A sea surface target detection method based on wake wave characteristics and computer readable medium

Abstract

The present invention discloses a method for detecting sea surface targets based on wake wave characteristics and a computer-readable medium. The present invention uses image differential processing to obtain differential detection images from multiple consecutive frames of images; performs target identification on each differential detection image frame and obtains a target determination result; repeatedly determines whether a target exists on all differential detection images, and if a target exists, performs target background cancellation to obtain a target background cancellation image; performs primary target identification on the target background cancellation image, and based on this, performs clustering, optimization, and pairing to obtain a secondary target identification image; estimates the target's heading based on the secondary target identification image; and obtains target heading information based on multiple frames of the secondary target identification image. The present invention can achieve effective detection of sea surface targets with a low false alarm rate, and is based on image domain processing, with the advantages of small computational data volume and high operational efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of passive microwave remote sensing and sea surface target detection, and in particular relates to a sea surface target detection method based on wake wave characteristics and a computer-readable medium. Background Art

[0002] Synthetic Aperture Microwave Passive Detection Technology primarily utilizes the target's own microwave thermal radiation signals to detect it. The device used is a Synthetic Aperture Microwave Passive Imaging System (or Synthetic Aperture Microwave Radiometer), which falls under the category of passive radar. Compared to traditional radar, this technology does not emit any signals. It offers advantages such as low power consumption, high concealment, and minimal interference from sea clutter. It also has excellent detection capabilities for stealthy targets. Researchers at home and abroad have been conducting research on its applications in military reconnaissance.

[0003] However, microwave passive detection technology has long been limited by the low spatial resolution of microwave imaging systems, preventing long-range target detection. In the 1980s, based on the concept of "aperture synthesis" in radio astronomy, the theory of synthetic aperture microwave radiometry was proposed to improve its spatial resolution. Subsequently, the military application of synthetic aperture microwave passive imaging technology has gradually attracted the attention of scholars both domestically and internationally. In 2002, the German Aerospace Center demonstrated research on an airborne synthetic aperture microwave passive imaging system for reconnaissance of ground military targets. In 2004, the Tenth Research Institute of the China Electronics Technology Group Corporation demonstrated that synthetic aperture microwave passive imaging could detect a 3m x 3m metal target on the ground from a distance of 1.5km. In 2005, Huazhong University of Science and Technology successfully developed China's first one-dimensional synthetic aperture microwave passive imaging system and used this system to conduct a series of research on microwave passive detection. Although scholars both domestically and internationally have conducted research on the application of synthetic aperture microwave passive imaging technology in military reconnaissance, most of these research is based on ground-based or airborne platforms. In 2016, the idea of ​​using airborne or satellite-borne synthetic aperture microwave radiation passive detection technology to achieve all-day, all-weather, and covert detection of surface ship targets was proposed, and a lot of research work was carried out.

[0004] However, due to the long detection range and the limited spatial resolution of the synthetic aperture microwave radiometry passive imaging system, sea surface targets occupy only a few pixels in the synthetic aperture microwave radiometry passive detection image. Furthermore, the target brightness temperature image has a low signal-to-noise ratio, appearing weak. Furthermore, it is also affected by factors such as system background noise, sea clutter, and platform jitter. Therefore, the sea surface target detection algorithm is a key issue in space-based and airborne synthetic aperture microwave radiometry passive detection technology. Summary of the Invention

[0005] The technical problem solved by the present invention is to overcome the shortcomings of existing technologies and methods, and provide a sea surface target detection method based on tail wave characteristics and a computer-readable medium, which can effectively reduce the false alarm rate of the target and quickly obtain the target's heading information while achieving effective detection of sea surface targets. It is based on image domain processing and has the advantages of small amount of calculation data and high detection efficiency.

[0006] The technical solution of the method of the present invention is a method for detecting sea surface targets based on wake wave characteristics, and the specific steps are as follows:

[0007] Step 1: Input multiple consecutive frames of passive interferometric microwave images, perform image difference processing on two adjacent frames of passive interferometric microwave images in sequence, and obtain a passive interferometric microwave difference image of each frame;

[0008] Step 2: Perform image identification processing on each frame of passive interferometric microwave difference image in sequence to obtain target determination results in two adjacent frames of passive interferometric microwave images;

[0009] Step 3: Repeat step 2. If the target determination result is that the target exists in the passive interferometric microwave image of the two frames apart, jump to step 4. If the target determination result in the last frame of the passive interferometric microwave difference image is that the target does not exist, jump to step 1.

[0010] Step 4: Based on the target determination result of step 3, multiple frames of passive interferometric microwave images containing sea surface targets are screened out from the continuous multiple frames of passive interferometric microwave images, and passive interferometric microwave images with a pure sea surface background are selected from the remaining multiple frames of passive interferometric microwave images that do not contain sea surface targets. Each frame of the passive interferometric microwave image containing the sea surface target and the passive interferometric microwave image with a pure sea surface background are sequentially subjected to image difference processing to obtain a background cancellation detection image containing the sea surface target in each frame;

