A method for estimating wave direction and wave period from X-band radar images

Abstract

The present invention discloses a method for estimating wave direction and wave period from X-band radar images. The method comprises the following steps: dividing each X-band radar image into multiple sub-images, extracting the texture features of the sub-images using a two-dimensional empirical curve transform (2D-ECT), and decomposing the sub-images into multiple two-dimensional curve components; selecting the curvelet component with the highest correlation with the sub-image and estimating the wave direction using an empirical ridge transform (ERT); simultaneously, eliminating the 180° ambiguity of the wave direction using a cross-correlation coefficient; calculating the periodogram power spectrum of the sub-images after extracting the texture features to estimate the wave period; then, obtaining the frequency corresponding to the maximum power spectral density in the azimuth pixel of the sub-image, and estimating the wave period by the reciprocal of the frequency. This estimation method can effectively obtain the texture features of ocean waves and provides an effective means and method for obtaining high-precision ocean wave direction and wave period.

Description

Technical Field

[0001] The invention belongs to the technical field of ocean remote sensing and relates to a method for estimating wave direction and wave period from an X-band radar image. Background Art

[0002] With the rapid development of ocean development and maritime transportation, accurately obtaining the characteristics of waves (including wave direction and wave period) has become crucial in the fields of marine meteorological forecasting, ship navigation, and offshore structure design. At present, X-band ocean radar has been widely used to obtain wave parameters because of its advantages such as being unaffected by weather conditions and being able to monitor a large area of ​​the ocean surface around the clock. However, traditional wave parameter inversion methods usually face problems such as low accuracy and complex data processing when dealing with complex ocean environments. The two-dimensional empirical wavelet transform is an adaptive time-frequency analysis method that can effectively decompose the different frequency components in radar images, thereby more accurately extracting the direction and period information of the waves. Compared with traditional methods, the two-dimensional empirical wavelet transform can better capture the local characteristics of waves, especially when the ocean environment is complex and the wave parameters vary widely. Summary of the Invention

[0003] The purpose of the present invention is to propose an improved method for estimating wave direction and wave period, in order to solve the problem of low accuracy in the prior art.

[0004] In order to solve the above technical problems, the present invention proposes a method for estimating wave direction and wave period from X-band radar images, comprising the following steps:

[0005] Step 1: Radar original wavefront image acquisition. Use the navigation radar to collect a set of original radar wavefront image sequences and store them according to the radar protocol to obtain the radar wavefront image in polar coordinates. ,in, is the distance from the sea surface point to the radar, is the azimuth;

[0006] Step 2: Select the wave parameter inversion area. In the effective wave texture image screened from the X-band radar wave surface image, select a sub-area with a distance range of d1*d2 , t=1,2,…,S is used as the wave parameter inversion area. Convert to Cartesian coordinates and get S wave inversion areas in Cartesian coordinates ;

[0007] Step 3: Radar sub-image texture feature extraction. For the selected S wave inversion areas Perform a 2D Empirical Curvelet Transform (2D-ECT). Each sub-inversion region divided in step 2 is decomposed into multiple curvelet component images (curvelet coefficients). Image processing includes co-frequency interference, target interference, and wave texture feature extraction to obtain the processed radar wavefront image.

[0008] Step 4: Estimate the peak direction. Perform a correlation test on the decomposed curvelet component image in each sub-region in step 3 and the original sub-region, retain the curvelet component image with the greatest correlation with each sub-region, use the Empirical Ridgelet Transform (ERT) on the largest curvelet component image in each sub-region, and then solve the standard deviation of the pixel intensity in each projection direction in each sub-region image. The direction with the largest standard deviation is taken as the dominant texture direction. For all sub-region images of each radar image, the average standard deviation of all sub-images after the ERT method is taken for each projection direction, and the direction with the largest average standard deviation is taken as the rough estimate direction. ,choose exist The sub-image within the predetermined deviation range is used, and the median value of the selected sub-image is used as the final texture dominant direction of a radar image. ;

[0009] Preferably, select exist The sub-image within the range.

