Long-delay high-resolution remote sensing image ocean vortex motion extraction method and system

Abstract

The application discloses a long-time-delay high-resolution remote sensing image ocean vortex motion extraction method and system, relates to the synthetic aperture radar signal processing field and the ocean remote sensing application field, and the method comprises the following steps: three high-resolution time series remote sensing image acquisition and pretreatment; vortex velocity fields are extracted from the first remote sensing image and the second remote sensing image; the prior velocity field between the second remote sensing image and the third remote sensing image is extracted from the vortex velocity field; feature extraction is carried out on the second remote sensing image and the third remote sensing image; parameters are set according to the prior velocity field, and the vortex velocity field is extracted by tracking the features in the second remote sensing image and the third remote sensing image. The application makes full use of the correlation information between time series remote sensing images, improves the time resolution of vortex motion extraction, and lays a foundation for long-time-series ocean vortex space-time evolution information extraction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of synthetic aperture radar (SAR) signal processing and ocean remote sensing application, and more particularly to a long time delay high resolution remote sensing image ocean vortex motion extraction method and system. BACKGROUND

[0002] Ocean vortexes exist universally in the global ocean, and the kinetic energy carried by the ocean vortexes accounts for 80% of the total kinetic energy of the ocean. Ocean vortexes exist not only in open waters but also in marginal ice zones, and the vortexes in the marginal ice zones can accelerate the melting of sea ice. Therefore, research on vortex motion extraction is of great significance to understanding the thermal and salinity circulation of the global ocean and climate change.

[0003] With the rapid development of satellite remote sensing technology, a series of high-resolution remote sensing satellites such as the Sentinel series, the GF series, TerraSAR-X, Tandem-X, etc. have been launched, and a large amount of high-resolution remote sensing images have been acquired. Ocean vortexes have strong wrapping power and can redistribute oil films, ice floes, etc., and thus can be shown in high-resolution remote sensing images. By tracking the ocean features such as oil films and ice floes in remote sensing images with a certain time delay, the vortex motion information can be extracted, but in the case of long time delay, the performance of the vortex motion extraction method is restricted.

[0004] The time delay refers to the time delay (time interval) experienced from the acquisition of the first remote sensing image to the acquisition of the second remote sensing image. In this definition, 2h (including 2h) is defined as short time delay, and 2-10h is defined as long time delay. Commonly used ocean vortex motion extraction methods such as the maximum cross-correlation method approximate the motion of pixels within the template window as a constant, and find the best match of the template window by correlation calculation of two remote sensing images with a certain time delay, which can accurately obtain the vortex motion information. However, the traditional maximum cross-correlation method requires short time delay between two high-resolution remote sensing images, so that the rotational motion can be approximated as translation. For long time delay high-resolution remote sensing images, the vortex has a large rotational motion, and the maximum cross-correlation method is no longer applicable. Therefore, there is an urgent need for a long time delay high-resolution remote sensing image ocean vortex motion extraction method that can fully utilize the correlation information in time series remote sensing images and improve the time resolution of vortex motion extraction. SUMMARY

[0005] To solve the above technical problems, the main purpose of the present application is to provide a long time delay high resolution remote sensing image ocean vortex motion extraction method and system, which can fully utilize the correlation information between time series remote sensing images and improve the time resolution of vortex motion extraction, and lay a foundation for long time series ocean vortex spatiotemporal evolution information extraction.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images includes the following steps:

[0008] Step 1: Acquire and preprocess three high-resolution time-series remote sensing images to obtain the first remote sensing image, the second remote sensing image, and the third remote sensing image;

[0009] Step 2: Extract the vortex velocity field from the first and second remote sensing images;

[0010] Step 3: Extract the prior velocity field between the second and third remote sensing images from the vortex velocity field;

[0011] Step 4: Extract features from the second and third remote sensing images;

[0012] Step 5: Set parameters based on the prior velocity field, and calculate the vortex velocity field by tracking the sea surface features in the second and third remote sensing images.

[0013] Furthermore, in step 1, there is a short time delay between the first remote sensing image and the second remote sensing image, defined as ≤2h, and a long time delay between the second remote sensing image and the third remote sensing image, defined as 2-10h.