[0011] Step 5: According to the brightness temperature of each pixel point in each frame of the background cancellation detection image containing the sea surface target, the target brightness temperature feature and the target trail brightness temperature feature of each frame of the background cancellation detection image are obtained. The target brightness temperature feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple target pixel points of each frame of the background cancellation detection image. The target trail brightness temperature feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple target trail pixel points of each frame of the background cancellation detection image. The multiple target pixel points of each frame of the background cancellation detection image are subjected to the neighboring clustering method to obtain multiple clustered target pixel points of each frame of the background cancellation detection image. The multiple target trail pixel points of each frame of the background cancellation detection image are subjected to the neighboring clustering method to obtain multiple clustered target pixel points of each frame of the background cancellation detection image. Marking tail pixel points, combining multiple clustered target pixel points and multiple clustered target tail pixel points of each frame of background cancellation detection image to calculate target distance features and target tail distance features of each frame of background cancellation detection image, combining the target distance features of each frame of background cancellation detection image with a target identification algorithm to obtain multiple optimized target pixel points of each frame of background cancellation detection image, combining the target tail distance features of each frame of background cancellation detection image with a target identification algorithm to obtain multiple optimized target tail pixel points of each frame of background cancellation detection image, pairing the multiple optimized target pixel points and multiple optimized target tail pixel points of each frame of background cancellation detection image to obtain multiple target pixel points of each type of target and multiple target tail pixel points of each type of target in each frame of background cancellation detection image;

[0012] Step 6: Calculate the pixel coordinates of the center position of the target pixel point of each type of target in each frame of the background cancellation detection image based on multiple target pixel points of each type of target in each frame of the background cancellation detection image; calculate the pixel coordinates of the center position of the target trail pixel point of each type of target in each frame of the background cancellation detection image based on multiple target trail pixel points of each type of target in each frame of the background cancellation detection image; and estimate the heading of each type of target in each frame of the background cancellation detection image;

[0013] Step 7: Construct a track map of each type of target based on the heading of each type of target in multiple frames of background cancellation detection images;

[0014] Preferably, in step 1, the passive interferometric microwave images separated by two frames are sequentially subjected to image difference processing to obtain each frame of the passive interferometric microwave difference image, specifically as follows:

[0015] ΔT D,n(R) (ξ,η)=TB map,n (ξ,η)-TB map,(n-R) (ξ,η)

[0016] ξ∈[-1,1],η∈[-1,1]

[0017] n∈[1,NUM]

[0018] Where ΔT D,n(R) represents the nth frame of passive interferometric microwave differential image when the interval is R, R represents the interval number of frames between two frames of passive interferometric microwave images, ΔT D,n(R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the nth frame of the passive interference microwave differential image when the interval is R, TB map,n represents the nth frame of passive interference microwave image, TB map,n (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interference microwave image of the nth frame, TB map,(n-R) represents the passive interference microwave image of the nRth frame, TB map,(n-R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image of the nRth frame, and NUM represents the number of passive interferometric microwave difference images;

[0019] Preferably, in step 2, each frame of the passive interferometric microwave difference image is sequentially subjected to image identification processing to obtain target determination results in two adjacent frames of the passive interferometric microwave image. The specific operation of the image identification processing is as follows:

[0020]

[0021] Where ΔT D,n(R) represents the nth frame of passive interferometric microwave differential image when the interval is R, R represents the interval number of frames between two frames of passive interferometric microwave images, ΔT D,n(R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the n-th frame of passive interferometric microwave difference image when the interval is R. F(n) represents the initial target identification of the n-th frame of passive interferometric microwave image. If F(n) = 1, it means that the n-th frame of passive interferometric microwave image contains a sea surface target. All passive interferometric microwave images containing sea surface targets are represented by TB. map,Ti If F(n) = 0, it means that there is no sea surface target in the passive interference microwave image of the nth frame, that is, the interfered microwave image of the pure sea surface background, N represents the target judgment scale factor, and σ0 represents the standard deviation of the passive interference microwave image;

[0022] Preferably, in step 4, each frame of the passive interferometric microwave image containing the sea surface target and the passive interferometric microwave image of the pure sea surface background are sequentially subjected to image difference processing to obtain each frame of the background cancellation detection image, specifically as follows:

[0023] ΔT B,m (ξ,η)=TB map,Tm (ξ,η)-TB map,Sj (ξ,η)

[0024] ξ∈[-1,1],η∈[-1,1]

[0025] m∈[1,NUM1],j∈[1,NUM2]

[0026] Where ΔT B,m represents the background cancellation detection image containing sea surface targets in the mth frame, ΔT B,m (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the background cancellation detection image containing the sea surface target in the mth frame, The mth frame contains the passive interference microwave image of the sea surface target, that is, the Tth frame of the sea surface target. m Frame passive interferometric microwave image, It represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interference microwave image containing the sea surface target in the mth frame, represents the passive interference microwave image of the pure sea surface background, that is, the Sth j Frame passive interferometric microwave image, Represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image with a pure sea surface background; NUM1 represents the number of passive interferometric microwave difference images containing sea surface targets, and NUM2 represents the number of passive interferometric microwave difference images with a pure sea surface background.