[0010] Step 5: Estimate the peak period. Taking the center point of the sub-region image of the final texture dominant direction as the center, expand the sub-image to Pixel size, solve the periodogram power spectrum density for each column of pixel data in the expanded sub-image, obtain the frequency corresponding to the maximum value of the power spectrum density of each column, and then calculate the maximum value of each column frequency as the final frequency of the radar image, and then estimate the peak period;

[0011] Furthermore, the step 3 may include:

[0012] Step 31: Sub-region by 2D-ECT method Perform feature extraction, Perform pseudo-polar coordinate transformation on the pseudo grid. When the frequency adopted on the pseudo grid is , assuming that the image input pixels are N*N, the pseudo-polar Fourier transform is:

[0013]

[0014] Where x, y are the coordinate values ​​of the sub-region, N is the image pixel size of the sub-region, is a pseudo-polar coordinate transformation.

[0015] Step 32: Perform Fourier edge detection on the pseudo-polar Fourier transform, assuming the number of scales ( ) and the number of angular sectors ( ) is known, and the scale boundary is , the angle boundary is , generate an adaptive filter to filter each sub-band coefficient of the empirical curvelet decomposition to facilitate the necessary processing of each high and low frequency coefficient. Expressed as:

[0016]

[0017] In the formula is an arbitrary function in [0,1] that satisfies the following relationship:

[0018]

[0019] In the formula To ensure that two consecutive transition intervals do not overlap, the following conditions must be met:

[0020]

[0021] Among them, min() is the minimum function.

[0022] A polar wedge in the Fourier domain (n and m are the scale and angle index respectively) is a radial window and a polar window The product of hour, Expressed as:

[0023]

[0024] if hour:

[0025]

[0026] Polar Window Expressed as:

[0027]

[0028] in, , are polar coordinates in the Fourier plane. Furthermore, the present invention only considers real transforms, ie, the filters are symmetric about the origin.

[0029] Step 33: Establish an empirical curvelet tight support framework, using the first generation empirical curvelet framework. The Fourier boundary detection method is: scale and angle are detected independently, first detect the scale , and then detect different angle sets for each scale The detection method is the local extreme value method. The first generation of empirical curve wave tight support framework is shown as follows:

[0030]

[0031] Input image The detail coefficients of the 2D-ECT are first calculated using a radial window , rear pole window Definition, the definition of detail coefficient at this time:

[0032]

[0033]

[0034] The approximate coefficients are expressed as:

[0035]

[0036] In the formula is the Fourier transform, is the inverse Fourier transform.

[0037] Furthermore, the step 4 may further include:

[0038] Step 41: Combine the detail coefficients decomposed in step 33 with the atomic region Do correlation detection and retain the detail coefficient with the greatest correlation with the sub-region, expressed as .

[0039] Step 42: Right Perform empirical ridgelet transform to estimate the wave direction. First, Perform Radon transform to obtain the projection of the image at different angles. These projections are one-dimensional functions that represent the integral of the image in all directions. The Radon transform is shown below:

[0040]

[0041] in, It is the perpendicular distance from the origin, that is, the shortest distance from the projected line to the origin. The projection angle is the angle between the positive direction of the X axis and the range is 0-180°. The integral is along the line perpendicular to Direction of the straight line.

[0042] Step 43: Radon domain Perform one-dimensional empirical wavelet transform to obtain detail coefficients and approximate coefficients, detect Fourier boundaries by performing pseudo-polar Fourier transform on the image to obtain the average spectrum, and then establish a wavelet framework ,in, As described in step 32 As shown, As shown in the following formula:

[0043]

[0044] if ,

[0045]

[0046] The empirical ridgelet transform is defined as:

[0047]

[0048] in, is based on One-dimensional empirical wavelet transform about t. Because the empirical wavelet is defined in the Fourier domain, the above transformation is:

[0049]

[0050]

[0051] In the formula is the detail coefficient, is the approximate coefficient, is the one-dimensional Fourier transform, is the one-dimensional inverse Fourier transform, For time.

