CN114283318A - SAR image-based ocean front scale characteristic parameter inversion method and system - Google Patents
SAR image-based ocean front scale characteristic parameter inversion method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for inverting ocean front scale characteristic parameters based on SAR images, relating to the field of ocean mesoscale phenomenon remote sensing technology application and comprising the following steps: collecting an ocean front SAR image, extracting an ocean frontal area image, and filtering interference information; acquiring a binarization image of a marine front, wherein the binarization image of the marine front is used for extracting a marine front scale characteristic parameter; extracting a sea front edge and a space distribution image of the sea front edge according to a binarization image of the sea front edge, and acquiring a sea front scale characteristic parameter; the invention utilizes the bright line or dark line characteristics of the ocean front on the SAR image, and the provided inversion method effectively solves the problem that the ocean front band characteristics are not obvious on the SAR image and are difficult to directly obtain, and lays a technical foundation for the application of all-weather high-resolution SAR images in ocean front characteristic research.
Description
Technical Field
The invention relates to the field of application of a marine mesoscale phenomenon remote sensing technology, in particular to a method and a system for inverting marine forward scale characteristic parameters based on SAR images.
Background
The ocean front is a narrow transition zone between two or more bodies of water with distinct characteristics, and therefore its spatial distribution is characterized by a front length and a front width, which is also indicated by the horizontal gradients of environmental elements based on temperature, salinity, density, velocity, chlorophyll, color, or their higher order derivatives. However, with the development of aerospace technology, SAR satellite data resources are richer, and the advantages of all-weather, all-weather and high resolution play an important role in marine environment research. Due to the radiation divergence and shearing of the ocean frontal flow field interacting with the surface wave, the backscattering of the frontal zone is obviously different from that of other places, the SAR image is represented as a bright line or a dark line, the position of the ocean front is generally obtained by adopting an edge feature detection technology, further obtain the length information of the ocean front, but the characteristic of the frontal zone width is not obvious or not fully represented on the image, therefore, the method is difficult to directly obtain, the invention discloses an SAR image ocean front scale characteristic parameter inversion method based on multi-parameter threshold, firstly, the position of the ocean front is extracted based on the bright line or dark line characteristics of the SAR image of the ocean front to obtain the length information of the ocean front, and then acquiring the spatial distribution of the ocean front through multi-parameter threshold joint debugging, further acquiring the width information of the ocean front, and finally acquiring the isometric characteristic parameters of the front length and the front width of the ocean front. The sea surface temperature distribution data of the corresponding region shows that the method can acquire the sea front scale characteristic information at high precision, and provides a technical basis for the application of the sea SAR image in the sea front characteristic research.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an SAR image-based ocean front scale characteristic parameter inversion method, which is characterized by comprising the following steps of:
collecting an ocean front SAR image, extracting an ocean frontal area image, and filtering interference information;
acquiring a binarization image of a marine front, wherein the binarization image of the marine front is used for extracting a marine front scale characteristic parameter;
and extracting the ocean front edge and the space distribution image of the ocean front edge according to the binarization image of the ocean front edge, and acquiring the ocean front scale characteristic parameters.
Preferably, in the process of extracting the ocean frontal region image, the ocean frontal region image is extracted according to the characteristics of the upper bright lines or the dark lines of the ocean frontal region SAR image, and the speckle noise, the stripe noise and the high-frequency information of the ocean frontal region image are eliminated through median filtering and empirical mode decomposition.
Preferably, in the process of filtering out the interference information, the expression of median filtering is:
gi,j＝MedA{fi,j}＝Med{fi+r,j+s,(r,s)∈A,(i,j)∈I2}
wherein A is a filter window, fi,jTo input data, gi,jTo output data.
Preferably, in the process of filtering the interference information, the expression of the empirical mode decomposition is as follows:
EMD(I)＝I1+…+Ik+…+In+T
wherein I is the original signal, IkRepresenting the decomposed mode, and T is the variation trend of the original signal.