[0014] Furthermore, the three remote sensing images are filtered and registered, and the size of the extracted vortex region of interest is M×N, where M and N represent the number of pixels in the vertical and horizontal directions of the extracted region, respectively.

[0015] Furthermore, in step 2, assuming the time delay between the first and second remote sensing images is t1, the vortex velocity field V at time t1 is obtained by tracking the motion of sea surface features in the first and second remote sensing images. t1 .

[0016] Furthermore, in step 3, assuming the time delay between the second and third remote sensing images is t2, the vortex velocity field at time t1 is... Time-varying processing was performed to obtain the vortex prior velocity field within time t2. Time-varying processing includes:

[0017] From the vortex velocity field V at time t1 t1 After removing the time-varying wind field and background flow field components, the vortex velocity component V is obtained. eddy :

[0018]

[0019] in, denotes the sea surface wind field in t1 time, denotes the background flow field in t1 time, and β and γ are the influence factors of the sea surface wind field and the background flow field, respectively;

[0020] Assuming that the characteristics of the vortex remain unchanged during the study period, the vortex component V eddy is added to the average wind field and the influence of the background flow field in t2 time, and the vortex prior velocity field in t2 time is obtained

[0021]

[0022] wherein, denotes the sea surface wind field in t2 time, denotes the background flow field in t2 time.

[0023] Further, the step 4 of performing feature extraction on the second remote sensing image and the third remote sensing image comprises the following steps:

[0024] The gray level co-occurrence matrix G d,α (i,j) is calculated for the second remote sensing image and the third remote sensing image, respectively.

[0025]

[0026] wherein, i and j represent the gray level values of two pixels in the image with adjacent distance d and direction a, P d,α (i,j) represents the number of occurrences of the pixel pair (i,j), and K is the quantized gray level of the image.

[0027] Four texture features of the second remote sensing image and the third remote sensing image are calculated based on the gray level co-occurrence matrix, wherein Homogeneity represents homogeneity, Entropy represents entropy, Energy represents energy, and Dissimilarity represents dissimilarity. The calculation method of the texture features is as follows:

[0028]

[0029]

[0030]

[0031]

[0032] The calculation results of the texture features of the second remote sensing image and the third remote sensing image are input into the trained support vector machine model, and the support vector machine is used to classify the sea surface features and the background, to obtain the sea surface feature extraction results of the second remote sensing image and the third remote sensing image: image I2 and image I3.

[0033] Further, in step 5, the image I2 is divided into several small template windows T, and the rotation angle θ of the template window is set according to the prior velocity field, θ∈[ω min ·t2-Δθ,ω max ·t2+Δθ],ω max and ω min respectively represent the maximum and minimum of the maximum rotation angular velocity of the vortex in the east, west, south and north four directions calculated from the prior velocity field, and Δθ is the prediction angle deviation.

[0034] The search area S of the image I3 is set according to the prior velocity field, and then the template window T of the image I2 is rotated by an angle θ, and then slid in the search area of the image I3, and the cross-correlation coefficient r θ (p,q) is calculated when the template window slides to a certain position.

[0035]

[0036] Wherein, (X T ,Y T ) is the center of the template window T, (X S ,Y S ) is the center of the predicted search window S, S' represents the corresponding search sub-window of the template window in the search area, p and q respectively represent the displacement of the center of the search sub-window relative to the center of the search window in the distance direction and the azimuth direction in the sliding process of the template window, cov() represents the covariance, and var() represents the variance.

[0037] The sea surface feature displacement vector is defined as the displacement vector with the starting point at the center of the template window of the image I2 and the ending point at the center of the search sub-window with the maximum cross-correlation coefficient of the image I3, and the displacement is divided by the time delay t2 between the second remote sensing image and the third remote sensing image to obtain the vortex velocity vector, and the operation of calculating the cross-correlation coefficient r θ (p,q) is repeated by selecting the next template window T until all the template windows T in the image I2 are traversed, that is, the vortex velocity field in the t2 time is obtained.