[0052] Step 44: Calculate the standard deviation of the pixel intensity in each projection direction for the detail coefficient after the empirical ridgelet transform. The direction with the largest standard deviation is the texture dominant direction. For all sub-region images of each radar image, take the average standard deviation of all sub-images after the ERT method for each projection direction, and the direction with the largest average standard deviation is used as the rough estimate direction. ,choose exist The sub-image within the range is selected, and the median value of the selected sub-image is used as the final texture dominant direction of a radar image. ;

[0053] Step 4.5: Continuously calculate the final texture dominant direction of multiple sub-regions The cross-correlation coefficient is used to eliminate the 180° ambiguity of the wave direction. Assume that two consecutive radar sub-area images are and , calculate the cross-correlation function :

[0054]

[0055] in and They are and The average intensity value of Representing an image and In the offset The relevance of the.

[0056] Furthermore, the step 5 may include:

[0057] Step 51: Finalize the dominant texture direction The center point of the sub-image is the center, and the image in this area is expanded to Pixel size, the pixel data of each column of the expanded sub-image pixel matrix is ​​expressed as: , solve the periodogram power spectrum density for each column of pixel data in the sub-image, expressed as:

[0058]

[0059] in, is the sampling frequency, which can be set according to the specific Represents the nth pixel value in the jth column of the expanded sub-image.

[0060] Step 52: Find the frequency corresponding to the maximum value of each column power spectrum density ( ), then from The maximum frequency is selected as the frequency of the radar sub-image ( ), the peak period of this image is:

[0061]

[0062] Where M is the number of radar sub-images after expansion.

[0063] The beneficial effects of the present invention include: using a two-dimensional empirical curvelet transform to extract features from X-band radar images, eliminating co-frequency interference and target interference while extracting texture features from radar images; using an empirical ridgelet transform to estimate wave crest directions from the feature-extracted image, and using cross-correlation coefficients to eliminate 180° ambiguity in the wave directions. Image expansion is performed on the subregion of the final texture dominant direction, and the peak period is estimated using the periodogram power spectrum method. The two-dimensional empirical wavelet transform has advantages over the traditional three-dimensional Fourier transform in processing non-stationary signals, capturing local transformations in wave texture images, performing multi-scale analysis, and being robust in noisy environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 4 is a flowchart of a method for estimating a peak direction and a peak period according to an embodiment of the present invention.

[0065] Figure 2 Shown is the selection of sub-regions of X-band radar images in polar coordinates.

[0066] Figures 3a to 3f Shown are six selected sub-region images.

[0067] Figures 4a to 4j Shown are the curvelet coefficients after decomposition by the 2D-ECT method, the Fourier partitioned image, and the original sub-image.

[0068] Figures 5a to 5f Shown are the curvelet coefficients with the greatest image correlation in each sub-region.

[0069] Figure 6a Shown are the results after empirical ridgelet transform.

[0070] Figure 6b Shown is the standard deviation of pixel intensity in each direction.

[0071] Figure 7 Shown is a 180° blurring of the wave direction.

[0072] Figure 8a Shown are sub-region images used to estimate the peak period.

[0073] Figure 8b Shown is the sub-region image expansion used to estimate the peak period.

[0074] Figure 9a It is the power spectrum density of the periodogram of one column of pixel data in the sub-image.

[0075] Figure 9b It is the frequency corresponding to the maximum power spectral density of all azimuth pixels in a sub-image. DETAILED DESCRIPTION

[0076] The embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0077] Figure 1 The present invention is a flow chart of a method for estimating wave direction and wave period from an X-band radar image.

[0078] The present invention proposes a method for estimating wave direction and wave period from an X-band radar image, comprising the following steps:

[0079] Step 1: Collect the original radar wavefront image to obtain the X-band radar wavefront image.

