Marine vortex polarity identification method and device based on SAR satellite observation

Abstract

The invention discloses an ocean vortex polarity identification method and device based on SAR satellite observation. The method comprises the following steps: cutting an acquired SAR image to be identified to obtain a vortex slice image; carrying out vortex element extraction on the vortex slice image by adopting a self-adaptive threshold method to obtain a vortex threshold image; performing polar coordinate conversion on the vortex threshold image by taking the vortex of the vortex threshold image as a center to obtain a vortex polar coordinate image; performing texture extraction on the vortex polar coordinate image by adopting a local gradient method to obtain a main texture direction of the vortex polar coordinate image; and determining the polarity of the ocean vortex in the to-be-identified SAR image according to the main texture direction.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean vortex polarity recognition, and more particularly, to a method and device for identifying the polarity of ocean vortices based on SAR satellite observations. Background Art

[0002] Ocean vortices are widely distributed in the ocean. They change the distribution of matter and energy in the ocean through horizontal convergence and divergence and vertical thermohaline transport, becoming a phenomenon that cannot be ignored in the ocean. The polarity of vortices can be divided into cyclonic and anticyclonic. In the Northern Hemisphere, cyclonic vortices rotate counterclockwise, while anticyclonic vortices rotate clockwise. In the Southern Hemisphere, it is just the opposite. Ocean vortices with different polarities have different characteristics. Among them, cyclonic vortices correspond to lower sea surface heights. The divergence of seawater causes the central seawater to move upward from bottom to top, transporting the cold water in the lower layer to the upper layer, making the water temperature inside the vortex lower than that of the surrounding seawater. Anticyclones are the opposite, corresponding to higher sea surface heights. The convergence causes the warm water in the upper layer to enter the lower layer, making the water temperature inside the vortex higher than that of the surrounding seawater.

[0003] Traditional dynamic methods for ocean vortex detection mainly include the Euler method (physical parameter method, flow field geometry method) and the Lagrangian method, etc. The rotation direction and polarity of vortices are determined through the information of the flow field and the geometric characteristics of the trajectory loop. Limited by the observation resolution of satellite altimeters and radiometers, only larger-scale ocean vortices can be identified based on sea surface height anomalies and sea surface temperature anomalies. The emergence of high-resolution Synthetic Aperture Radar (SAR) makes it possible to observe submesoscale and small-scale vortices, and the refined structure of ocean vortices can be identified by observing the changes in sea surface roughness caused by ocean vortices. Multiple ocean vortex phenomena can be captured in SAR images, mainly including the following three categories: black vortices, white vortices, and ice vortices. Among them, the vortex formed by oil film coverage is simply called a "black vortex", the vortex formed by wave-current interaction is simply called a "white vortex", and the vortex formed by sea ice coverage is simply called an "ice vortex". Traditional vortex polarity discrimination methods are not applicable to SAR images, and currently, there is a lack of a polarity discrimination method for SAR ocean vortex images. Therefore, generally, the rotation direction of vortices is judged by visual interpretation, but its efficiency is significantly low. Subsequently, researchers proposed a method for depicting the morphological information of SAR, and indirectly gave a SAR ocean vortex polarity discrimination scheme through logarithmic spiral fitting. This method extracts the cantilever information of the vortex, uses the least squares fitting of the logarithmic spiral to characterize the morphology of the ocean vortex, and identifies the rotation direction of the vortex from the expression of the logarithmic spiral, thereby determining the polarity. This method shows a certain extraction effect on black vortices.

[0004] Existing methods for discriminating the polarity of ocean vortices have certain deficiencies. First, the traditional vortex polarity discrimination scheme based on the information of the flow field and the geometric characteristics of the trajectory loop is not applicable to the discrimination of the polarity of ocean vortices in SAR images. Second, the efficiency of using manual visual interpretation for discrimination is low. Finally, considering the complex characteristics of ocean vortices, using logarithmic spiral lines to characterize and indirectly obtain the rotation direction of vortices becomes unstable and is not applicable to the polarity discrimination of various ocean vortices on a global scale.