[0027] Preferably, in step 5, the target identification algorithm for obtaining multiple target trail pixel points of each background cancellation detection image is obtained by combining the target trail brightness temperature characteristics of each background cancellation detection image with a target identification algorithm, specifically as follows:

[0028]

[0029] Among them, F 1,m (ξ,η) represents the first target identification map of the passive interference microwave image containing the sea surface target in the mth frame. If F 1,m (ξ,η)=1 means that there is a sea surface target at the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis azimuth cosine coordinate η in the passive interference microwave image containing the sea surface target in the mth frame. If F 1,m (ξ,η)=-1 means that there is a tail wave of the sea target at the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis ξ azimuth cosine coordinate η in the passive interference microwave image containing the sea target in the mth frame. 1,m (ξ,η)=0 means that the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image containing a sea surface target in the mth frame has no target and is the ocean background; σ0 represents the standard deviation of the passive interferometric microwave image; M1 represents the first scale factor, and M2 represents the second scale factor;

[0030] In step 5, the multiple target pixels of each frame of the background cancellation detection image are clustered by the neighboring clustering method to obtain multiple clustered target pixels and target trail pixels of each frame of the background cancellation detection image. The specific results of the clustered target pixels and target trail pixels are as follows:

[0031] The target clustering is performed on the first target identification map, and the sea surface target label in the first target identification map after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel point in S Cosine coordinate of the longitudinal axis The tail wave label of the sea target is the horizontal axis azimuth cosine coordinate of the j-th pixel in W Cosine coordinate of the longitudinal axis That is and where 1≤i≤N S , N S Indicates the total number of pixels labeled as sea surface targets, 1≤j≤N W , N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface.

[0032] In step 5, the target trail distance feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple optimized target trail pixel points of each frame of the background cancellation detection image, as follows:

[0033]

[0034]

[0035] Among them, F 2,m It represents the second target identification map of the passive interference microwave image containing the sea surface target in the mth frame. If The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates If there is a sea surface target at the pixel point The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates The pixel point has no target and is the ocean background. The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates If there is a sea surface target trail at the pixel point The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates The pixel point has no target, it is the ocean background, sw It represents the distance between any two pixels in the wake of the sea surface target labeled S and the sea surface target labeled W after clustering. The specific calculation method is as follows:

[0036]

[0037] i=1,…,N S

[0038] j=1,…,N W

[0039] Among them, l min Indicates the distance threshold, Indicates that the sea surface target label in the first target identification image after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel in S, Indicates that the sea surface target label in the first target identification image after clustering is the vertical axis azimuth cosine coordinate of the i-th pixel in S, The tail wave label of the sea surface target is the horizontal axis azimuth cosine coordinate of the j-th pixel point in W, The tail wave label of the sea surface target is the vertical axis azimuth cosine coordinate of the j-th pixel in W, N S Indicates the total number of pixels with the label S as the sea surface target, N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface;

[0040] If the sea surface target labeled S and the sea surface target tail wave labeled W after clustering meet the above conditions, the coordinates of the sea surface target tail wave labeled S are redefined as follows

[0041]

[0042]

[0043] in, The tail wave label of the sea surface target is the horizontal axis azimuth cosine coordinate of the i-th pixel point in W, The tail wave label of the sea surface target is the vertical axis azimuth cosine coordinate of the i-th pixel in W, It represents the horizontal axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, Represents the longitudinal azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S;

[0044] Preferably, the heading of each type of target in each frame of the background cancellation detection image in step 6 is specifically defined as follows:

[0045]

[0046] in, It represents the mean of the horizontal axis azimuth cosine coordinates of all pixels with the sea surface target label S, It represents the mean of the vertical axis cosine coordinates of all pixels with the target label S on the sea surface. It represents the mean of the horizontal axis azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, The mean of the longitudinal azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S.

[0047] In the heading estimation of each type of target, the calculation formulas for the mean horizontal axis azimuth cosine coordinates and the mean vertical axis azimuth cosine coordinates of the sea surface target labeled S and the sea surface target tail wave are as follows:

[0048]

[0049]

[0050] in, It represents the mean of the horizontal axis azimuth cosine coordinates of all pixels with the sea surface target label S, It represents the mean of the vertical axis cosine coordinates of all pixels with the target label S on the sea surface. It represents the mean of the horizontal axis azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, The mean of the longitudinal azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, Indicates that the sea surface target label in the first target identification image after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel in S, Indicates that the sea surface target label in the first target identification image after clustering is the vertical axis azimuth cosine coordinate of the i-th pixel in S, It represents the horizontal axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, It represents the vertical axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, N S Indicates the total number of pixels with the label S as the sea surface target, N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface;

[0051] As an example, the track map of each type of target is constructed in step 7 as follows:

[0052]

[0053] Among them, T ship Represents the track diagram of each type of target, T ship(ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the track diagram of each type of target, F 2,m The second target identification image of the passive interference microwave image containing the sea surface target in the mth frame, F 2,m (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the second target identification diagram of the passive interference microwave image containing the sea surface target in the mth frame, t1…t s Indicates the frame range where the target is located.

[0054] The present invention also provides a computer-readable medium, which stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the steps of the sea surface target detection method based on tail wave characteristics are performed.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention provides a new and effective target detection algorithm for the sea surface target synthetic aperture microwave radiation passive detection system;

[0057] The present invention mainly processes images in the domain of passive detection of synthetic aperture microwave radiation, and has the advantages of simple algorithm, small amount of calculation and high operation efficiency.

[0058] The present invention can obtain a synthetic aperture microwave radiation passive detection cancellation detection image through image addition, which is equivalent to the relative brightness temperature image of the sea surface target. Therefore, it is possible to effectively detect the target without correcting the additive error of the system, thereby simplifying the system structure and omitting the additive error correction process.