[0080] Step 2: Select (divide) an ocean wave parameter inversion region for the X-band radar wave surface image.

[0081] Step 3: Use two-dimensional empirical curvelet transform to extract radar sub-image texture features in the wave parameter inversion area.

[0082] Step 4: Perform empirical ridgelet transform on the wave parameter inversion area to estimate the wave crest direction.

[0083] Step 5: Estimate the peak period.

[0084] Specifically, step 1 radar original wavefront image acquisition can include using the navigation radar to collect a set of original radar wavefront image sequences, namely X-band radar wavefront images, and storing them according to the radar protocol to obtain the radar wavefront image in polar coordinates. ,in, is the distance from the sea surface point to the radar, is the azimuth;

[0085] Step 2: Selecting the wave parameter inversion area can be included in the effective wave texture image screened from the X-band radar wave surface image, and selecting a sub-area with a distance range of d1*d2 , t=1,2,…,S is used as the wave parameter inversion area. Convert to Cartesian coordinates and get S wave inversion areas in Cartesian coordinates ;

[0086] Step 3: Radar sub-image texture feature extraction can include the selection of S wave inversion areas Perform a 2D Empirical Curvelet Transform (2D-ECT). Each sub-inversion region divided in step 2 is decomposed into multiple curvelet component images (curvelet coefficients). Image processing includes co-frequency interference, target interference, and wave texture feature extraction to obtain the processed radar wavefront image.

[0087] Step 4 of estimating the peak direction may include performing a correlation test on the decomposed curvelet component image in each sub-region in step 3 and the original sub-region, retaining the curvelet component image with the greatest correlation with each sub-region, applying the empirical ridgelet transform (ERT) to the largest curvelet component image in each sub-region, and then solving the standard deviation of the pixel intensity in each projection direction in each sub-region image, and taking the direction with the largest standard deviation as the dominant texture direction. , for all sub-region images of each radar image, take the average standard deviation of all sub-images after the ERT method for each projection direction, and the direction with the largest average standard deviation is taken as the rough estimate direction ( ),choose exist The sub-images within the predetermined deviation range are selected, and the median value of the selected sub-images is used as the final texture dominant direction of a radar image ( );

[0088] Preferably, select exist The sub-image within the range.

[0089] Step 5 of estimating the peak period may include expanding the sub-image to be centered at the center point of the sub-region image of the final texture dominant direction. Pixel size, solve the periodogram power spectrum density for each column of pixel data in the expanded sub-image, obtain the frequency corresponding to the maximum value of the power spectrum density of each column, and then calculate the maximum value of each column frequency as the final frequency of the radar image, and then estimate the peak period;

[0090] Furthermore, the step 3 may include:

[0091] Step 31: Sub-region by 2D-ECT method Perform feature extraction, Perform pseudo-polar coordinate transformation on the pseudo grid. When the frequency adopted on the pseudo grid is , assuming that the image input pixels are N*N, the pseudo-polar Fourier transform is:

[0092]

[0093] Where x, y are the coordinate values ​​of the sub-region, N is the image pixel size of the sub-region, is the pseudo-polar coordinate transformation;

[0094] Step 32: Perform Fourier edge detection on the pseudo-polar Fourier transform, assuming the number of scales ( ) and the number of angular sectors ( ) is known, and the scale boundary is , the angle boundary is , generate an adaptive filter to filter each sub-band coefficient of the empirical curvelet decomposition to facilitate the necessary processing of each high and low frequency coefficient. Expressed as:

[0095]

[0096] In the formula is an arbitrary function in [0,1] that satisfies the following relationship:

[0097]

[0098] In the formula To ensure that two consecutive transition intervals do not overlap, the following conditions must be met:

[0099]

[0100] Among them, min() is the minimum function.