Preferably, in the process of acquiring the marine front zone binary image, acquiring a gradient distribution image by acquiring the backscattering coefficient gradient of the marine front SAR image;
based on the gradient distribution image, performing image gradient threshold segmentation by a maximum inter-class variance method to obtain a marine front edge binary image, wherein the pixel point value of a suspected front signal is 1, and the pixel point value of a non-front signal is 0;
the expression of the maximum inter-class variance method is:
g＝w0×w1+w1×(μ0-μ1)2
wherein, w0＝N0/(M×N)，w1＝N1/(M×N)，N0+N1＝×N，w0+w 11, g denotes the between-class variance, μ0To target average gray scale, mu1The image size of the gradient distribution image is M multiplied by N, the initialization threshold value is T, and the number of pixels in the image with the gray value of the pixel less than the initialization threshold value T is recorded as N0The number of pixels greater than the initialization threshold T is denoted by N1。
Preferably, in the process of extracting the ocean front, extracting a framework of the ocean front according to the binary image of the ocean front to obtain the ocean front;
and connecting the discontinuous parts of the ocean front edge, and cutting burrs to obtain a smooth and continuous ocean front edge, wherein the length of the ocean front edge is obtained according to the sum of the distances between all points of the ocean front edge.
Preferably, in the process of acquiring the length of the ocean front, the expression of the length of the ocean front is as follows:
wherein N represents the number of points on the front of the ocean front, loniAnd latiRepresenting the latitude and longitude of the ith point.
Preferably, in the process of extracting the spatial distribution image of the ocean front, a new ocean front binarization image is obtained by adjusting the initialization threshold value;
filtering out non-frontal zone information through a set zone threshold value, and keeping the frontal zone information with obvious characteristics;
by setting various structural element thresholds of morphology, closing operation of structural elements with different parameters is performed to close narrow gaps and long and thin gullies, eliminate small holes and fill cracks in contour lines.
Preferably, in the process of obtaining the characteristic parameters of the ocean front scale, performing edge extraction on the binary image of the ocean front frontal zone to obtain the edge position of the ocean front frontal zone;
according to the trend of the ocean front, obtaining the width information of the ocean front along longitude or latitude, wherein,
at equal latitudes the peak width is:
front_w＝|lona-lonb|×100.0
the peak width at equal longitude is:
front_w＝|lata-latb|×111.32
wherein (lon)a,lona)、(lonb,latb) The latitude and longitude of the points a and b of the ocean front are shown.
An SAR image-based ocean front scale characteristic parameter inversion system comprises:
the data extraction unit is used for acquiring an ocean front SAR image, extracting an ocean frontal area image and filtering interference information;
the device comprises a binarization image extraction unit, a binarization image extraction unit and a feature extraction unit, wherein the binarization image extraction unit is used for acquiring a sea front binarization image which is used for extracting sea front scale feature parameters;
and the characteristic extraction unit is used for extracting the ocean front line and the space distribution image of the ocean front zone according to the binarization image of the ocean front zone to acquire the ocean front scale characteristic parameters.
The invention discloses the following technical effects:
the invention provides an SAR image ocean front scale characteristic parameter inversion method based on multi-parameter threshold values by utilizing bright line or dark line characteristics of an ocean front on an SAR image, effectively solves the problem that the ocean front characteristic is not obvious and difficult to directly obtain on the SAR image, and lays a technical foundation for the application of full-time, all-weather and high-resolution SAR images in ocean front characteristic research.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of an inversion method according to the present invention;
FIG. 2 is a SAR image including a marine front phenomenon according to an embodiment of the present invention;
FIG. 3 is an SAR image of marine frontal area according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a marine frontal backscatter coefficient gradient profile according to an embodiment of the present invention.
Fig. 5 is an image of a result of a marine front detection according to an embodiment of the present invention;
FIG. 6 is an image of a sea front skeleton according to an embodiment of the present invention;
FIG. 7 is an image of the ocean front according to an embodiment of the present invention;
FIG. 8 is an image of a marine front for multi-parameter threshold detection in accordance with an embodiment of the present invention;
fig. 9 is a schematic diagram of the marine front frontal band width according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1 to 9, the present invention provides a method for inverting a characteristic parameter of an ocean front scale based on an SAR image, which is characterized by comprising the following steps:
collecting an ocean front SAR image, extracting an ocean frontal area image, and filtering interference information;
acquiring a binarization image of a marine front, wherein the binarization image of the marine front is used for extracting a marine front scale characteristic parameter;
and extracting the ocean front edge and the space distribution image of the ocean front edge according to the binarization image of the ocean front edge, and acquiring the ocean front scale characteristic parameters.