[0059] The present invention combines the low brightness temperature characteristics of the sea surface target itself with the high brightness temperature characteristics of the sea surface target trail, which can eliminate a large number of incorrectly marked pixels and greatly reduce the false alarm rate of the target;

[0060] The present invention is based on the position information of the sea surface target itself and the sea surface target wake, and can obtain high-precision heading information of the sea surface target through a single-frame image. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 : A flow chart of a method according to an embodiment of the present invention;

[0062] Figure 2 : Schematic diagram of a sample of passive interferometric microwave images according to an embodiment of the present invention;

[0063] Figure 3 : Schematic diagram of a sample of passive interferometric microwave differential image according to an embodiment of the present invention;

[0064] Figure 4: Schematic diagram of an image sample for background cancellation detection according to an embodiment of the present invention;

[0065] Figure 5 : Schematic diagram of a target secondary identification diagram and a target heading assessment example according to an embodiment of the present invention;

[0066] Figure 6 : Schematic diagram of optical images at corresponding moments of an embodiment of the present invention;

[0067] Figure 7 : Target track diagram obtained by the embodiment of the present invention;

[0068] Figure 8 : Target heading statistics obtained by the embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0071] The following combination Figures 1-8 The technical solution of the method of the embodiment of the present invention is a method for detecting sea surface targets based on wake wave characteristics, which is specifically as follows:

[0072] like Figure 1 Shown is a flow chart of the method for implementing incentives according to the present invention.

[0073] Step 1: Input multiple frames of continuous passive interferometric microwave images, such as Figure 2 As shown in Figure 2, samples of different frames of passive interferometric microwave images are given, where: Figure 2 (a) is the passive interferometric microwave image of frame 6580; Figure 2 (b) is the passive interferometric microwave image of frame 6595; Figure 2 (c) is the passive interferometric microwave image of frame 6610.

[0074] The passive interference microwave images of two adjacent frames are processed by image difference in turn to obtain the passive interference microwave difference image of each frame, such as Figure 3 As shown in the figure, samples of different frames of passive interferometric difference images are given. Figure 3 (a) is the passive interference microwave difference image of the 6580th frame image. Figure 3 (b) is the passive interference microwave difference image of the 6595th frame.

[0075] Figure 3 (c) is the passive interferometric microwave difference image of the 6610th frame;

[0076] In step 1, the passive interferometric microwave images separated by two frames are sequentially subjected to image difference processing to obtain a passive interferometric microwave difference image of each frame, as follows:

[0077] ΔT D,n(R) (ξ,η)=TB map,n (ξ,η)-TB map,(n-R) (ξ,η)

[0078] ξ∈[-1,1],η∈[-1,1]

[0079] n∈[1,NUM]

[0080] Where ΔT D,n(R) represents the nth frame of passive interferometric microwave differential image when the interval is R, R represents the interval number of frames between two frames of passive interferometric microwave images, ΔT D,n(R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the nth frame of the passive interference microwave differential image when the interval is R, TB map,n represents the nth frame of passive interference microwave image, TB map,n (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interference microwave image of the nth frame, TB map,(n-R) represents the passive interference microwave image of the nRth frame, TB map,(n-R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image of the nRth frame, NUM represents the number of passive interferometric microwave difference images, NUM = 10000, R = 10;

[0081] Step 2: Perform image identification processing on each frame of passive interferometric microwave difference image in sequence to obtain target determination results in two adjacent frames of passive interferometric microwave images;

[0082] In step 2, each frame of the passive interferometric microwave difference image is sequentially subjected to image identification processing to obtain target determination results in two adjacent frames of the passive interferometric microwave image. The specific operation of the image identification processing is as follows:

[0083]

[0084] Where ΔT D,n(R)represents the nth frame of passive interferometric microwave differential image when the interval is R, R represents the interval number of frames between two frames of passive interferometric microwave images, ΔT D,n(R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the n-th frame of passive interferometric microwave difference image when the interval is R. F(n) represents the initial target identification of the n-th frame of passive interferometric microwave image. If F(n) = 1, it means that the n-th frame of passive interferometric microwave image contains a sea surface target. All passive interferometric microwave images containing sea surface targets are represented by TB. map,Ti Indicates; if F(n) = 0, it means there is no sea surface target in the passive interferometric microwave image of the nth frame, that is, the interferometric microwave image with a pure sea surface background, N represents the target judgment scale factor, σ0 represents the standard deviation of the passive interferometric microwave image; in the sample, σ0 = 0.65K, N = 1.0 / 0.65.

[0085] Step 3: Repeat step 2. If the target determination result is that the target exists in the passive interferometric microwave image of the two frames apart, jump to step 4. If the target determination result in the last frame of the passive interferometric microwave difference image is that the target does not exist, jump to step 1.

[0086] Step 4: Based on the target determination result of step 3, multiple frames of passive interferometric microwave images containing sea surface targets are screened out from the continuous multiple frames of passive interferometric microwave images, and passive interferometric microwave images with a pure sea surface background are selected from the remaining multiple frames of passive interferometric microwave images that do not contain sea surface targets. Each frame of passive interferometric microwave images containing sea surface targets and each frame of passive interferometric microwave images with a pure sea surface background are subjected to image difference processing in sequence to obtain a background cancellation detection image containing sea surface targets in each frame; Figure 4 As shown in the figure, examples of different frames of background cancellation detection images are given. Figure 4 (a) is the background cancellation detection image of the 6580th frame image. Figure 4 (b) is the background cancellation detection image of the 6595th frame image.

[0087] Figure 4 (c) is the background cancellation detection image of the 6610th frame, and the passive interference microwave image of the pure sea surface background is selected as the 6300th frame.