[0101] A polar wedge in the Fourier domain (n and m are the scale and angle index respectively) is a radial window and a polar window The product of hour, Expressed as:

[0102]

[0103] if hour:

[0104]

[0105] Polar Window Expressed as:

[0106]

[0107] in, , are polar coordinates in the Fourier plane. In addition, the present invention only considers real number transforms, i.e., these filters are symmetric about the origin; and

[0108] Step 33: Establish an empirical curvelet tight support framework, using the first generation empirical curvelet framework. The Fourier boundary detection method is: scale and angle are detected independently, first detect the scale , and then detect different angle sets for each scale The detection method is the local extreme value method. The first generation of empirical curve wave tight support framework is shown as follows:

[0109]

[0110] Input image The detail coefficients of the 2D-ECT are first calculated using a radial window , rear pole window Definition, the definition of detail coefficient at this time:

[0111]

[0112]

[0113] The approximate coefficients are expressed as:

[0114]

[0115] In the formula is the Fourier transform, is the inverse Fourier transform.

[0116] Furthermore, the step 4 may further include:

[0117] Step 41: Combine the detail coefficients decomposed in step 33 with the atomic region Do correlation detection and retain the detail coefficient with the greatest correlation with the sub-region, expressed as ;

[0118] Step 42: Right Perform empirical ridgelet transform to estimate the wave direction. First, Perform Radon transform to obtain the projection of the image at different angles. These projections are one-dimensional functions that represent the integral of the image in all directions. The Radon transform is shown below:

[0119]

[0120] in, It is the perpendicular distance from the origin, that is, the shortest distance from the projected line to the origin. The projection angle is the angle between the positive direction of the X axis and the range is 0-180°. The integral is along the line perpendicular to Direction of the straight line;

[0121] Step 43: Radon domain Perform one-dimensional empirical wavelet transform to obtain detail coefficients and approximate coefficients, detect Fourier boundaries by performing pseudo-polar Fourier transform on the image to obtain the average spectrum, and then establish a wavelet framework ,in, As described in step 32 As shown, As shown in the following formula:

[0122]

[0123] if ,

[0124]

[0125] The empirical ridgelet transform is defined as:

[0126]

[0127] in, is based on One-dimensional empirical wavelet transform about t. Because the empirical wavelet is defined in the Fourier domain, the above transformation is:

[0128]

[0129]

[0130] In the formula is the detail coefficient, is the approximate coefficient, is the one-dimensional Fourier transform, is the one-dimensional inverse Fourier transform, For time;

[0131] Step 44: Calculate the standard deviation of the pixel intensity in each projection direction for the detail coefficient after the empirical ridgelet transform. The direction with the largest standard deviation is the texture dominant direction. For all sub-region images of each radar image, take the average standard deviation of all sub-images after the ERT method for each projection direction, and the direction with the largest average standard deviation is used as the rough estimate direction. ,choose exist The sub-image within the range is selected, and the median value of the selected sub-image is used as the final texture dominant direction of a radar image. ;as well as

[0132] Step 45: Continuously calculate the final texture dominant direction of multiple sub-regions The cross-correlation coefficient is used to eliminate the 180° ambiguity of the wave direction. Assume that two consecutive radar sub-area images are and , calculate the cross-correlation function :

[0133]

[0134] in and They are and The average intensity value of Representing an image and In the offset The relevance of the.

[0135] Furthermore, step 5 may include:

[0136] Step 51: Finalize the dominant texture direction The center point of the sub-image is the center, and the image in this area is expanded to Pixel size, the pixel data of each column of the expanded sub-image pixel matrix is ​​expressed as: , solve the periodogram power spectrum density for each column of pixel data in the sub-image, expressed as:

[0137]

[0138] in, is the sampling frequency, which can be set according to the specific Represents the nth pixel value in the jth column of the expanded sub-image.

[0139] Step 52: Find the frequency corresponding to the maximum value of each column power spectrum density ( ), then from The maximum frequency is selected as the frequency of the radar sub-image ( ), the peak period of this image is:

[0140]

[0141] Where M is the number of radar sub-images after expansion.