[0005] For the three common types of ocean vortices: black vortices, white vortices, and ice vortices, their characteristics are complex and different. Among them, black vortices are modulated by the sea surface oil film, and their distribution is often very complex, affecting the integrity of the black vortex cantilever characteristics in SAR images. For white vortices, affected by the wave-current interaction, the cantilever characteristics in SAR images are very weak. For ice vortices, the random characteristics of the upper floating ice are significant, and the cantilevers are relatively fragmented, making it difficult to identify. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method and device for discriminating the polarity of ocean vortices based on SAR satellite observations.

[0007] According to one aspect of the present invention, there is provided a method for discriminating the polarity of ocean vortices based on SAR satellite observations, including:

[0008] Cropping the collected SAR image to be discriminated to obtain a vortex slice image;

[0009] Using an adaptive threshold method to extract vortex elements from the vortex slice image to obtain a vortex threshold image;

[0010] Taking the vortex in the vortex threshold image as the center to perform polar coordinate transformation on the vortex threshold image to obtain a vortex polar coordinate image;

[0011] Using the local gradient method to extract the texture of the vortex polar coordinate image to obtain the main texture direction of the vortex polar coordinate image;

[0012] Determining the polarity of the ocean vortex in the SAR image to be discriminated according to the main texture direction.

[0013] Optionally, cropping the collected SAR image to be identified to obtain a vortex slice image, including:

[0014] Preprocessing the SAR image to be discriminated to obtain a preprocessed SAR image;

[0015] Cropping the preprocessed SAR image to obtain a vortex slice image.

[0016] Optionally, preprocessing the SAR image to be discriminated to obtain a preprocessed SAR image, including:

[0017] Perform orbital correction, radiometric calibration, thermal noise removal, multi-look processing, filtering processing, and terrain correction processing on the SAR image to be judged and recognized to obtain a preprocessed SAR image.

[0018] Optionally, adopt an adaptive threshold method to extract vortex elements from the vortex slice image to obtain a vortex threshold image, including:

[0019] Perform histogram equalization processing on the vortex slice image to obtain a vortex histogram equalized image;

[0020] Adopt an adaptive threshold method to calculate the local threshold of the vortex histogram equalized image to obtain a vortex threshold image.

[0021] Optionally, adopt an adaptive threshold method to calculate the local threshold of the vortex histogram equalized image to obtain a vortex threshold image, including:

[0022] Determine the window size according to the size and brightness distribution of the vortex histogram equalized image;

[0023] Calculate the local threshold within each window of the vortex histogram equalized image;

[0024] Perform binarization processing on the vortex histogram equalized image according to the local threshold of each window to obtain a vortex threshold image.

[0025] Optionally, adopt a local gradient method to extract texture from the vortex polar coordinate image to obtain the main texture direction of the vortex polar coordinate image, including:

[0026] Adopt a pre-constructed smoothing operator to perform smoothing processing on the vortex polar coordinate image to obtain a vortex smoothed image;

[0027] Adopt an optimized Sobel operator to calculate the initial local gradient result of the vortex smoothed image;

[0028] Perform squaring and smoothing processing on the initial local gradient result to obtain a second local gradient result;

[0029] Perform squaring on the initial local gradient result and perform smoothing processing on the absolute value of the squared result to obtain a third local gradient result;

[0030] Construct a coherence factor and a quality factor according to the second local gradient result and the third local gradient result;

[0031] Calculate the maximum weighted squared gradient according to the coherence factor, the quality factor, and the second local gradient result;

[0032] Statistically calculate the maximum weighted squared gradient value in each direction and draw a gradient histogram;

[0033] Smooth the histogram of gradients to obtain a gradient-smoothed image;

[0034] Take the square root of the direction corresponding to the maximum value of the maximum weighted square gradient value in the gradient-smoothed image to obtain the main texture direction.

[0035] Optionally, the smoothing operator R |2 has the following expression:

[0036] R |2 = B 2 S |2 B 4

[0037]

[0038] In the formula, B 2 and B 4 represent smoothing operators, and S |2 is downsampling processing;

[0039] The Sobel operator D x has the following expression:

[0040]

[0041] The initial local gradient result g' mn , the second local gradient result g″ mn and the third local gradient result g″′ mn are:

[0042] g′ mn = (D x + iD y )(A)

[0043]

[0044] In the formula, D y represents the transpose of D x , A is the vortex-smoothed image, and the subscripts m and n represent the row and column numbers of the sub-image;

[0045] The coherence factor c mn and the quality factor r mn have the following expressions:

[0046]

[0047]

[0048] In the formula, p is all the row numbers of the sub-image; q is all the column numbers of the sub-image.

