×5 or 9×9. The setting of the preset identification frame size should meet the requirements of being greater than or equal to 3 and less than or equal to 11, and the size value should be an odd number, which only needs to meet the feature analysis of the vortex image.

[0079] Specifically, in step S31 of this embodiment, starting from the upper left corner of the vortex image, the vortex image within the preset identification frame is sequentially analyzed for features, moving one pixel at a time. The average grayscale value of the vortex image within the preset identification frame is calculated, and the grayscale value of each pixel within the preset identification frame is corrected based on the grayscale value of each pixel in the vortex image within the preset identification frame and the average grayscale value. The image composed of the pixels within the preset identification frame after correction is used as the feature image. If the grayscale value of the vortex image pixel within the preset identification frame is less than the average grayscale value of the vortex image within the preset identification frame, the grayscale value of the currently analyzed pixel is reduced by α; otherwise, the grayscale value of the currently analyzed pixel is increased by α. Wherein, α represents a grayscale adjustment parameter, and is 5%≤α≤20%. It will be understood that the value of the grayscale adjustment parameter in this embodiment is specifically limited and can be freely adjusted by those skilled in the art, as long as it satisfies the extraction of the feature image. The optimal value of the grayscale adjustment parameter is: α = 10%.

[0080] Specifically, in step S31 described in this embodiment, the characteristics of the vortex image are analyzed to traverse the pixel points of each part of the vortex image through a preset identification frame to obtain a characteristic image, thereby improving the diversity of the characteristic image, increasing the analysis data, and thereby improving the analysis efficiency of the sub-mesoscale process and the accuracy of the sub-mesoscale vortex analysis.

[0081] Please continue reading Figure 3 As shown, the analysis method of the sub-mesoscale process also includes:

[0082] Step S32: Calculate the Rossby number based on the flow field data of the characteristic image.

[0083] Specifically, in step S32 described in this embodiment, vortex pixel points are extracted based on the grayscale value of the feature image pixel points. If the grayscale value of the feature image pixel point is greater than or equal to the feature grayscale threshold, the current analysis feature pixel point is extracted as the vortex pixel point; otherwise, the current analysis feature pixel point is not extracted.

[0084] In this embodiment, the characteristic grayscale threshold is set to 210. It can be understood that in this embodiment, there is no specific limitation on the value of the characteristic grayscale threshold, and those skilled in the art can set it freely. It only needs to satisfy the extraction of vortex pixel points. The setting of the characteristic grayscale threshold should satisfy [200,220].

[0085] Specifically, in step S32 of this embodiment, the Rossby number is analyzed based on the flow field data of the vortex pixel point. The expression of the Rossby number is as follows:

[0086]

[0087] Where Ro represents the Rossby number, U represents the characteristic velocity, L represents the number of vortex pixels, c represents the number of vortex pixels, u(c) represents the latitudinal velocity of the vortex pixel, and v(c) represents the longitudinal velocity of the vortex pixel.

[0088] Specifically, in step S32 described in this embodiment, the grayscale of the pixels of the characteristic image is analyzed to find the pixels with vortex characteristics in the characteristic image, and then the Rossby number is analyzed. The Rossby number is used to represent the changes in the vortex image, thereby improving the analysis efficiency of the sub-mesoscale process and the accuracy of the sub-mesoscale vortex analysis.

[0089] Please continue reading Figure 3 As shown, the analysis method of the sub-mesoscale process also includes:

[0090] Step S33 , iterating the analysis process of the feature image according to the Rossby number and the preset recognition frame, and adjusting the preset recognition frame according to the number of iterations.

[0091] Specifically, in step S33 described in this embodiment, the analysis process of the feature image is iterated based on the Rossby number and the size of the preset identification frame. If 0.8≤Ro≤1.2 and j>0, the analysis process of the feature image is iterated; if Ro<0.8 or Ro>1.2, the analysis process of the feature image is not iterated; wherein j represents the size of the preset identification frame.

[0092] Specifically, in step S33 of this embodiment, the size of the preset identification frame is reduced according to the number of iterations, so that the reduction amount of the size of the preset identification frame is equal to 2×the number of iterations.