[0038] The application also provides a system for realizing the long-time-delay high-resolution remote sensing image ocean vortex motion extraction method.

[0039] The first and second remote sensing image vortex velocity field extraction module is used for vortex velocity field extraction of the first remote sensing image and the second remote sensing image.

[0040] The prior velocity field extraction module is used for extracting the prior velocity field of the second remote sensing image and the third remote sensing image from the vortex velocity field.

[0041] The sea surface feature extraction module is used for sea surface feature extraction on the second remote sensing image and the third remote sensing image.

[0042] The second and third remote sensing image vortex velocity field extraction module is used for extracting a vortex velocity field between the second remote sensing image and the third remote sensing image.

[0043] The present application has the following beneficial effects:

[0044] 1. The present application solves the problem that long time delay high resolution remote sensing images cannot be used to obtain vortex motion information, starting from the prior information contained in time series images.

[0045] 2. The present application improves the time resolution of vortex motion extraction, starting from the prior information contained in time series images.

[0046] 3. The present application is simple in system and feasible in method, and lays a foundation for subsequent long time series ocean vortex spatio-temporal evolution information extraction, starting from the prior information contained in time series images. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a long time delay high resolution remote sensing image ocean vortex motion extraction method flowchart diagram provided by an embodiment of the present application;

[0048] Figure 2 is a detailed flowchart diagram of the long time delay high resolution remote sensing image ocean vortex motion extraction method provided by an embodiment of the present application;

[0049] Fig. 3(a) is a region of interest of a Sentinel-1A SAR image after pre-processing in an embodiment of the present application;

[0050] Fig. 3(b) is a region of interest of a Sentinel-1B SAR image after pre-processing in an embodiment of the present application;

[0051] Fig. 3(c) is a region of interest of a Sentinel-2B MSI image after pre-processing in an embodiment of the present application;

[0052] Fig. 4(a) is a vortex velocity field result extracted from a first remote sensing image and a second remote sensing image in an embodiment of the present application;

[0053] Fig. 4(b) is a prior velocity field result of labeling four direction vortex maximum rotation angular velocity extracted in an embodiment of the present application;

[0054] Fig. 5(a) is a sea ice extraction result of a Sentinel-1B SAR image region of interest in an embodiment of the present application;

[0055] Fig. 5(b) is a sea ice extraction result of a region of interest of a Sentinel-2B MSI image in an embodiment of the present application;

[0056] Figure 6 is a vortex velocity field result extracted from the second remote sensing image and the third remote sensing image in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the objects, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to specific embodiments and the accompanying drawings.

[0058] The existing sea vortex motion extraction method mainly extracts vortex motion by obtaining the correlation information between adjacent time phase remote sensing images. The most commonly used method is the maximum cross-correlation method, but this method requires that two high-resolution remote sensing images have a short time delay, so that the rotational motion can be approximated as translation. For long time delay high resolution remote sensing images, the vortex has a large rotational motion, and the maximum cross-correlation method is no longer applicable. At this time, it is necessary to develop a vortex motion extraction method suitable for long time delay high resolution remote sensing images to improve the time resolution of vortex motion extraction.

[0059] Based on the above reasons, the present application discloses a long time delay high resolution remote sensing image sea vortex motion extraction method and system.

[0060] In one aspect, as shown in Figure 1 The present application provides a long time delay high resolution remote sensing image sea vortex motion extraction method, which comprises the following steps:

[0061] Step 1, three high resolution time series remote sensing images are acquired and preprocessed;

[0062] Step 2, vortex velocity field is extracted from the first remote sensing image and the second remote sensing image;

[0063] Step 3, the prior velocity field between the second remote sensing image and the third remote sensing image is extracted from the vortex velocity field;

[0064] Step 4, feature extraction is performed on the second remote sensing image and the third remote sensing image;

[0065] Step 5, parameters are set according to the prior velocity field, and vortex velocity field is calculated by tracking the sea surface features in the second remote sensing image and the third remote sensing image.