[0049] Optionally, determining the polarity of the ocean vortex in the SAR image to be identified according to the main texture direction includes:

[0050] Determining the rotation direction of the ocean vortex according to the main texture direction;

[0051] Determining the polarity of the ocean vortex in the SAR image to be identified according to the rotation direction.

[0052] According to another aspect of the present invention, there is provided an apparatus for identifying the polarity of an ocean vortex based on SAR satellite observations, including:

[0053] A cropping module for cropping the SAR image to be identified collected to obtain a vortex slice image;

[0054] An element extraction module for extracting vortex elements from the vortex slice image by using an adaptive threshold method to obtain a vortex threshold image;

[0055] A conversion module for performing polar coordinate conversion on the vortex threshold image with the vortex of the vortex threshold image as the center to obtain a vortex polar coordinate image;

[0056] A texture extraction module for extracting the texture of the vortex polar coordinate image by using the local gradient method to obtain the main texture direction of the vortex polar coordinate image;

[0057] A determination module for determining the polarity of the ocean vortex in the SAR image to be identified according to the main texture direction.

[0058] According to yet another aspect of the present invention, there is provided a computer-readable storage medium storing a computer program for executing the method described in any one of the above aspects of the present invention.

[0059] According to yet another aspect of the present invention, there is provided an electronic device including: a processor; a memory for storing executable instructions of the processor; the processor for reading the executable instructions from the memory and executing the instructions to implement the method described in any one of the above aspects of the present invention.

[0060] Accordingly, the present invention provides a method for identifying the polarity of ocean vortices based on SAR satellite observations. The SAR images to be identified are cropped to obtain vortex slice images; an adaptive threshold method is used to extract vortex elements from the vortex slice images to obtain vortex threshold images; the vortex threshold images are subjected to polar coordinate transformation with the vortices in the vortex threshold images as the centers to obtain vortex polar coordinate images; a local gradient method is used to extract textures from the vortex polar coordinate images to obtain the main texture directions of the vortex polar coordinate images; and the polarities of the ocean vortices in the SAR images to be identified are determined according to the main texture directions. The cantilever information of the ocean vortices is accurately extracted by the adaptive threshold method, reducing interference information to a certain extent, and is simpler and more effective than the existing solutions; the ocean vortices are transformed from complex two-dimensional rotation information to more direct polar coordinate texture information, solving the complex vortex rotation problem and making the discrimination of the rotation direction simpler and more intuitive; the use of the local gradient method for main gradient calculation can ignore the interference of other signals in the image, thereby judging the main body of the ocean vortices and accurately discriminating the rotation direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The exemplary embodiments of the present invention can be more fully understood by referring to the following drawings:

[0062] Figure 1 is a schematic flowchart of a method for identifying the polarity of ocean vortices based on SAR satellite observations provided by an exemplary embodiment of the present invention;

[0063] Figure 2 is another schematic flowchart of a method for identifying the polarity of ocean vortices based on SAR satellite observations provided by an exemplary embodiment of the present invention;

[0064] Figure 3 is a schematic diagram of a black vortex SAR image provided by an exemplary embodiment of the present invention;

[0065] Figure 4 is a schematic diagram of a white vortex SAR image provided by an exemplary embodiment of the present invention;

[0066] Figure 5 is a schematic diagram of an ice vortex SAR image provided by an exemplary embodiment of the present invention;

[0067] Figure 6 is a schematic diagram of the histogram equalization results of ocean vortices provided by an exemplary embodiment of the present invention (from left to right are black vortices, white vortices, and ice vortices);

[0068] Figure 7 is a schematic diagram of the adaptive threshold results of ocean vortices provided by an exemplary embodiment of the present invention (from left to right are black vortices, white vortices, and ice vortices);