[0093] Specifically, in step S33 described in this embodiment, by analyzing the Rossby number, an iterative operation is performed on the characteristic analysis of the vortex image that meets the conditions, thereby improving the analysis accuracy of the characteristic image, thereby improving the analysis efficiency of the sub-mesoscale process, and improving the accuracy of the sub-mesoscale vortex analysis. By analyzing the number of iterations, the size of the preset identification frame is adjusted, and the size of the preset identification frame is reduced, so that the analysis accuracy of the characteristic image is gradually increased, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0094] Please continue reading Figure 3 As shown, the analysis method of the sub-mesoscale process also includes:

[0095] Step S34: Analyze the submesoscale process based on the Rossby number.

[0096] Specifically, in step S34 of this embodiment, the sub-mesoscale process is analyzed based on the Rossby number. If ro1 ≤ Ro ≤ ro2, it is determined that a sub-mesoscale process exists in the current mesoscale vortex; otherwise, it is determined that no sub-mesoscale process exists in the current mesoscale vortex. Here, ro1 represents a first Rossby number threshold, and ro2 represents a second Rossby number threshold, with 0.9 ≤ ro1 < 1 < ro2 ≤ 1.1. It will be appreciated that the Rossby number thresholds are not specifically limited in this embodiment and can be freely set by those skilled in the art, as long as they satisfy the analysis of sub-mesoscale processes. The optimal Rossby number thresholds are: ro1 = 0.95, ro2 = 1.05.

[0097] Please continue reading Figure 1 As shown, the method for processing ocean satellite remote sensing data also includes:

[0098] Step S4: constructing a front recognition model based on the thermal image and the flow field data of the mesoscale vortex to identify the front of the mesoscale vortex.

[0099] See also Figure 4 As shown in FIG, a method for constructing a front identification model includes:

[0100] Step S41: constructing a front recognition model based on the thermal image to identify the front of the mesoscale vortex.

[0101] Specifically, in step S41 of this embodiment, the grayscale values ​​of the pixel points in the thermal image are compared with those of the adjacent pixel points to identify the front. If G1(x1, y1) / G1(x2, y2)<β1 or G1(x1, y1) / G1(x2, y2)>β2, it is determined that the color change of the current analysis pixel point is large, and the current analysis pixel point is extracted as the front pixel point; if β1≤G1(x1, y1) / G1(x2, y2)≤β2, it is determined that the color change of the current analysis pixel point is small, and the current analysis pixel point is not extracted; and the set of front pixel points is taken as the front; wherein (x1, y1) represents the thermal image The coordinates of the currently analyzed pixel in the image, (x2, y2) represent the coordinates of the pixels adjacent to the currently analyzed pixel in the thermal image, G1 represents the pixel grayscale value, G1(x1, y1) = Re(x1, y1) × 0.3 + Gr(x1, y1) × 0.59 + Bl(x1, y1) × 0.11, Re(x1, y1) represents the R value of the currently analyzed pixel, Gr(x1, y1) represents the G value of the currently analyzed pixel, and Bl(x1, y1) represents the B value of the currently analyzed pixel. β1 represents the first thermal grayscale threshold, and β2 represents the second thermal grayscale threshold, where 0.7 ≤ β1 < 1 < β2 ≤ 1.3. It will be appreciated that the values ​​of the thermal grayscale thresholds are not specifically limited in this embodiment and can be freely set by those skilled in the art as long as they satisfy the extraction of the front coordinates. The optimal values ​​of the thermal grayscale thresholds are: β1 = 0.8 and β2 = 1.2.

[0102] Specifically, in step S41 described in this embodiment, by analyzing the thermal image, the area with larger color change in the thermal image is analyzed as the front, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0103] Please continue reading Figure 4 As shown, the method for constructing the front identification model further includes:

[0104] Step S42: updating the front recognition model based on the flow field data of the mesoscale vortex after filtering.