Further preferably, in the process of extracting the ocean frontal region image, the ocean frontal region image is extracted according to the characteristics of the upper bright lines or the dark lines of the ocean frontal region SAR image, and the speckle noise, the stripe noise and the high-frequency information of the ocean frontal region image are eliminated through median filtering and empirical mode decomposition.
Further preferably, in the process of filtering out the interference information, the expression of median filtering is:
gi,j＝MedA{fi,j}＝Med{fi+r,j+s,(r,s)∈A,(i,j)∈I2}
wherein A is a filter window, fi,jTo input data, gi,jTo output data.
Further preferably, in the process of filtering the interference information, the expression of the empirical mode decomposition is as follows:
EMD(I)＝I1+…+Ik+…+In+T
wherein I is the original signal, IkRepresenting the decomposed mode, and T is the variation trend of the original signal.
Further preferably, in the process of acquiring the marine front zone binary image, acquiring a gradient distribution image by acquiring a backscattering coefficient gradient of the marine front SAR image;
based on the gradient distribution image, performing image gradient threshold segmentation by a maximum inter-class variance method to obtain a marine front edge binary image, wherein the pixel point value of a suspected front signal is 1, and the pixel point value of a non-front signal is 0;
the expression of the maximum inter-class variance method is:
g＝w0×w1+w1×(μ0-μ1)2
wherein, w0＝N0/(M×N)，w1＝N1/(M×N)，N0+N1＝×N，w0+w 11, g denotes the between-class variance, μ0To target average gray scale, mu1The image size of the gradient distribution image is M multiplied by N, the initialization threshold value is T, and the number of pixels in the image with the gray value of the pixel less than the initialization threshold value T is recorded as N0The number of pixels greater than the initialization threshold T is denoted by N1。
Preferably, in the process of extracting the ocean front, extracting a framework of the ocean front according to the binary image of the ocean front to obtain the ocean front;
and connecting the discontinuous parts of the ocean front edge, and cutting burrs to obtain a smooth and continuous ocean front edge, wherein the length of the ocean front edge is obtained according to the sum of the distances between all points of the ocean front edge.
Further preferably, in the process of acquiring the length of the ocean front, the expression of the length of the ocean front is as follows:
wherein N represents the number of points on the front of the ocean front, loniAnd latiRepresenting the latitude and longitude of the ith point.
Further preferably, in the process of extracting the spatial distribution image of the ocean front, a new ocean front binarization image is obtained by adjusting the initialization threshold value;
filtering out non-frontal zone information through a set zone threshold value, and keeping the frontal zone information with obvious characteristics;
by setting various structural element thresholds of morphology, closing operation of structural elements with different parameters is performed to close narrow gaps and long and thin gullies, eliminate small holes and fill cracks in contour lines.
Further preferably, in the process of obtaining the ocean front scale characteristic parameters, performing edge extraction on the ocean front frontal zone binary image to obtain the edge position of the ocean front zone;
according to the trend of the ocean front, obtaining the width information of the ocean front along longitude or latitude, wherein,
at equal latitudes the peak width is:
front_w＝|lona-lonb|×100.0
the peak width at equal longitude is:
front_w＝|lata-latb|×111.32
wherein (lon)a,lona)、(lonb,latb) The latitude and longitude of the points a and b of the ocean front are shown.
An SAR image-based ocean front scale characteristic parameter inversion system comprises:
the data extraction unit is used for acquiring an ocean front SAR image, extracting an ocean frontal area image and filtering interference information;
the device comprises a binarization image extraction unit, a binarization image extraction unit and a feature extraction unit, wherein the binarization image extraction unit is used for acquiring a sea front binarization image which is used for extracting sea front scale feature parameters;
and the characteristic extraction unit is used for extracting the ocean front line and the space distribution image of the ocean front zone according to the binarization image of the ocean front zone to acquire the ocean front scale characteristic parameters.