[0069] Figure 8Schematic diagram of the polar coordinate conversion result of ocean vortices provided by an exemplary embodiment of the present invention (from left to right are black vortices, white vortices, and ice vortices);

[0070] Figure 9 Schematic diagram of the principle for discriminating the rotation direction provided by an exemplary embodiment of the present invention;

[0071] Figure 10 Schematic diagram of the structure of an ocean vortex polarity discrimination device based on SAR satellite observations provided by an exemplary embodiment of the present invention;

[0072] Figure 11 Structure of an electronic device provided by an exemplary embodiment of the present invention. Detailed implementation manners

[0073] Next, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0074] It should be noted that: Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present invention.

[0075] Those skilled in the art can understand that terms such as "first", "second", etc. in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, etc., and do not represent any specific technical meaning, nor do they indicate an inevitable logical order between them.

[0076] It should also be understood that in the embodiments of the present invention, "a plurality of" may refer to two or more, and "at least one" may refer to one, two, or more.

[0077] It should also be understood that for any component, data, or structure mentioned in the embodiments of the present invention, unless clearly defined or given a contrary indication in the context, it can generally be understood as one or more.

[0078] In addition, the term "and / or" in the present invention is only a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the associated objects before and after.

[0079] It should also be understood that the present invention emphasizes the differences between various embodiments. The same or similar parts can be referred to each other. For the sake of brevity, they will not be described one by one.

[0080] Meanwhile, it should be understood that, for the sake of description convenience, the sizes of the various parts shown in the drawings are not drawn in actual proportional relationships.

[0081] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way a limitation on the present invention, its application, or its use.

[0082] Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and devices should be regarded as part of the specification.

[0083] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0084] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate together with numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above systems, and so on.

[0085] Terminal devices, computer systems, servers, and other electronic devices can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. The computer system / server can be implemented in a distributed cloud computing environment where tasks are executed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0086] Exemplary method

[0087] Figure 1 is a schematic flowchart of a method for identifying the polarity of ocean vortices based on SAR satellite observations provided by an exemplary embodiment of the present invention. This embodiment can be applied to an electronic device, such as Figure 1 As shown, the method 100 for identifying the polarity of ocean vortices based on SAR satellite observations includes the following steps:

[0088] Step 101: Crop the collected SAR image to be identified to obtain a vortex slice image.

[0089] Step 102: Use the adaptive threshold method to extract vortex elements from the vortex slice image to obtain a vortex threshold image.

[0090] Step 103: Perform polar coordinate transformation on the vortex threshold image with the vortex in the vortex threshold image as the center to obtain a vortex polar coordinate image.

[0091] Step 104: Use the local gradient method to extract the texture of the vortex polar coordinate image to obtain the main texture direction of the vortex polar coordinate image.

[0092] Step 105: Determine the polarity of the ocean vortex in the SAR image to be identified according to the main texture direction.

[0093] Specifically, the problem to be solved by the present invention is how to eliminate the interference of various elements mentioned in the background art, discriminate the rotation directions of the three types of ocean vortices, namely black vortices, white vortices, and ice vortices, so as to obtain the vortex polarity. Refer to Figure 2 As shown, the specific implementation steps are as follows:

[0094] Step 1: Preprocessing of ocean vortex data

[0095] For the SAR image with ocean vortex phenomenon, first perform relevant preprocessing operations: orbit correction, radiometric calibration, thermal noise removal, multi-look processing, filtering processing, terrain correction, so as to restore the real vortex shape, and obtain a vortex slice image through cropping. Figure 3 、 Figure 4 and Figure 5 show the results after preprocessing of the three types of ocean vortices.

[0096] Step 2: Vortex element extraction and polar coordinate transformation

[0097] Vortices usually show light and dark differences in SAR images, so the vortex elements can be screened by the threshold method. The traditional global threshold method distinguishes the target area and the background area in the image by setting a unified gray threshold. However, when processing different image scenes, a single global threshold often cannot meet the requirements, which may lead to problems of missed screening or mis-screening. Therefore, in this paper, the adaptive threshold method is adopted to calculate the local threshold according to the brightness distribution of the image in different regions, so that the threshold can be dynamically adjusted with the change of the brightness of the surrounding pixels to adapt to the extraction of different vortex images.