[0105] Specifically, in step S42 described in this embodiment, the thermal-sensitive grayscale threshold is updated based on the flow field data of the mesoscale vortex after filtering to update the front recognition model. If u(x3,y3)×u(x2,y2)<0 or v(x3,y3)×v(x2,y2)<0, it is determined that convection exists at the current analysis front pixel point, and the thermal-sensitive grayscale threshold is updated to reduce the first thermal-sensitive grayscale threshold by α and increase the second thermal-sensitive grayscale threshold by α; if u(x3,y3)×u(x2,y2)≥0 or v(x3,y3)×v(x2,y2)≥0, it is determined that convection does not exist at the current analysis front pixel point, and the thermal-sensitive grayscale threshold is not updated; wherein, (x3,y3) represents the coordinates of the front pixel point.

[0106] Specifically, in step S42 described in this embodiment, the thermal grayscale threshold is adjusted by analyzing the flow field data to determine whether convection exists on both sides of the front area, thereby improving the accuracy of the front analysis, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0107] Please continue reading Figure 4 As shown, the method for constructing the front identification model further includes:

[0108] Step S43, analyzing the sub-mesoscale process based on the front adjustment.

[0109] Specifically, in step S43 of this embodiment, the Rossby number threshold is adjusted based on the number of frontal pixels and the number of vortex image pixels. If Nx3 / [2×(X+Y)]>γ, the Rossby number threshold is not adjusted. Conversely, the Rossby number threshold is adjusted to decrease the first Rossby number threshold by α and increase the second Rossby number threshold by α. Where Nx3 represents the number of frontal pixels, and γ represents the frontal threshold, which has a value range of 1≤γ≤1.5. It will be appreciated that the value of the frontal threshold is not specifically limited in this embodiment, and those skilled in the art may freely set it, as long as the Rossby number threshold is adjusted. The optimal value of the frontal threshold is: γ=1.1.

[0110] Specifically, in step S43 described in this embodiment, the front is analyzed to analyze the proportion of front pixels, and the Rossby number threshold is adjusted so that the value of the Rossby number threshold changes with the change of the proportion of front pixels, thereby improving the analysis efficiency of sub-mesoscale processes and improving the accuracy of sub-mesoscale vortex analysis.

[0111] Please continue reading Figure 1 As shown, the method for processing ocean satellite remote sensing data also includes:

[0112] Step S5: classifying the sub-mesoscale vortex according to the sub-mesoscale process, the flow field data and the height data of the mesoscale vortex after filtering. The classification of the sub-mesoscale vortex includes cyclone and anticyclone.

[0113] Specifically, in step S5 described in this embodiment, the vortex direction coordinates are extracted based on the flow field data of the mesoscale vortex after filtering. If u(x,y)=0 and v(x,y)≠0 or v(x,y)=0 and u(x,y)≠0, the current analysis coordinates are extracted as the vortex direction coordinates; if u(x,y)=v(x,y)=0 or u(x,y)≠0 and v(x,y)≠0, the current analysis coordinates are not extracted, and (x,y) represents the coordinates of the pixel point in the vortex image.

[0114] Specifically, in step S5 of this embodiment, the sub-mesoscale vortex is classified according to the vortex direction coordinates, sub-mesoscale process and height data. If the latitude of h(0,0) is in the northern hemisphere and there is a sub-mesoscale process and v(x4 min ,y4)<0,v(x4 max ,y4)>0,u(x4,y4 min )>0,u(x4,y4 max )<0, and h(0,0)>0, the submesoscale vortex is classified as a cyclone; if the latitude of h(0,0) is in the Southern Hemisphere and there is a submesoscale process and v(x4 min ,y4)<0,v(x4 max ,y4)>0,u(x4,y4 min )>0,u(x4,y4 max )<0, and h(0,0)>0, the classification of the submesoscale vortex is determined to be anticyclonic; if the latitude of h(0,0) is in the Northern Hemisphere and there is a submesoscale process and v(x4 min ,y4)>0,v(x4 max ,y4)<0,u(x4,y4 min )<0,u(x4,y4 max )>0, and h(0,0)<0, the classification of the submesoscale vortex is determined to be an anticyclone; if the latitude of h(0,0) is in the southern hemisphere and there is a submesoscale process and v(x4 min ,y4)>0,v(x4 max ,y4)<0,u(x4,y4 min )<0,u(x4,y4 max )>0, and h(0,0)<0, the sub-mesoscale vortex is classified as a cyclone; if there is no sub-mesoscale process, the sub-mesoscale vortex is not classified; where (x4,y4) represents the vortex direction coordinate, x4 min Indicates the minimum horizontal coordinate in the vortex direction coordinate, x4 maxIndicates the maximum horizontal coordinate in the vortex coordinate, y4 min Indicates the minimum ordinate in the vortex direction coordinate, y4 max Indicates the maximum vertical coordinate in the vortex coordinate.