[0088] In step 4, each frame of the passive interferometric microwave image containing the sea surface target and the passive interferometric microwave image of the pure sea surface background are sequentially subjected to image difference processing to obtain each frame of the background cancellation detection image, as follows:

[0089] ΔT B,m (ξ,η)=TB map,Tm (ξ,η)-TB map,Sj (ξ,η)

[0090] ξ∈[-1,1],η∈[-1,1]

[0091] m∈[1,NUM1],j∈[1,NUM2]

[0092] Where ΔT B,m represents the background cancellation detection image containing sea surface targets in the mth frame, ΔT B,m (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the background cancellation detection image containing the sea surface target in the mth frame, The mth frame contains the passive interference microwave image of the sea surface target, that is, the Tth frame of the sea surface target. m Frame passive interferometric microwave image, It represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interference microwave image containing the sea surface target in the mth frame, represents the passive interference microwave image of the pure sea surface background, that is, the Sth j Frame passive interferometric microwave image, Represents the pixel values ​​of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image with a pure sea surface background; NUM1 represents the number of passive interferometric microwave difference images containing sea surface targets, and NUM2 represents the number of passive interferometric microwave difference images with a pure sea surface background, NUM1 = 150, NUM2 = 9850.

[0093] Step 5: According to the brightness temperature of each pixel point in each frame of the background cancellation detection image containing the sea surface target, the target brightness temperature feature and the target trail brightness temperature feature of each frame of the background cancellation detection image are obtained. The target brightness temperature feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple target pixel points of each frame of the background cancellation detection image. The target trail brightness temperature feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple target trail pixel points of each frame of the background cancellation detection image. The multiple target pixel points of each frame of the background cancellation detection image are subjected to the neighboring clustering method to obtain multiple clustered target pixel points of each frame of the background cancellation detection image. The multiple target trail pixel points of each frame of the background cancellation detection image are subjected to the neighboring clustering method to obtain multiple clustered target pixel points of each frame of the background cancellation detection image. Marking tail pixel points, combining multiple clustered target pixel points and multiple clustered target tail pixel points of each frame of background cancellation detection image to calculate target distance features and target tail distance features of each frame of background cancellation detection image, combining the target distance features of each frame of background cancellation detection image with a target identification algorithm to obtain multiple optimized target pixel points of each frame of background cancellation detection image, combining the target tail distance features of each frame of background cancellation detection image with a target identification algorithm to obtain multiple optimized target tail pixel points of each frame of background cancellation detection image, pairing the multiple optimized target pixel points and multiple optimized target tail pixel points of each frame of background cancellation detection image to obtain multiple target pixel points of each type of target and multiple target tail pixel points of each type of target in each frame of background cancellation detection image;

[0094] In step 5, the target trail brightness temperature characteristics of each frame of the background cancellation detection image are combined with the target identification algorithm to obtain a target identification algorithm for multiple target trail pixels of each frame of the background cancellation detection image. The specific algorithm is as follows:

[0095]

[0096] Among them, F 1,m (ξ,η) represents the first target identification map of the passive interference microwave image containing the sea surface target in the mth frame. If F 1,m (ξ,η)=1 means that there is a sea surface target at the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis azimuth cosine coordinate η in the passive interference microwave image containing the sea surface target in the mth frame. If F 1,m (ξ,η)=-1 means that there is a tail wave of the sea target at the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis ξ azimuth cosine coordinate η in the passive interference microwave image containing the sea target in the mth frame. 1,m(ξ,η)=0 means that in the mth frame of the passive interferometric microwave image containing a sea surface target, the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis azimuth cosine coordinate η has no target and is the ocean background; σ0 represents the standard deviation of the passive interferometric microwave image; M1 represents the first scale factor, and M2 represents the second scale factor; in the example, σ0=0.65, M1=M2=2.

[0097] The multiple target pixels of each frame of the background cancellation detection image are clustered by the neighboring clustering method to obtain multiple clustered target pixels of each frame of the background cancellation detection image in step 5, as follows:

[0098] The target clustering is performed on the first target identification map, and the sea surface target label in the first target identification map after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel point in S Cosine coordinate of the longitudinal axis The tail wave label of the sea target is the horizontal axis azimuth cosine coordinate of the j-th pixel in W Cosine coordinate of the longitudinal axis That is and where 1≤i≤N S , N S Indicates the total number of pixels labeled as sea surface targets, 1≤j≤N W , N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface;

[0099] In step 5, the target trail distance feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple optimized target trail pixel points of each frame of the background cancellation detection image, as follows:

[0100]

[0101]

[0102] Among them, F 2,m It represents the second target identification map of the passive interference microwave image containing the sea surface target in the mth frame. If The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates If there is a sea surface target at the pixel point The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates The pixel point has no target and is the ocean background. The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates If there is a sea surface target trail at the pixel point The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates The pixel point has no target, it is the ocean background, sw It represents the distance between any two pixels in the wake of the sea surface target labeled S and the sea surface target labeled W after clustering. The specific calculation method is as follows:

[0103]

[0104] i=1,…,N S

[0105] j=1,…,N W

[0106] Among them, l min Indicates the distance threshold, Indicates that the sea surface target label in the first target identification image after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel in S, Indicates that the sea surface target label in the first target identification image after clustering is the vertical axis azimuth cosine coordinate of the i-th pixel in S, The tail wave label of the sea surface target is the horizontal axis azimuth cosine coordinate of the j-th pixel point in W, The tail wave label of the sea surface target is the vertical axis azimuth cosine coordinate of the j-th pixel in W, N S Indicates the total number of pixels with the label S as the sea surface target, N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface. In this example, l min =0.15. Figure 5 As shown in the figure, a number of optimized target and target trail pixel identification image samples are obtained by combining the target trail distance feature of each frame of background cancellation detection image with the target identification algorithm. Figure 5 (a) is the target secondary identification map and target heading of the 6580th frame image. Figure 5 (b) is the target secondary identification map and target heading of the 6595th frame image. Figure 5 (c) The target secondary identification map and target heading of the 6610th frame image;