[0142] like Figures 2 to 9b A specific experimental example shown may include the following steps:

[0143] Step 1: Radar original wavefront image acquisition. Use the navigation radar to collect a set of original radar wavefront image sequences as X-band radar images, and store them according to the radar protocol to obtain the radar wavefront image in polar coordinates. ,in, is the distance from the sea surface point to the radar, is the azimuth;

[0144] Step 2: Figure 2 The wave parameter inversion area is selected. In the effective wave texture image screened out by the X-band radar image, a sub-area with a distance range of 1125m*1125m is selected. , t=1,2,…,S, in this case S=6, as the wave parameter inversion area. Converted to Cartesian coordinates, we get 6 wave inversion areas in Cartesian coordinates , each sub-region has a pixel size of 150 150, such as Figure 3a 、 Figure 3b 、 Figure 3c 、 Figure 3d 、 Figure 3e 、 Figure 3f As shown;

[0145] Step 3: Figure 3a 、 Figure 3b 、 Figure 3c 、 Figure 3d 、 Figure 3e 、 Figure 3f Shown - The feature extraction is performed on the 6 radar sub-area images. The filter used in this experiment is the first generation empirical curve support framework. , first scale Directional decomposition, the number of decompositions is set to 2, and the decomposition method is the local maximum and minimum values ​​on the spectrum; then the angle Directional decomposition, the number of decompositions is 8, and the decomposition method is also the local maximum and minimum values ​​on the spectrum. Figures 4a to 4h Among them Figure 3a 2D-EWTC decomposition of neutron image I1, Figures 4a to 4h The sub-image is decomposed into 8 curvelet coefficient components, and Figure 4i is the low-pass filtering of the sub-region image, Figure 4i The white dividing line in the middle is the dividing line in the angular direction. Figure 4j is the radar sub-image I1. Figure 4a As can be seen from the sub-image, Figure 3a The red circle in the figure is the interference noise of the radar sub-image. After 2D-ECT decomposition, the noise interference in the radar image can be separated from the wave texture features. Figure 4c 、 Figure 4e is the noise interference part in the sub-image, Figure 4a It is the wave texture part in the decomposed radar image;

[0146] Step 4: Estimate the peak direction. Perform correlation test on the decomposed curvelet component image in each sub-region in step 3 and the original sub-region, and retain the curvelet component image with the greatest correlation with each sub-region. After 2D-ECT of the sub-region in Figure 3, the curvelet coefficient with the greatest correlation with the atomic region is as follows: Figures 5a to 5f The ERT method is used for Figures 5a to 5f The curvelet coefficient with the greatest correlation with the atomic region is used to estimate the peak direction of the wave using the transformed third sub-band image. The sub-band image after the ERT method is as follows: Figure 6a As shown, the horizontal axis in the figure is the projection direction , the vertical axis is the distance from the edge image to the origin The image pixel in the corresponding texture line projection direction will show the largest intensity value change. The standard deviation of the pixel intensity in each projection direction of the transformation result is calculated. The direction with the largest standard deviation is the texture dominant direction ( ). The standard deviation of pixel intensity in each direction of the sub-band image after ERT transformation is as follows Figure 6b Furthermore, for each radar image sub-image, the average standard deviation of all sub-images after ERT is taken for each projection direction, and the direction with the largest average standard deviation is selected as the rough estimation direction. Next, compare the texture dominant direction and roughly estimate the direction ,choose exist The sub-image within the range is selected, and the median value of the selected sub-image is used as the final texture dominant direction of a radar image. , Figure 6b The estimated peak direction is 114°.

[0147] At this time, the final texture dominant direction calculated has a 180° fuzzy problem. The present invention calculates the final texture dominant direction of multiple consecutive images. The cross-correlation function between the sub-images is used to eliminate the 180° ambiguity of the wave direction. The normalized cross-correlation coefficient matrix of two consecutive radar sub-images is as follows: Figure 7 As shown in the figure, the waves move in a specific direction in the image, and this movement appears as a significant peak in the cross-correlation function. The red arrow in the figure indicates the direction of wave propagation, eliminating the problem of 180° ambiguity in solving the wave direction from the radar image.