[0098] The basic principle of the adaptive threshold method is as follows: First, determine a basic window size according to the size and brightness distribution of the image; then, calculate the local threshold (e.g., grayscale mean) within each window; finally, perform binary processing on the image based on the local threshold of each window to obtain the final thresholding result. Considering that the size of ice vortex slice images is generally between 100 and 1000 pixels, this paper uses an adaptive threshold window of size 51 and takes the grayscale mean within this window as the threshold for processing. Before performing the threshold processing, it is necessary to perform histogram equalization on the preprocessed image to improve the overall light and dark balance. Figure 6 、 Figure 7 and Figure 8 show the histogram equalization results and adaptive threshold processing results of three ocean vortices.

[0099] Thus, histogram equalization corrects the over-bright and over-dark areas in the image. Further, the adaptive threshold method is used to convert the alternating bright and dark rotating stripes in the SAR image into a binary image, which is convenient for subsequent processing. The cantilever information of ocean vortices is accurately extracted through histogram equalization and the adaptive threshold method, reducing interference information to a certain extent and being simpler and more effective than existing solutions.

[0100] Polar coordinate transformation is used to process the ocean vortex. The processed binary image is subjected to polar coordinate transformation according to the image center position, converting the original two-dimensional rotation information into angle and radius information in polar coordinates, that is, texture information. Converting the ocean vortex from complex two-dimensional rotation information to more direct polar coordinate texture information solves the problem of complex vortex rotation and makes the discrimination of the rotation direction simpler and more intuitive.

[0101] Step 3: Local gradient calculation

[0102] The local gradient method is used to extract the texture of the vortex image after polar coordinate transformation. The specific algorithm is as follows:

[0103] First, since local gradient calculation is sensitive to noise in the SAR image, during the processing, it is first necessary to smooth the image. For this purpose, this paper uses a smoothing operator R |2 , which aims to effectively reduce the interference of noise on the calculation result by performing two smoothing operations and one downsampling step. The specific processing flow can be represented by the following formula:

[0104] R |2 = B 2 S |2 B 4 R |2 = B 2 S |2 B 4

[0105]

[0106] Then, the optimized Sobel operator D is adopted x to calculate the local gradient of image A, where G′ represents the result of the preliminarily calculated local gradient.

[0107]

[0108] G′ = (g′ mn ) = (D x + iD y )(A)

[0109] To solve the problem of negative gradient values, the preliminarily calculated gradient values are squared and smoothed. At the same time, the squared gradient values also need to be processed similarly to ensure the consistency of the results.

[0110]

[0111] In the process of calculating the main gradient, two evaluation factors, c m′n′ and r m′n′ , are considered to be introduced. These two factors can optimize the calculation process of the main gradient to a certain extent, because the larger their values, the higher the accuracy and stability of the calculation results will be.

[0112]

[0113] The main direction of the squared gradient is determined by the maximum weighted squared gradient (WSLG). For this purpose, the directions in the range of 0 to 360 degrees are evenly divided into 72 intervals, and the gradient values in each direction are counted to obtain the distribution of the main direction.

[0114]

[0115] Finally, to improve the smoothness and interpretability of the WSLG histogram, the calculated histogram results are smoothed. The specific smoothing operation adopts the following operator:

[0116]

[0117] Statistically, the direction corresponding to the maximum value of WSLG is square-rooted to obtain the main gradient direction. Thus, the main texture direction of the image is inferred. For the three given examples of ocean vortices, the calculated results of their local gradients are 58.5°, 68.5°, and 111° respectively.

[0118] Therefore, the present invention applies a local gradient operator to calculate the main texture information of the image after polar coordinate transformation. Considering that the texture directions in polar coordinates are different for different rotation directions, the distance corresponding to clockwise rotation increases with the increase of the angle, while the distance corresponding to counterclockwise rotation decreases with the increase of the angle, and their texture directions in polar coordinates are completely different. Even after the ocean vortex is transformed into polar coordinates, there are still various interference stripe information. Using the local gradient method to calculate the main gradient can ignore the interference of other signals in the image, thereby judging the main body of the ocean vortex and accurately discriminating the rotation direction.