[0115] Please continue reading Figure 1 As shown, the method for processing ocean satellite remote sensing data also includes:

[0116] Step S6: constructing a vortex dataset based on the classification of sub-mesoscale vortices and optimizing the analysis process of sub-mesoscale processes.

[0117] See also Figure 5 As shown in Figure 2, it is an optimization method for sub-mesoscale processes, including:

[0118] Step S61 , calculating the vortex diameter according to the classification of the sub-mesoscale vortex and the vortex image, and counting the vortex diameter as a vortex data set.

[0119] Specifically, in step S61 of this embodiment, the vortex diameter is calculated according to the vortex direction coordinates. The expression of the vortex diameter is: q = {[(x4 max -x4 min ) 2 +(y4 xmax -y4 xmin ) 2 ] 1 / 2 +[(y4 max -y4 min ) 2 +(x4 ymax -x4 ymin ) 2 ] 1 / 2} / 2, where q represents the vortex diameter, y4 xmax Indicates that the horizontal coordinate is x4 max The vertical coordinate at time y4 xmin Indicates that the horizontal coordinate is x4 min The vertical coordinate at time, x4 ymax Indicates that the vertical coordinate is y4 max The horizontal coordinate at time, x4 ymin Indicates that the vertical coordinate is y4 min The horizontal axis of time.

[0120] Specifically, in step S61 of this embodiment, the vortex coordinates are analyzed to analyze the vortex diameter and determine the size of the vortex, thereby improving the analysis efficiency of sub-mesoscale processes and improving the accuracy of sub-mesoscale vortex analysis.

[0121] Please continue reading Figure 5 As shown, the optimization method of the sub-mesoscale process further includes:

[0122] Step S62: Optimizing the analysis process of the sub-mesoscale process based on the vortex data set.

[0123] Specifically, in step S62 of this embodiment, the adjustment process of the Rossby number threshold is optimized according to the vortex data set to optimize the analysis process of the submesoscale process. If μ(q|q∈Q)>q last , it is determined that the vortex data set data meets the conditions, and the adjustment process of the Rossby number threshold is not optimized; on the contrary, it is determined that the vortex data set data does not meet the conditions, and the adjustment of the Rossby number threshold is optimized so that the optimized first Rossby number threshold is equal to the adjusted first Rossby number threshold × q last / μ(q|q∈Q), so that the optimized second Rossby number threshold is equal to the adjusted second Rossby number threshold ×μ(q|q∈Q) / q last ; where μ() represents the average value of the data in brackets, Q represents the vortex data set, and q last Indicates the vortex diameter obtained in the last analysis.

[0124] Specifically, in step S62 of the present embodiment, the adjustment process of the Rossby number threshold is optimized by analyzing the vortex data set, so that when the Rossby number threshold meets or does not meet the conditions of the data in the vortex data set, the Rossby number threshold is optimized, the coverage range of the Rossby number threshold is reduced, and the analysis accuracy of the sub-mesoscale process is increased, thereby improving the analysis efficiency of the sub-mesoscale process and improving the accuracy of the sub-mesoscale vortex analysis.

[0125] Please continue reading Figure 1 As shown, the method for processing ocean satellite remote sensing data also includes:

[0126] Step S7: output the classification of sub-mesoscale vortices.

[0127] See also Figure 6 As shown, it is a processing device for ocean satellite remote sensing data of this embodiment, including:

[0128] Data acquisition module, used to collect ocean eddy data;

[0129] A vortex identification module is used to identify mesoscale vortices based on ocean vortex data and filter the flow field data of mesoscale vortices;

[0130] The vortex analysis module is used to analyze sub-mesoscale processes based on the flow field data and vortex images of filtered mesoscale vortices;

[0131] A front recognition module is used to build a front recognition model based on thermal images and flow field data of mesoscale vortices to identify the front of the mesoscale vortex;

[0132] The vortex classification module is used to classify submesoscale vortices based on submesoscale processes, the flow field data of the filtered mesoscale vortices, and the height data;

[0133] Build an optimization module to construct eddy datasets based on the classification of submesoscale eddies and optimize the analysis of submesoscale processes;

[0134] Data output module, used to output the classification of submesoscale vortices.