[0148] Step 5: Estimate the peak period. Take the center point of the sub-region image of the final texture dominant direction as the center, and expand the sub-image to 256 256 pixels in size, such as Figure 8a 、 Figure 8b shown. Figure 9a 、 Figure 9b The process of peak period estimation is shown, where Figure 9(a) is the power spectrum density of the periodogram of one column of pixel data in the sub-image. Figure 9b is the frequency corresponding to the maximum power spectrum density of all azimuth pixels in a sub-image, Figure 9a 、 Figure 9b For example, the maximum frequency of the sub-image is 0.125 Hz, and the peak period is 8 seconds.

[0149] The above-described embodiments merely express the implementation methods of the present invention, but should not be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A method for estimating wave direction and wave period from X-band radar images, characterized in that: Including steps: Step 1: Use the navigation radar to collect a set of X-band radar wave surface image sequences and store them according to the radar protocol to obtain the X-band radar wave surface images in polar coordinates. ,in, is the distance from the sea surface point to the radar, is the azimuth; Step 2: Filter out the effective wave texture image of the X-band radar wave surface image, and select the sub-region image with a distance range of d1*d2 from the effective wave texture image , t=1, 2, …, S as the wave parameter inversion area image; the S wave parameter inversion area images in polar coordinates Convert to Cartesian coordinates and obtain S wave parameter inversion area images in Cartesian coordinates ; Step 3: Invert the regional image for the S wave parameters Performing a two-dimensional empirical curvelet transform, wherein each of the wave parameter inversion area images is decomposed into a plurality of curvelet component images, and image processing is performed on the curvelet component images to obtain processed radar wavefront images, wherein the image processing includes wave texture feature extraction; Step 4: Perform a correlation test on each of the curvelet component images and its corresponding ocean wave parameter inversion area image, retain the curvelet component image with the greatest correlation with each of the ocean wave parameter inversion area images, apply the empirical ridgelet transform (ERT) to each of the largest curvelet component images, and then calculate the standard deviation of the pixel intensity in each projection direction in each of the ocean wave parameter inversion area images. The direction with the largest standard deviation is taken as the texture dominant direction. For all the wave parameter inversion area images of each radar image, take the average value of the standard deviation of each wave parameter inversion area image after ERT for each projection direction, and take the direction with the largest average value of the standard deviation as the rough estimation direction ,choose exist The wave parameter inversion area image within the predetermined deviation range is obtained, and the relevant median of the selected wave parameter inversion area image is used as the final texture dominant direction of the effective wave texture image. ; Step 5: Estimate the peak period, including the dominant direction of the final texture The center point of the wave parameter inversion area image is taken as the center, and the wave parameter inversion area image is expanded to The pixel size is calculated by solving the power spectral density of each column of pixel data in the expanded wave parameter inversion area image, and the frequency corresponding to the maximum value of the power spectral density of each column of pixel data is obtained. The maximum value of each column frequency is then calculated as the final frequency of the effective wave texture image, and the peak period is estimated based on the final frequency.

2. The method according to claim 1, wherein: The predetermined deviation is .

3. The method according to claim 1, wherein: The image processing also includes co-frequency interference extraction and target object interference extraction.