[0066] In some embodiments, two scenes of Sentinel-1 SAR images are acquired as the first remote sensing image and the second remote sensing image, and one scene of Sentinel-2 MSI image is acquired as the third remote sensing image, wherein the time delay between the first remote sensing image and the second remote sensing image is 50 min, and the time delay between the second remote sensing image and the third remote sensing image is about 6 h.

[0067] The two scenes of SAR images are subjected to radiometric calibration and filtering processing, the MSI image is subjected to filtering and resampling processing, and the three scenes of remote sensing images are registered. The size of the vortex region of interest intercepted after the preprocessing is MxN, and M and N respectively represent the number of pixel points of the intercepted region in the vertical direction and the horizontal direction.

[0068] Suppose the time delay between the first remote sensing image and the second remote sensing image is t1, by tracking the motion of the sea surface features in the first remote sensing image and the second remote sensing image, the vortex velocity field at t1 time is obtained The velocity field includes not only the vortex rotation velocity component, but also the contribution of the wind field and the background flow field to the velocity field.

[0069] Suppose the time delay between the second remote sensing image and the third remote sensing image is t2, the above-mentioned average vortex velocity field is subjected to time-varying processing to obtain the vortex prior velocity field at t2 time First, the wind field and the background flow field component with time-varying nature are removed from the vortex velocity field at t1 time to obtain the vortex velocity component V eddy :

[0070]

[0071] wherein, represents the sea surface wind field at t1 time, represents the background flow field at t1 time, and β and γ are respectively the sea surface wind field and the background flow field influence factor;

[0072] Suppose the characteristics of the vortex remain unchanged within the research period, but the wind field and the background flow field have time-varying nature, and the change speed of the wind field is much faster than that of the background flow field. The average wind field and the background flow field at t2 time are added to the vortex velocity component V eddy to obtain the vortex prior velocity field at t2 time

[0073]

[0074] wherein, represents the sea surface wind field at t2 time, represents the background flow field at t2 time.

[0075] Due to the long time delay between the second remote sensing image and the third remote sensing image, the imaging conditions have changed greatly, so before extracting the average vortex velocity field in t2 time, the second remote sensing image and the third remote sensing image are subjected to feature extraction, including three parts of calculation of gray level co-occurrence matrix, texture feature extraction and support vector machine classification. d,α The calculation method of (i,j) is as follows:

[0076]

[0077] Wherein, i and j represent the gray values of two pixels with adjacent distance d and direction α in the image, P d,α (i,j) represents the number of occurrence of pixel pair (i,j), and K is the quantized gray level of the image;

[0078] Four texture features of the second remote sensing image and the third remote sensing image are calculated based on the gray level co-occurrence matrix, wherein Homogeneity represents homogeneity, Entropy represents entropy, Energy represents energy, and Dissimilarity represents dissimilarity, and the calculation method of the texture features is as follows:

[0079]

[0080]

[0081]

[0082]

[0083] The calculation results of the texture features of the second remote sensing image and the third remote sensing image are input into the trained support vector machine model, and the support vector machine is used to classify the sea surface features and the background, so as to obtain the feature extraction results of the second remote sensing image and the third remote sensing image: image I2 and image I3.

[0084] On the basis of the feature extraction results of the sea surface, the image I2 is divided into a plurality of small template windows T, and the template window rotation angle θ is set according to the prior velocity field, θ∈[ω min ·t2-Δθ,ω max ·t2+Δθ], ω max And ω min Respectively represent the maximum value and the minimum value of the maximum rotation angular velocity of the vortex in the east, west, south and north four directions calculated from the prior velocity field, and Δθ is the prediction angle deviation.

[0085] According to the prior velocity field, a search area S is set in the image I3, then the template window T of the image I2 is rotated by an angle θ, and then slides in the search area of the image I3, and the cross-correlation coefficient r is calculatedθ (p,q):

[0086]

[0087] where (X T ,Y T ) is the center of the template window T, (X S ,Y S ) is the center of the search window S, S' represents the corresponding search sub-window of the template window in the search area, p and q represent the displacement of the center of the search sub-window relative to the center of the search window in the distance direction and the azimuth direction during the sliding of the template window, cov() represents the covariance, and var() represents the variance;

[0088] The sea surface feature displacement vector is defined as a displacement vector with the starting point at the center of the template window of the image I2 and the ending point at the center of the search sub-window of the image I3 with the maximum cross-correlation coefficient, and the displacement is divided by the time delay t2 between the second remote sensing image and the third remote sensing image, so as to obtain the vortex velocity vector. The next template window T is selected to repeat the above operation until all the template windows in the image I2 are traversed, so as to obtain the vortex velocity field in the t2 time.