[0135] An embodiment of the present application also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the method for processing ocean satellite remote sensing data as described in the above method embodiment.

[0136] Those skilled in the art will appreciate that all or some of the steps in the method disclosed above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as a computer-readable program, a data structure, a program module or other data) and is volatile and non-volatile, removable and non-removable media. Computer storage media includes but is not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable programs, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0137] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for processing ocean satellite remote sensing data, characterized in that: include: Collect ocean eddy data; Identify mesoscale vortices based on ocean vortex data and filter the flow field data of mesoscale vortices; Analyze submesoscale processes based on filtered flow field data and vortex images of mesoscale vortices; A front recognition model is constructed based on thermal images and flow field data of mesoscale vortices to identify the front of mesoscale vortices. Submesoscale vortices are classified based on submesoscale processes, filtered flow field data, and height data of mesoscale vortices. Construct an eddy dataset based on the classification of submesoscale eddies and optimize the analysis of submesoscale processes; Outputs the classification of submesoscale eddies.

2. The method for processing ocean satellite remote sensing data according to claim 1, wherein: The ow parameter and ow threshold are calculated based on the flow field data and height data, and the ow parameter and ow threshold are compared to identify mesoscale vortices. If the ow parameter is greater than or equal to the ow threshold, the ocean vortex currently being analyzed is determined to be a non-mesoscale vortex; otherwise, the ocean vortex currently being analyzed is determined to be a mesoscale vortex.

3. The method for processing ocean satellite remote sensing data according to claim 2, characterized in that: Starting from the upper left corner of the vortex image, the vortex image within the preset identification frame is analyzed in sequence by moving 1 pixel at a time. The average grayscale value of the vortex image within the preset identification frame is calculated, and the grayscale value of each pixel within the preset identification frame is corrected according to the grayscale value of each pixel of the vortex image within the preset identification frame and the average grayscale value. The image composed of the pixels within the preset identification frame after correction is used as the feature image. If the grayscale value of the pixel of the vortex image within the preset identification frame is less than the average grayscale value of the vortex image within the preset identification frame, the grayscale value of the currently analyzed pixel is reduced by α; otherwise, the grayscale value of the currently analyzed pixel is increased by α; wherein α represents a grayscale adjustment parameter; Vortex pixels are extracted based on the grayscale value of the feature image pixels. If the grayscale value of the feature image pixel is greater than or equal to the feature grayscale threshold, the current analysis feature pixel is extracted as the vortex pixel; otherwise, the current analysis feature pixel is not extracted.

4. The method for processing ocean satellite remote sensing data according to claim 3, characterized in that: The Rossby number is analyzed based on the flow field data of the vortex pixel point. The expression of the Rossby number is as follows: Where Ro represents the Rossby number, U represents the characteristic velocity, L represents the number of vortex pixels, c represents the number of vortex pixels, u(c) represents the latitudinal velocity of the vortex pixel, and v(c) represents the longitudinal velocity of the vortex pixel. The feature image analysis process is iterated according to the Rossby number and the size of the preset recognition frame. When 0.8≤Ro≤1.2 and j>0, the feature image analysis process is iterated, j represents the size of the preset recognition frame, and the size of the preset recognition frame is reduced according to the number of iterations, so that the reduction amount of the preset recognition frame size is equal to 2×the number of iterations; The sub-mesoscale process is analyzed based on the Rossby number. When the Rossby number satisfies ro1≤Ro≤ro2, it is determined that a sub-mesoscale process exists in the current mesoscale vortex; where ro1 represents the first Rossby number threshold and ro2 represents the second Rossby number threshold.