Example 1: the invention provides a marine front scale characteristic parameter inversion method, which comprises the following steps:
s1, reading the SAR image, intercepting the ocean frontal zone image, and filtering interference information;
s2, preliminarily obtaining a marine front frontal zone binary image by adopting a marine front SAR image backscattering coefficient gradient threshold method;
s3, extracting a sea front skeleton, connecting the discontinuous parts, and shearing burrs to obtain a sea front edge, and calculating the length of the sea front edge according to the longitude and latitude of all points on the front edge;
s4, obtaining a space distribution image of the ocean front by adopting a multi-parameter threshold method;
and S5, acquiring the width information of the ocean front according to the edge position of the ocean front.
Step S1 specifically includes:
s11, the upper bright line or dark line of the selected ocean front SAR image is obvious in characteristic, as shown in figure 2;
s12, intercepting the ocean frontal zone image, as shown in figure 3;
and S13, eliminating speckle noise, stripe noise and high-frequency information of sea waves in the data through preprocessing such as median filtering, empirical mode decomposition and the like.
(1) Median filtering
gi,j＝MedA{fi,j}＝Med{fi+r,j+s,(r,s)∈A,(i,j)∈I2}
Wherein A is a filter window, fi,jTo input data, gi,jTo output data.
(2) Empirical mode decomposition
EMD(I)＝I1+…+Ik+…+In+T
Where I is the original signal, IkRepresenting the decomposed mode, and T is the variation trend of the original signal. Removing background signal from original signal to obtain new signal Inew：
Inew＝I1+…+Ik+…+In
Step S2 specifically includes:
s21, calculating the backscattering coefficient gradient of the SAR image to obtain a gradient distribution image;
and S22, performing image gradient threshold segmentation by adopting a maximum inter-class variance method to obtain a binary image of the ocean front.
Step S21 specifically includes performing backscattering coefficient gradient calculation on the marine frontal zone image to obtain a marine frontal zone backscattering coefficient gradient distribution image, as shown in fig. 4;
step S22 specifically includes obtaining a binarized image of the ocean front frontal band by maximum inter-class variance method image gradient threshold segmentation, as shown in fig. 5.
Assuming that the image size is M × N, the initialization threshold is T, and the number of pixels in the image with the gray-scale value smaller than the initialization threshold T is recorded as N0The number of pixels greater than the initialization threshold T is denoted by N1Then the target pixel is in proportion w of the whole image0Comprises the following steps:
w0＝N0/(M×N)
the proportion w of background pixels to the whole image1Comprises the following steps:
w1＝N1/(M×N)
wherein N is0+N1＝×N，w0+w1＝1。
The total average gray level of the image is mu, and the target average gray level is mu0Background mean gray level of mu1The inter-class variance is g, then there are
μ＝w0×μ0+w1×μ1
g＝w0×(μ0-μ)2+w1×(μ1-μ)2
The two simultaneous formulas can be obtained:
g＝w0×w1+w1×(μ0-μ1)2
and obtaining a threshold peak k which enables the inter-class variance g to be maximum by adopting a traversal method, and considering that the difference between the target and the background is maximum at the moment, and the gray level T at the moment is the optimal threshold.
And (4) carrying out binary image by using the threshold k, wherein the pixel point value of the suspected frontal surface signal is 1, and the pixel point value of the non-frontal surface signal is 0, thus obtaining a binary image.
Step S3 specifically includes:
s31, extracting a marine front belt framework to obtain a marine front belt line, as shown in figure 6;
s32, connecting and cutting burrs of the discontinuous part of the ocean front edge to obtain a smooth and continuous ocean front edge, as shown in figure 7;
and S33, calculating the sum of the distances between all points on the front to be used as the length (the unit is km) of the ocean front.
Wherein N represents the number of points on the front of the ocean front, loniAnd latiRepresenting the latitude and longitude of the ith point.
Step S4 specifically includes:
s41, adjusting the binarization threshold value in S2, and obtaining the binarization image of the ocean front frontal zone again;
s42, filtering out non-frontal zone information through a set zone threshold, and keeping the frontal zone information with obvious characteristics;
if the area of the region is A, the following are provided:
in the formula (I), the compound is shown in the specification,
and filtering the small signals when the area A is smaller than a set area threshold value.