[0107] If the sea surface target labeled S and the sea surface target tail wave labeled W after clustering meet the above conditions, the coordinates of the sea surface target tail wave labeled S are redefined as follows

[0108]

[0109]

[0110] in, The tail wave label of the sea surface target is the horizontal axis azimuth cosine coordinate of the i-th pixel point in W, The tail wave label of the sea surface target is the vertical axis azimuth cosine coordinate of the i-th pixel in W, It represents the horizontal axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, Represents the longitudinal azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S;

[0111] Step 6: Calculate the pixel coordinates of the center position of the target pixel point of each type of target in each frame of the background cancellation detection image based on multiple target pixel points of each type of target in each frame of the background cancellation detection image; calculate the pixel coordinates of the center position of the target trail pixel point of each type of target in each frame of the background cancellation detection image based on multiple target trail pixel points of each type of target in each frame of the background cancellation detection image; and estimate the heading of each type of target in each frame of the background cancellation detection image;

[0112] The heading of each type of target in each frame of the background cancellation detection image in step 6 is specifically defined as follows:

[0113]

[0114] in, It represents the mean of the horizontal axis azimuth cosine coordinates of all pixels with the sea surface target label S, It represents the mean of the vertical axis cosine coordinates of all pixels with the target label S on the sea surface. It represents the mean of the horizontal axis azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, The mean of the longitudinal azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, Figure 6 The optical images at the corresponding moments are given;

[0115] Figure 6 (a) is the optical image corresponding to the 6580th frame image. Figure 6 (b) is the optical image corresponding to the 6595th frame image. Figure 6 (c) is the optical image corresponding to the 6610th frame, which is used to verify the detected target and target heading.

[0116] In the heading estimation of each type of target, the calculation formulas for the mean horizontal axis azimuth cosine coordinates and the mean vertical axis azimuth cosine coordinates of the sea surface target labeled S and the sea surface target tail wave are as follows:

[0117]

[0118]

[0119] in, It represents the mean of the horizontal axis azimuth cosine coordinates of all pixels with the sea surface target label S, It represents the mean of the vertical axis cosine coordinates of all pixels with the target label S on the sea surface. It represents the mean of the horizontal axis azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, The mean of the longitudinal azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, Indicates that the sea surface target label in the first target identification image after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel in S, Indicates that the sea surface target label in the first target identification image after clustering is the vertical axis azimuth cosine coordinate of the i-th pixel in S, It represents the horizontal axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, It represents the vertical axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, N S Indicates the total number of pixels with the label S as the sea surface target, N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface;

[0120] Step 7: Construct a track map of each type of target based on the heading of each type of target in the multi-frame background cancellation detection image; Figure 7 As shown in the figure, the target track diagram obtained from the 6500th to 6650th frame continuous images is given, as shown in Figure 8 As shown in FIG, a target heading statistics diagram obtained from the 6500th to 6650th frames of continuous images is given.

[0121] Construct the track map of each type of target as described in step 7, as follows:

[0122]

[0123] Among them, T ship Represents the track diagram of each type of target, T ship (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the track diagram of each type of target, F 2,m The second target identification image of the passive interference microwave image containing the sea surface target in the mth frame, F 2,m (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the second target identification diagram of the passive interference microwave image containing the sea surface target in the mth frame, t1…t s Indicates the frame range where the target is located.

[0124] A specific embodiment of the present invention also provides a computer-readable medium.

[0125] The computer readable medium is a server workstation;

[0126] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the sea surface target detection method based on tail wave characteristics of an embodiment of the present invention.