5. The method for processing ocean satellite remote sensing data according to claim 4, characterized in that: The grayscale values ​​of pixels in the thermal image are compared with those of their adjacent pixels to identify the front. Pixels that satisfy G1(x1, y1) / G1(x2, y2)<β1 or G1(x1, y1) / G1(x2, y2)>β2 are extracted as front pixels, and the set of front pixels is taken as the front, where (x1, y1) represents the coordinates of the current analysis pixel in the thermal image, (x2, y2) represents the coordinates of the pixels adjacent to the current analysis pixel in the thermal image, G1 represents the pixel grayscale value, β1 represents the first thermal grayscale threshold, and β2 represents the second thermal grayscale threshold.

6. The method for processing ocean satellite remote sensing data according to claim 5, characterized in that: Update the thermal grayscale threshold based on the flow field data of the filtered mesoscale vortex to update the front recognition model. When u(x3,y3)×u(x2,y2)<0 or v(x3,y3)×v(x2,y2)<0, update the thermal grayscale threshold so that the first thermal grayscale threshold is reduced by α and the second thermal grayscale threshold is increased by α, where (x3,y3) represents the coordinates of the front pixel point; The Rossby number threshold is adjusted according to the number of front pixels and the number of vortex image pixels. When Nx3 / [2×(X+Y)]≤γ, the Rossby number threshold is adjusted to reduce the first Rossby number threshold by α and increase the second Rossby number threshold by α; where Nx3 represents the number of front pixels and γ represents the front threshold.

7. The method for processing ocean satellite remote sensing data according to claim 6, characterized in that: Extract the vortex direction coordinates based on the flow field data of the mesoscale vortex after filtering, and extract the coordinates that satisfy u(x,y)=0 and v(x,y)≠0 or v(x,y)=0 and u(x,y)≠0 as the vortex direction coordinates; Submesoscale vortices are classified according to vortex direction coordinates, submesoscale processes and height data. The classification of submesoscale vortices includes cyclones and anticyclones.

8. The method for processing ocean satellite remote sensing data according to claim 7, characterized in that: The vortex diameter is calculated based on the vortex direction coordinates. The expression of the vortex diameter is: q = {[(x4 max -x4 min ) 2 +(y4 xmax -y4 xmin ) 2 ] 1 / 2 +[(y4 max -y4 min ) 2 +(x4 ymax -x4 ymin ) 2 ] 1 / 2 } / 2, where q represents the vortex diameter, y4 xmax Indicates that the horizontal coordinate is x4 max The vertical coordinate at time y4 xmin Indicates that the horizontal coordinate is x4 min The vertical coordinate at time, x4 ymax Indicates that the vertical coordinate is y4 max The horizontal coordinate at time, x4 ymin Indicates that the vertical coordinate is y4 min The horizontal axis of time; The Rossby number threshold adjustment process is optimized based on the vortex data set to optimize the analysis process of submesoscale processes. last When the Rossby number threshold is optimized, the adjustment of the Rossby number threshold is made so that the optimized first Rossby number threshold is equal to the adjusted first Rossby number threshold × q last / μ(q|q∈Q), so that the optimized second Rossby number threshold is equal to the adjusted second Rossby number threshold ×μ(q|q∈Q) / q last ; where μ() represents the average value of the data in brackets, Q represents the vortex data set, and q last Indicates the vortex diameter obtained in the last analysis.

9. A device for processing ocean satellite remote sensing data, applied to the method for processing ocean satellite remote sensing data according to claim 1, characterized in that: include: Data acquisition module, used to collect ocean eddy data; A vortex identification module is used to identify mesoscale vortices based on ocean vortex data and filter the flow field data of the mesoscale vortices; The vortex analysis module is used to analyze sub-mesoscale processes based on the flow field data and vortex images of filtered mesoscale vortices; A front recognition module is used to build a front recognition model based on thermal images and flow field data of mesoscale vortices to identify the front of the mesoscale vortex; The vortex classification module is used to classify submesoscale vortices based on submesoscale processes, the flow field data of filtered mesoscale vortices, and the height data; Build an optimization module to construct eddy datasets based on the classification of submesoscale eddies and optimize the analysis of submesoscale processes; Data output module, used to output the classification of submesoscale vortices.

10. A storage medium, characterized in that: The method stores instructions which, when executed on a computer, enable the computer to execute the method for processing ocean satellite remote sensing data according to any one of claims 1 to 8.

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