[0119] Step Four: Vortex Polarity Discrimination

[0120] In the polar coordinate system, the abscissa represents the rotation angle, and the ordinate represents the distance from the image center to the pixel point. The main texture direction of the ice vortex body judged by the local gradient method has a specific meaning in the polar coordinate system. Based on this direction, it is found that the distance of sea ice pixels shows a regular pattern with the change of the angle: in the clockwise direction, the distance increases with the increase of the angle; while in the counterclockwise direction, the distance decreases with the increase of the angle. The specific principle is as Figure 9 shown.

[0121] Therefore, according to the local gradient calculation results of the three vortex examples given, their rotation directions can be discriminated. 0 - 90° corresponds to counterclockwise, and 90 - 180° corresponds to clockwise. The determination results of the examples are counterclockwise, counterclockwise, and clockwise respectively. Considering that the positions of these three ocean vortices are all in the Northern Hemisphere, the corresponding polarities are cyclone, cyclone, and anticyclone.

[0122] Therefore, the present invention accurately extracts the cantilever information of the ocean vortex through the adaptive threshold method, reduces the interference information to a certain extent, and is simpler and more effective than the existing solutions; transforms the ocean vortex from complex two-dimensional rotation information into more direct polar coordinate texture information, solves the complex vortex rotation problem, and makes the discrimination of the rotation direction simpler and more intuitive; using the local gradient method to calculate the main gradient can ignore the interference of other signals in the image, thereby judging the main body of the ocean vortex and accurately discriminating the rotation direction.

[0123] Exemplary device

[0124] Figure 10 is a schematic structural diagram of an ocean vortex polarity discrimination device based on SAR satellite observation provided by an exemplary embodiment of the present invention. As Figure 10 shown, the device 1000 includes:

[0125] A cropping module 1010, configured to crop the collected SAR image to be discriminated to obtain a vortex slice image;

[0126] The element extraction module 1020 is used to extract vortex elements from the vortex slice image by using the adaptive threshold method to obtain the vortex threshold image;

[0127] The conversion module 1030 is used to perform polar coordinate conversion on the vortex threshold image with the vortex of the vortex threshold image as the center to obtain the vortex polar coordinate image;

[0128] The texture extraction module 1040 is used to extract the texture of the vortex polar coordinate image by using the local gradient method to obtain the main texture direction of the vortex polar coordinate image;

[0129] The determination module 1050 is used to determine the polarity of the ocean vortex in the SAR image to be identified according to the main texture direction.

[0130] Optionally, the cropping module 1010 includes:

[0131] The preprocessing sub-module is used to preprocess the SAR image to be identified to obtain the preprocessed SAR image;

[0132] The cropping sub-module is used to crop the preprocessed SAR image to obtain the vortex slice image.

[0133] Optionally, the preprocessing sub-module includes:

[0134] The processing unit is used to perform orbit correction, radiometric calibration, thermal noise removal, multi-look processing, filtering processing, and terrain correction processing on the SAR image to be identified to obtain the preprocessed SAR image.

[0135] Optionally, the element extraction module 1020 includes:

[0136] The first processing sub-module is used to perform histogram equalization processing on the vortex slice image to obtain the vortex histogram equalized image;

[0137] The first calculation sub-module is used to calculate the local threshold of the vortex histogram equalized image by using the adaptive threshold method to obtain the vortex threshold image.

[0138] Optionally, the first calculation sub-module includes:

[0139] The determination unit is used to determine the window size according to the size and brightness distribution of the vortex histogram equalized image;

[0140] The calculation unit is used to calculate the local threshold within each window of the vortex histogram equalized image;

[0141] The binarization processing unit is used to perform binarization processing on the vortex histogram equalized image according to the local threshold of each window to obtain the vortex threshold image.