S43, by setting thresholds of various structural elements of morphology, close operation of the structural elements with different parameters is performed to close narrow gaps and narrow gaps, eliminate small holes, and fill up cracks in the contour lines, as shown in fig. 8.
And (3) performing a closing operation on the set A by using the structural element B, wherein the closing operation is defined as:
the expansion operation is carried out on A by B, and then the corrosion operation is carried out on the result by B.
Step S5 specifically includes:
s51, extracting the edge of the binarized image of the marine front edge to obtain the edge position of the marine front edge;
and S52, determining the width information of the ocean front along the longitude or the latitude according to the trend of the ocean front. Fig. 9 shows a schematic view of the peak widths along the latitudes at equal longitudes.
The peak width at equal latitudes is (in km):
front_w＝|lona-lonb|×100.0
the peak width is (in km) when the longitudes are equal:
front_w＝|lata-latb|×111.32
wherein (lon)a,lata)、(lonb,latb) The latitude and longitude of the points a and b of the ocean front are shown.
The invention discloses a sea front scale characteristic parameter inversion method, which comprises the steps of extracting a sea front by utilizing bright line or dark line characteristics of the sea front on an SAR image, obtaining the length information of the sea front, obtaining a space distribution image of the sea front through multi-parameter threshold joint debugging such as a backscattering coefficient gradient threshold, morphological multi-structural element thresholds, a region filtering threshold and the like, further obtaining the width information of the sea front, and finally obtaining scale characteristic parameters such as the front length, the front width and the like of the sea front.
Claims (10)
1. An SAR image-based ocean front scale characteristic parameter inversion method is characterized by comprising the following steps:
collecting an ocean front SAR image, extracting an ocean frontal area image, and filtering interference information;
acquiring a binarization image of a marine front, wherein the binarization image of the marine front is used for extracting a marine front scale characteristic parameter;
and extracting the ocean front edge and the space distribution image of the ocean front edge according to the ocean front edge binaryzation image to obtain the ocean front scale characteristic parameters.
2. The SAR image-based ocean front scale feature parameter inversion method according to claim 1, characterized in that:
in the process of extracting the ocean frontal region image, the ocean frontal region image is extracted according to the characteristics of the upper bright lines or the dark lines of the ocean frontal region SAR image, and the spot noise, the stripe noise and the high-frequency information of the ocean frontal region image are eliminated through median filtering and empirical mode decomposition.
3. The SAR image-based ocean front scale feature parameter inversion method according to claim 2, characterized in that:
in the process of filtering out interference information, the expression of the median filtering is as follows:
gi,j＝MedA{fi,j}＝Med{fi+r,j+s,(r,s)∈A,(i,j)∈I2}
wherein A is a filter windowMouth, fi,jTo input data, gi,jTo output data.
4. The SAR image-based ocean front scale feature parameter inversion method according to claim 3, characterized in that:
in the process of filtering out the interference information, the expression of the empirical mode decomposition is as follows:
EMD(I)＝I1+…+Ik+…+In+T
wherein I is the original signal, IkRepresenting the decomposed mode, and T is the variation trend of the original signal.
5. The SAR image-based ocean front scale feature parameter inversion method according to claim 4, characterized in that:
in the process of obtaining a marine front zone binary image, obtaining a gradient distribution image by obtaining the backscattering coefficient gradient of the marine front SAR image;
based on the gradient distribution image, performing image gradient threshold segmentation by a maximum inter-class variance method to obtain the marine front edge binary image, wherein the pixel point value of a suspected front signal is 1, and the pixel point value of a non-front signal is 0;
the expression of the maximum inter-class variance method is as follows:
g＝w0×w1+w1×(μ0-μ1)2
wherein, w0＝N0/(M×N)，w1＝N1/(M×N)，N0+N1＝×N，w0+w11, g denotes the between-class variance, μ0To target average gray scale, mu1The image size of the gradient distribution image is M multiplied by N, the initialization threshold value is T, and the number of pixels in the image with the gray value of the pixel less than the initialization threshold value T is recorded as N0The number of pixels greater than the initialization threshold T is denoted by N1。
6. The SAR image-based ocean front scale feature parameter inversion method according to claim 5, characterized in that:
extracting a sea front framework according to the sea front binarization image in the process of extracting the sea front to obtain the sea front;
and connecting the discontinuous parts of the ocean front edge, cutting burrs to obtain a smooth and continuous ocean front edge, wherein the length of the ocean front edge is obtained according to the sum of the distances between all points of the ocean front edge.