[0127] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0128] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. A sea surface target detection method based on wake wave characteristics, characterized in that: The following steps are involved: Step 1: Input multiple consecutive frames of passive interferometric microwave images, perform image difference processing on two adjacent frames of passive interferometric microwave images in sequence, and obtain a passive interferometric microwave difference image of each frame; Step 2: Perform image identification processing on each frame of passive interferometric microwave difference image in sequence to obtain target determination results in two adjacent frames of passive interferometric microwave images; Step 3: Repeat step 2. If the target determination result is that the target exists in the passive interferometric microwave image of the two frames apart, jump to step 4. If the target determination result in the last frame of the passive interferometric microwave difference image is that the target does not exist, jump to step 1. Step 4: Based on the target determination result of step 3, multiple frames of passive interferometric microwave images containing sea surface targets are screened out from the continuous multiple frames of passive interferometric microwave images, and passive interferometric microwave images with a pure sea surface background are selected from the remaining multiple frames of passive interferometric microwave images that do not contain sea surface targets. Each frame of the passive interferometric microwave image containing the sea surface target and the passive interferometric microwave image with a pure sea surface background are sequentially subjected to image difference processing to obtain a background cancellation detection image containing the sea surface target in each frame; Step 5: obtaining multiple target pixel points and multiple target trail pixel points of each frame of background cancellation detection image respectively through target identification algorithm processing, obtaining multiple clustered target pixel points and multiple clustered target trail pixel points of each frame of background cancellation detection image respectively through adjacent clustering method, obtaining multiple optimized target pixel points and multiple optimized target trail pixel points of each frame of background cancellation detection image respectively through target identification algorithm processing, pairing the multiple optimized target pixel points and multiple optimized target trail pixel points of each frame of background cancellation detection image to obtain multiple target pixel points of each type of target and multiple target trail pixel points of each type of target in each frame of background cancellation detection image; Step 5 combines the target trail brightness temperature characteristics of each frame of background cancellation detection image with the target identification algorithm to obtain multiple target trail pixel points of each frame of background cancellation detection image, as follows: Among them, F 1,m (ξ,η) represents the first target identification map of the passive interference microwave image containing the sea surface target in the mth frame. If F 1,m (ξ,η)=1 means that there is a sea surface target at the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis azimuth cosine coordinate η in the passive interference microwave image containing the sea surface target in the mth frame. If F 1,m (ξ,η)=-1 means that there is a tail wave of the sea target at the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis ξ azimuth cosine coordinate η in the passive interference microwave image containing the sea target in the mth frame. 1,m (ξ,η)=0 means that the pixel point with the horizontal axis azimuth cosine coordinate and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image containing a sea surface target in the mth frame has no target and is the ocean background; σ0 represents the standard deviation of the passive interferometric microwave image; M1 represents the first scale factor, and M2 represents the second scale factor; Step 6: Calculate the pixel coordinates of the center position of the target pixel point of each type of target in each frame of the background cancellation detection image based on multiple target pixel points of each type of target in each frame of the background cancellation detection image; calculate the pixel coordinates of the center position of the target trail pixel point of each type of target in each frame of the background cancellation detection image based on multiple target trail pixel points of each type of target in each frame of the background cancellation detection image; and estimate the heading of each type of target in each frame of the background cancellation detection image; Step 7: Construct a track map of each type of target based on the heading of each type of target in multiple frames of background cancellation detection images.

2. The method for detecting sea surface targets based on wake characteristics according to claim 1, wherein: Step 1: The passive interferometric microwave images separated by two frames are sequentially subjected to image difference processing to obtain a passive interferometric microwave difference image of each frame, as follows: ΔT D,n(R) (ξ,η)=TB map,n (ξ,η)-TB map,(n-R) (x,h) ξ∈[-1,1],η∈[-1,1] n∈[1,NUM] Where ΔT D,n(R) represents the nth frame of passive interferometric microwave differential image when the interval is R, R represents the interval number of frames between two frames of passive interferometric microwave images, ΔT D,n(R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the nth frame of the passive interference microwave differential image when the interval is R, TB map,n represents the nth frame of passive interference microwave image, TB map,n (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interference microwave image of the nth frame, TB map,(n-R) represents the passive interference microwave image of the nRth frame, TB map,(n-R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image of the nRth frame, and NUM represents the number of passive interferometric microwave difference images.

3. The method for detecting sea surface targets based on wake characteristics according to claim 2, wherein: In step 2, each frame of the passive interferometric microwave difference image is sequentially subjected to image identification processing to obtain target determination results in two adjacent frames of the passive interferometric microwave image. The specific operation of the image identification processing is as follows: Where ΔT D,n(R) represents the nth frame of passive interferometric microwave differential image when the interval is R, R represents the interval number of frames between two frames of passive interferometric microwave images, ΔT D,n(R) (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the n-th frame of passive interferometric microwave difference image when the interval is R. F(n) represents the initial target identification of the n-th frame of passive interferometric microwave image. If F(n) = 1, it means that the n-th frame of passive interferometric microwave image contains a sea surface target. All passive interferometric microwave images containing sea surface targets are represented by TB. map,Ti Indicates that if F(n) = 0, it means that there is no sea surface target in the passive interference microwave image of the nth frame, that is, the interfered microwave image of the pure sea surface background, N represents the target judgment scale factor, and σ0 represents the standard deviation of the passive interference microwave image.

4. The method for detecting sea surface targets based on wake characteristics according to claim 3, wherein: In step 4, each frame of the passive interferometric microwave image containing the sea surface target and the passive interferometric microwave image of the pure sea surface background are sequentially subjected to image difference processing to obtain each frame of the background cancellation detection image, as follows: ξ∈[-1,1],η∈[-1,1] m∈[1,NUM1],j∈[1,NUM2] Where ΔT B,m represents the background cancellation detection image containing sea surface targets in the mth frame, ΔT B,m (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the background cancellation detection image containing the sea surface target in the mth frame, The mth frame contains the passive interference microwave image of the sea surface target, that is, the Tth frame of the sea surface target. m Frame passive interferometric microwave image, It represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interference microwave image containing the sea surface target in the mth frame, represents the passive interference microwave image of the pure sea surface background, that is, the Sth j Frame passive interferometric microwave image, represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the passive interferometric microwave image with a pure sea surface background. NUM1 represents the number of passive interferometric microwave difference images containing sea surface targets, and NUM2 represents the number of passive interferometric microwave difference images with a pure sea surface background.