[0089] In another aspect, the present application provides a long-time-delay high-resolution remote sensing image ocean vortex motion extraction system, which comprises:

[0090] A first and second remote sensing image vortex velocity field extraction module is used for vortex velocity field extraction of the first remote sensing image and the second remote sensing image.

[0091] A prior velocity field extraction module is used for extracting the prior velocity field of the second remote sensing image and the third remote sensing image from the vortex velocity field.

[0092] A sea surface feature extraction module is used for sea surface feature extraction of the second remote sensing image and the third remote sensing image.

[0093] A second and third remote sensing image vortex velocity field extraction module is used for extracting the vortex velocity field between the second remote sensing image and the third remote sensing image.

[0094] The long-time-delay high-resolution remote sensing image ocean vortex motion extraction method and system provided by the present application are described in further detail through specific embodiments as follows:

[0095] As Figure 2As shown, first, the embodiment takes the ocean eddy in the marginal ice zone as an example, selects three time series of remote sensing images, the first two are Sentinel-1 SAR images, the time delay t1 ≈ 50 min, the third remote sensing image is a Sentinel-2 MSI remote sensing image, and the time delay t2 between the second remote sensing image and the third remote sensing image is about 6 h. After preprocessing the above three remote sensing images, the interested eddy region is intercepted as shown in FIG. 3(a), FIG. 3(b) and FIG. 3(c).

[0096] The first two short time delay remote sensing images are selected, and the t1 time eddy velocity field is obtained by tracking the movement of sea ice, as shown in FIG. 4(a). The velocity field contains the contribution of the sea surface wind field and the background flow field. It is assumed that the rotation characteristics of the eddy remain unchanged within the study period of the embodiment, while the wind field and the background flow field are time-varying, and the change rate of the flow field is much lower than that of the wind field. Since the time resolution of the available sea surface wind field data is 1 h, the time resolution of the background flow field is 1 day, and the study time span in the embodiment is only 7 h, therefore, in the embodiment, only the influence of the sea surface wind field is considered when the time-varying is processed. The t1 time eddy rotation component is obtained by removing the t1 time wind field influence from the t1 time eddy velocity field, and then the t2 time average wind field influence is added to obtain the t2 time eddy prior velocity field result as shown in FIG. 4(b), which shows the maximum rotation angular velocity of the eddy in four directions.

[0097] Since the SAR image and the MSI image have a long time delay, the imaging conditions have changed greatly, so the support vector machine model is used for sea ice-seawater classification, and then the sea ice is extracted according to the classification result. The sea ice extraction results of the second SAR image and the MSI image are shown in FIG. 5(a) and FIG. 5(b). Finally, based on the sea ice extraction result, the rotation angle and the search area of the template window are set according to the prior velocity field, and the average eddy velocity field in t2 time is extracted as shown in Figure 6 .

[0098] Thus, the long time delay high resolution remote sensing image ocean eddy motion extraction is completed.