7. The SAR image-based ocean front scale feature parameter inversion method according to claim 6, characterized in that:
in the process of acquiring the length of the ocean front, the expression of the length of the ocean front is as follows:
wherein N represents the number of points on the front of the ocean front, loniAnd latiRepresenting the latitude and longitude of the ith point.
8. The SAR image-based ocean front scale feature parameter inversion method according to claim 7, characterized in that:
in the process of extracting the spatial distribution image of the ocean front, a new ocean front binarization image is obtained by adjusting the initialization threshold value;
filtering out non-frontal zone information through a set zone threshold value, and keeping the frontal zone information with obvious characteristics;
by setting various structural element thresholds of morphology, closing operation of structural elements with different parameters is performed to close narrow gaps and long and thin gullies, eliminate small holes and fill cracks in contour lines.
9. The SAR image-based ocean front scale feature parameter inversion method according to claim 8, characterized in that:
in the process of obtaining the ocean front scale characteristic parameters, performing edge extraction on the ocean front frontal zone binary image to obtain the edge position of the ocean front frontal zone;
according to the trend of the ocean front, obtaining the width information of the ocean front along longitude or latitude, wherein,
at equal latitudes the peak width is:
front_w＝|lona-lonb|×100.0
the peak width at equal longitude is:
front_w＝|lata-latb|×111.32
wherein (lon)a,lata)、(lonb,latb) The latitude and longitude of the points a and b of the ocean front are shown.
10. SAR image-based ocean front scale characteristic parameter inversion system is characterized by comprising:
the data extraction unit is used for acquiring an ocean front SAR image, extracting an ocean frontal area image and filtering interference information;
the device comprises a binarization image extraction unit, a binarization image extraction unit and a binarization processing unit, wherein the binarization image extraction unit is used for acquiring a sea front binarization image which is used for extracting sea front scale characteristic parameters;
and the characteristic extraction unit is used for extracting the ocean front line and the space distribution image of the ocean front zone according to the ocean front zone binarization image and acquiring the ocean front scale characteristic parameters.
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| CN116051416A (en) * | 2023-01-30 | 2023-05-02 | 中国科学院空天信息创新研究院 | SAR image-based ocean front automatic detection and identification method and device |
| CN116203277A (en) * | 2023-05-06 | 2023-06-02 | 中国海洋大学 | Sea surface small-scale flow field measurement method based on PTV and PIV technologies |
| CN117975296A (en) * | 2024-04-01 | 2024-05-03 | 自然资源部第一海洋研究所 | Satellite remote sensing detection extraction and identification method and system for abnormal ocean fronts |
| CN117975296B (en) * | 2024-04-01 | 2024-06-11 | 自然资源部第一海洋研究所 | Satellite remote sensing detection extraction and identification method and system for abnormal ocean fronts |
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| CN116051416A (en) * | 2023-01-30 | 2023-05-02 | 中国科学院空天信息创新研究院 | SAR image-based ocean front automatic detection and identification method and device |
| CN116051416B (en) * | 2023-01-30 | 2023-10-20 | 中国科学院空天信息创新研究院 | SAR image-based ocean front automatic detection and identification method and device |
| CN116203277A (en) * | 2023-05-06 | 2023-06-02 | 中国海洋大学 | Sea surface small-scale flow field measurement method based on PTV and PIV technologies |
| CN117975296A (en) * | 2024-04-01 | 2024-05-03 | 自然资源部第一海洋研究所 | Satellite remote sensing detection extraction and identification method and system for abnormal ocean fronts |
| CN117975296B (en) * | 2024-04-01 | 2024-06-11 | 自然资源部第一海洋研究所 | Satellite remote sensing detection extraction and identification method and system for abnormal ocean fronts |
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