[0142] Optionally, the texture extraction module 1040 includes:

[0143] A first smoothing sub-module for smoothing the vortex polar coordinate image using a pre-constructed smoothing operator to obtain a vortex smoothed image;

[0144] A second calculation sub-module for calculating an initial local gradient result of the vortex smoothed image using an optimized Sobel operator;

[0145] A second processing sub-module for squaring and smoothing the initial local gradient result to obtain a second local gradient result;

[0146] A third processing sub-module for squaring the initial local gradient result and smoothing the absolute value of the squared result to obtain a third local gradient result;

[0147] A construction sub-module for constructing a coherence factor and a quality factor based on the second local gradient result and the third local gradient result;

[0148] A third calculation sub-module for calculating a maximum weighted squared gradient based on the coherence factor, the quality factor, and the second local gradient result;

[0149] A statistics sub-module for statistically calculating the maximum weighted squared gradient value in each direction and plotting a gradient histogram;

[0150] A second smoothing sub-module for smoothing the gradient histogram to obtain a gradient smoothed image;

[0151] A fourth processing sub-module for taking the square root of the direction corresponding to the maximum value of the maximum weighted squared gradient value in the gradient smoothed image to obtain a main texture direction.

[0152] Optionally, the smoothing operator R |2 has the following expression:

[0153] R |2 = B 2 S |2 B 4

[0154]

[0155] wherein, B 2 and B 4 represent smoothing operators, and S |2 is downsampling processing;

[0156] The Sobel operator D x has the following expression:

[0157]

[0158] Initial local gradient result g' mn and the second local gradient result g″ mn and the third local gradient result g″′ mn are as follows:

[0159] g′ mn =(D x +iD y )(A)

[0160]

[0161] In the formula, D y represents the transpose of D x , A is the vortex smoothed image, and the subscripts m and n represent the row and column numbers of the sub - image;

[0162] The expressions of the coherence factor c mn and the quality factor r mn are as follows:

[0163]

[0164] In the formula, p is all the row numbers of the sub - image; q is all the column numbers of the sub - image.

[0165] Optionally, the determination module 1050 includes:

[0166] The first determination sub - module is used to determine the rotation direction of the ocean vortex according to the main texture direction;

[0167] The second determination sub - module is used to determine the polarity of the ocean vortex in the SAR image to be identified according to the rotation direction.

[0168] Exemplary electronic device

[0169] Figure 11 is the structure of an electronic device provided by an exemplary embodiment of the present invention. As Figure 11 shown, the electronic device 110 includes one or more processors 111 and a memory 112.

[0170] The processor 111 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0171] The memory 112 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage media, and the processor 111 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 113 and an output device 114, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0172] In addition, the input device 113 may further include, for example, a keyboard, a mouse, and so on.

[0173] The output device 114 may output various information to the outside. The output device 114 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, and so on.

[0174] Of course, for simplicity, Figure 11 only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application scenarios, the electronic device may further include any other appropriate components.

[0175] Exemplary computer program product and computer-readable storage medium

[0176] In addition to the above methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions that, when run by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above of this specification.

[0177] The computer program product may be written in any combination of one or more programming languages to write program code for performing the operations of the embodiments of the present invention. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0178] In addition, an embodiment of the present invention may also be a computer-readable storage medium storing computer program instructions, which, when run by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above of this specification.

[0179] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0180] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present invention. In addition, the specific details disclosed above are only for the purposes of illustration and easy understanding, rather than limitations. The above details do not limit the present invention to necessarily adopt the above specific details for implementation.

[0181] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For system embodiments, since they basically correspond to method embodiments, they are described relatively simply, and the relevant parts can be referred to the description of the method embodiments.

[0182] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only exemplary examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended words, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with each other.

[0183] The methods and systems of the present invention can be implemented in many ways. For example, the methods and systems of the present invention can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the methods is for illustration purposes only. The steps of the methods of the present invention are not limited to the specific order described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention can also be implemented as a program recorded in a recording medium, and these programs include machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers the recording medium storing the programs for executing the methods according to the present invention.

[0184] It should also be noted that in the systems, devices, and methods of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0185] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. A method for identifying the polarity of an ocean eddy based on SAR satellite observations, characterized in that: include: The collected SAR images to be identified are cropped to obtain vortex slice images; Using an adaptive threshold method to extract vortex elements from the vortex slice image to obtain a vortex threshold image; Performing polar coordinate conversion on the vortex threshold image with the vortex of the vortex threshold image as the center to obtain a vortex polar coordinate image; Using a local gradient method to perform texture extraction on the vortex polar coordinate image to obtain a main texture direction of the vortex polar coordinate image; The polarity of the ocean vortex in the SAR image to be identified is determined according to the main texture direction.