4. The method according to claim 1, wherein: The step 3 comprises: Step 31: Sub-region by 2D-ECT method Perform feature extraction, Perform pseudo-polar coordinate transformation on the pseudo grid. When the frequency adopted on the pseudo grid is , assuming that the image input pixels are N*N, the pseudo-polar Fourier transform is: ； Where x, y are the coordinate values ​​of the sub-region, N is the image pixel size of the sub-region, is the pseudo-polar coordinate transformation; Step 32: Perform Fourier edge detection on the pseudo-polar Fourier transform, assuming the number of scales ( ) and the number of angular sectors ( ) is known, and the scale boundary is , the angle boundary is , generate an adaptive filter to filter each sub-band coefficient of the empirical curvelet decomposition to facilitate the necessary processing of each high and low frequency coefficient; The adaptive filter is a low-pass filter , expressed as: ； In the formula is an arbitrary function in [0,1] that satisfies the following relationship: ； In the formula To ensure that two consecutive transition intervals do not overlap, the following conditions must be met: ； Among them, min() is the minimum function; A polar wedge in the Fourier domain , where n and m are scale and angle indices respectively, and is a radial window and a polar window The product of hour, Expressed as: ； if hour: ； Polar Window Expressed as: ； in, , are polar coordinates in the Fourier plane, wherein the adaptive filter is symmetric about the origin; and Step 33: Establish an empirical curvelet tight support framework, using the first generation empirical curvelet framework. The Fourier boundary detection method is: scale and angle are detected independently, first detect the scale , and then detect different angle sets for each scale The detection method is the local extreme value method; The first generation of empirical curve wave tight support framework is shown as follows: ； Input image The detail coefficients of the 2D-ECT are first calculated using a radial window , rear pole window Definition, the definition of detail coefficient at this time: ； ； The approximate coefficients are expressed as: ； In the formula is the Fourier transform, is the inverse Fourier transform.

5. The method according to claim 4, characterized in that: The step 4 further comprises: Step 41: The detail coefficients decomposed in step 33 are combined with the atomic regions Do correlation detection and retain the detail coefficient with the greatest correlation with the sub-region, expressed as ; Step 42: Right Perform empirical ridgelet transform to estimate the wave direction. First, Perform Radon transform to obtain the projections of the image at different angles. These projections are one-dimensional functions that represent the integral of the image in all directions. The Radon transform is shown below: ； in, is the vertical distance from the origin, that is, the shortest distance from the projected line to the origin, The projection angle is the angle between the positive direction of the X axis and the range is 0-180°. The integral is along the line perpendicular to Direction of the straight line; Step 43: Radon domain Perform one-dimensional empirical wavelet transform to obtain detail coefficients and approximate coefficients, detect Fourier boundaries by performing pseudo-polar Fourier transform on the image to obtain the average spectrum, and then establish a wavelet framework ,in, As described in step 32 As shown, As shown in the following formula: ； if , ； The empirical ridgelet transform is defined as: ； in, is based on One-dimensional empirical wavelet transform about t; because the empirical wavelet is defined in the Fourier domain, the above transformation is: ； ； In the formula is the detail coefficient, is the approximate coefficient, is the one-dimensional Fourier transform, is the one-dimensional inverse Fourier transform, For time; Step 44: Calculate the standard deviation of the pixel intensity in each projection direction for the detail coefficient after the empirical ridgelet transform. The direction with the largest standard deviation is the texture dominant direction. ; For all sub-region images of each radar image, take the average standard deviation of all sub-images after the ERT method for each projection direction, and the direction with the largest average standard deviation is taken as the rough estimate direction ,choose exist Preset the sub-image within the deviation range and use the median value of the selected sub-image as the final texture dominant direction of a radar image ;as well as Step 45: Continuously calculate the final texture dominant direction of multiple sub-regions The cross-correlation coefficient is used to eliminate the 180° ambiguity in the wave direction.

6. The method according to claim 5, characterized in that: The step 5 comprises: Step 51: Finalize the dominant texture direction The center point of the sub-image is the center, and the image in this area is expanded to Pixel size, the pixel data of each column of the expanded sub-image pixel matrix is ​​expressed as: , solve the periodogram power spectrum density for each column of pixel data in the sub-image, expressed as: ； in, is the sampling frequency, which can be set according to the specific Represents the nth pixel value of the jth column of the expanded sub-image; Step 52: Find the frequency corresponding to the maximum value of each column power spectrum density ( ), then from The maximum frequency is selected as the frequency of the radar sub-image ( ), the peak period of this image is: , where M is the number of radar sub-images after expansion.

Images