[0099] The above described specific embodiments further illustrate the purpose, technical solutions and advantages of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images, characterized in that, Includes the following steps: Step 1: Acquire and preprocess three high-resolution time-series remote sensing images to obtain the first remote sensing image, the second remote sensing image, and the third remote sensing image; Step 2: Extract the vortex velocity field from the first and second remote sensing images; assume the time delay between the first and second remote sensing images is... By tracking the movement of sea surface features in the first and second remote sensing images, the following can be obtained: vortex velocity field of time ; Step 3: Extract the prior velocity field between the second and third remote sensing images from the vortex velocity field; assume the time delay between the second and third remote sensing images is... ,right vortex velocity field of time Obtain by performing time-varying processing Vortex prior velocity field over time Time-varying processing includes: (1) From vortex velocity field of time After removing the time-varying wind field and background flow field components, the vortex velocity components are obtained. : ； in, express Sea surface wind field over a period of time express Background flow field over time, and These are the influencing factors of sea surface wind field and background current field, respectively; (2) Assuming that the characteristics of the vortex remain unchanged during the study period, in the vortex component Add to The influence of the average wind field and background flow field over a time period was obtained. Vortex prior velocity field over time : ； in, express Sea surface wind field over a period of time express Background flow field over time; Step 4: Extract features from the second and third remote sensing images; Step 5: Set parameters based on the prior velocity field, and calculate the vortex velocity field by tracking the sea surface features in the second and third remote sensing images.

2. The method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images according to claim 1, characterized in that, In step 1, there is a short time delay between the first remote sensing image and the second remote sensing image, and the short time delay is defined as follows: There is a long time delay between the second and third remote sensing images, defined as 2-10 hours.

3. The method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images according to claim 2, characterized in that, In step 1, the three remote sensing images are filtered and registered, and the size of the extracted vortex region of interest is [size missing]. , and These represent the number of pixels in the vertical and horizontal directions of the cropped area, respectively.

4. The method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images according to claim 3, characterized in that, Step 4, which involves feature extraction from the second and third remote sensing images, includes the following steps: (1) Calculate the gray-level co-occurrence matrix for the second and third remote sensing images respectively. : ； in, and Indicates the distance between adjacent elements in the image. , direction is The grayscale values ​​of the two pixels, Represents pixel pairs Number of times it appears It represents the quantized gray level of the image; (2) Based on the gray-level co-occurrence matrix, calculate four texture features of the second and third remote sensing images, where Homogeneity represents homogeneity, Entropy represents entropy, Energy represents energy, and Dissimilarity represents dissimilarity. The calculation method of the four texture features is as follows: ； ； ； ； (3) Input the calculated texture features of the second and third remote sensing images into the trained support vector machine model, and use the support vector machine to classify the sea surface features and background to obtain the feature extraction results of the second and third remote sensing images: Image and images .

5. The method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images according to claim 4, characterized in that, In step 5, the image Divided into several small template windows The template window rotation angle is set according to the prior velocity field. , , and These represent the maximum and minimum values ​​of the vortex's maximum rotational angular velocities in the four cardinal directions, calculated from the prior velocity field. To predict angular deviation.

6. The method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images according to claim 5, characterized in that, In step 5, based on the prior velocity field in the image Set search area Then the image template window Rotation angle Later in the image The algorithm slides within the search area and calculates the cross-correlation coefficient at a certain position. : ； in, For template window The center, For search window The center, This indicates the search sub-window corresponding to the template window within the search area. and These represent the displacements of the center of the search sub-window relative to the center of the search window in the distance and azimuth directions, respectively, during the sliding process of the template window. Describing covariance, Indicates variance.

7. The method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images according to claim 6, characterized in that, In step 5, the sea surface feature displacement vector is defined as starting from the image. The center and endpoint of the template window are in the image. The displacement vector at the center of the search sub-window with the highest cross-correlation number is used to remove the bit by the time delay between the second and third remote sensing images. This obtains the vortex velocity vector; select the next template window. Repeatedly calculate cross-correlation coefficients The operation continues until the image is... All template windows Once the traversal is complete, the result will be obtained. The vortex velocity field over time.

8. A system for implementing the method for extracting ocean eddy motion from long-delay, high-resolution remote sensing images as described in any one of claims 1-7, characterized in that, include: The first and second remote sensing image vortex velocity field extraction modules are used for extracting the vortex velocity field of the first and second remote sensing images. The prior velocity field extraction module is used to extract the prior velocity fields of the second and third remote sensing images from the vortex velocity field; The sea surface feature extraction module is used to extract sea surface features from the second and third remote sensing images; The second and third remote sensing image vortex velocity field extraction module is used to extract the vortex velocity field between the second and third remote sensing images.

Images