2. The method according to claim 1, characterized in that The collected SAR images to be identified are cropped to obtain vortex slice images, including: Preprocessing the SAR image to be identified to obtain a preprocessed SAR image; The pre-processed SAR image is cropped to obtain a vortex slice image.

3. The method according to claim 2, characterized in that Preprocessing the SAR image to be identified to obtain a preprocessed SAR image includes: The SAR image to be identified is subjected to orbit correction, radiation calibration, thermal noise removal, multi-view processing, filtering processing and terrain correction processing to obtain the pre-processed SAR image.

4. The method according to claim 1, characterized in that: The vortex elements are extracted from the vortex slice image using an adaptive threshold method to obtain a vortex threshold image, including: Performing histogram averaging on the vortex slice image to obtain a vortex histogram balanced image; The adaptive threshold method is used to calculate the local threshold of the vortex histogram equalization image to obtain a vortex threshold image.

5. The method according to claim 4, characterized in that The adaptive threshold method is used to calculate the local threshold of the vortex histogram equalization image to obtain the vortex threshold image, including: Determining the window size according to the size and brightness distribution of the vortex histogram equalization image; Calculating a local threshold in each window of the vortex histogram equalization image; The vortex histogram equalization image is binarized according to the local threshold of each window to obtain the vortex threshold image.

6. The method according to claim 1, characterized in that The local gradient method is used to extract the texture of the vortex polar coordinate image to obtain the main texture direction of the vortex polar coordinate image, including: Using a pre-built smoothing operator to smooth the vortex polar coordinate image to obtain a vortex smoothed image; Using the optimized Sobel operator to calculate the initial local gradient result of the vortex smoothed image; Squaring and smoothing the initial local gradient result to obtain a second local gradient result; Squaring the initial local gradient result and smoothing the absolute value of the square result to obtain a third local gradient result; constructing a coherence factor and a quality factor according to the second local gradient result and the third local gradient result; Calculating a maximum weighted square gradient according to the coherence factor, the quality factor and the second local gradient result; Count the maximum weighted square gradient value in each direction and draw a gradient histogram; Smoothing the gradient histogram to obtain a gradient smoothed image; Performing square root processing on the direction corresponding to the maximum value of the maximum weighted square gradient value in the gradient smoothed image to obtain the main texture direction.

7. The method according to claim 6, characterized in that The smoothing operator R |2 The expression is: R |2 =B 2 S |2 B 4 In the formula, B 2 and B 4 represents the smoothing operator, S |2 It is downsampling processing; The Sobel operator D x The expression is: The initial local gradient result g' mn , the second local gradient result g″ mn And the third local gradient result g″′ mn for: g′ mn =(D x +iD y )(A) Where D y Indicates D x The transpose of , A is the vortex smoothed image, and the subscripts m and n represent the row and column numbers of the sub-image; The coherence factor c mn and quality factor r mn The expression is: Where p is the row number of the sub-image and q is the column number of the sub-image.

8. The method according to claim 1, characterized in that: Determining the polarity of the ocean vortex in the to-be-identified SAR image according to the main texture direction includes: determining a rotation direction of the ocean vortex according to the main texture direction; According to the rotation direction, the polarity of the ocean vortex in the SAR image to be identified is determined.

9. A device for identifying polarity of ocean eddies based on SAR satellite observation, characterized in that: include: A cropping module is used to crop the collected SAR image to be identified to obtain a vortex slice image; An element extraction module is used to extract vortex elements from the vortex slice image using an adaptive threshold method to obtain a vortex threshold image; A conversion module, used for performing polar coordinate conversion on the vortex threshold image with the vortex of the vortex threshold image as the center to obtain a vortex polar coordinate image; A texture extraction module, used for performing texture extraction on the vortex polar coordinate image by using a local gradient method to obtain a main texture direction of the vortex polar coordinate image; A determination module is used to determine the polarity of the ocean vortex in the SAR image to be identified according to the main texture direction.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 8.

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