5. The method for detecting sea surface targets based on wake characteristics according to claim 4, wherein: In step 5, the target identification algorithm is used to obtain multiple target pixel points and multiple target trail pixel points of each frame of the background cancellation detection image, respectively, as follows: According to the brightness temperature of each pixel point in each frame of the background cancellation detection image containing the sea surface target, the target brightness temperature feature and the target trail brightness temperature feature of each frame of the background cancellation detection image are obtained; the target brightness temperature feature of each frame of the background cancellation detection image is combined with the target brightness temperature feature of each frame of the background cancellation detection image to obtain multiple target pixel points in each frame of the background cancellation detection image; the target trail brightness temperature feature of each frame of the background cancellation detection image is combined with the target trail brightness temperature feature of each frame of the background cancellation detection image to obtain multiple target trail pixel points in each frame of the background cancellation detection image; In step 5, a plurality of clustered target pixel points and a plurality of clustered target trail pixel points of each frame of background cancellation detection image are obtained by the neighbor clustering method, as follows: A plurality of target pixel points of each frame of background cancellation detection image are clustered by a neighboring clustering method to obtain a plurality of clustered target pixel points of each frame of background cancellation detection image, and a plurality of target trail pixel points of each frame of background cancellation detection image are clustered by a neighboring clustering method to obtain a plurality of clustered target trail pixel points of each frame of background cancellation detection image; In step 5, the target identification algorithm is used to obtain multiple optimized target pixel points and multiple optimized target trail pixel points of each frame of the background cancellation detection image, respectively, as follows: The target distance feature and target tail distance feature of each frame of background cancellation detection image are calculated by combining multiple clustered target pixel points and multiple clustered target tail pixel points of each frame of background cancellation detection image. The target distance feature of each frame of background cancellation detection image is processed by a target identification algorithm to obtain multiple optimized target pixel points of each frame of background cancellation detection image. The target tail distance feature of each frame of background cancellation detection image is processed by a target identification algorithm to obtain multiple optimized target tail pixel points of each frame of background cancellation detection image.

6. The method for detecting sea surface targets based on wake characteristics according to claim 5, wherein: In step 5, the multiple target pixels of each frame of the background cancellation detection image are clustered by the neighboring clustering method to obtain multiple clustered target pixels and target trail pixels of each frame of the background cancellation detection image. The specific results of the clustered target pixels and target trail pixels are as follows: The target clustering is performed on the first target identification map, and the sea surface target label in the first target identification map after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel point in S Cosine coordinate of the longitudinal axis The tail wave label of the sea target is the horizontal axis azimuth cosine coordinate of the j-th pixel in W Cosine coordinate of the longitudinal axis That is and where 1≤i≤N S , N S Indicates the total number of pixels labeled as sea surface targets, 1≤j≤N W , N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface.

7. The method for detecting sea surface targets based on wake characteristics according to claim 6, wherein: In step 5, the target trail distance feature of each frame of the background cancellation detection image is combined with the target identification algorithm to obtain multiple optimized target trail pixel points of each frame of the background cancellation detection image, as follows: Among them, F 2,m It represents the second target identification map of the passive interference microwave image containing the sea surface target in the mth frame. If The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates If there is a sea surface target at the pixel point The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates The pixel point has no target and is the ocean background. The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates If there is a sea target wake at the pixel point The horizontal axis azimuth cosine coordinate vertical axis represents the mth frame of the passive interference microwave image containing the sea surface target. Azimuth cosine coordinates The pixel point has no target, it is the ocean background, sw It represents the distance between any two pixels in the wake of the sea surface target labeled S and the sea surface target labeled W after clustering. The specific calculation method is as follows: i=1,…,N S j=1,…,N W Among them, l min Indicates the distance threshold, Indicates that the sea surface target label in the first target identification image after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel in S, Indicates that the sea surface target label in the first target identification image after clustering is the vertical axis azimuth cosine coordinate of the i-th pixel in S, The tail wave label of the sea surface target is the horizontal axis azimuth cosine coordinate of the j-th pixel point in W, The tail wave label of the sea surface target is the vertical axis azimuth cosine coordinate of the j-th pixel in W, N S Indicates the total number of pixels with the label S as the sea surface target, N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface; If the sea surface target labeled S and the sea surface target tail wave labeled W meet the corresponding conditions after clustering, the coordinates of the sea surface target tail wave labeled S are redefined as follows in, The tail wave label of the sea surface target is the horizontal axis azimuth cosine coordinate of the i-th pixel point in W, The tail wave label of the sea surface target is the vertical axis azimuth cosine coordinate of the i-th pixel in W, It represents the horizontal axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, Indicates the longitudinal azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S.

8. The method for detecting sea surface targets based on wake characteristics according to claim 7, wherein: The heading of each type of target in each frame of the background cancellation detection image in step 6 is specifically defined as follows: in, It represents the mean of the horizontal axis azimuth cosine coordinates of all pixels with the sea surface target label S, It represents the mean of the vertical axis cosine coordinates of all pixels with the target label S on the sea surface. It represents the mean of the horizontal axis azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, The mean of the longitudinal azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S; In the heading estimation of each type of target, the calculation formulas for the mean horizontal axis azimuth cosine coordinates and the mean vertical axis azimuth cosine coordinates of the sea surface target labeled S and the sea surface target tail wave are as follows: in, It represents the mean of the horizontal axis azimuth cosine coordinates of all pixels with the sea surface target label S, It represents the mean of the vertical axis cosine coordinates of all pixels with the target label S on the sea surface. It represents the mean of the horizontal axis azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, The mean of the longitudinal azimuth cosine coordinates of all pixel points of the tail wave of the sea surface target with the sea surface target label S, Indicates that the sea surface target label in the first target identification image after clustering is the horizontal axis azimuth cosine coordinate of the i-th pixel in S, Indicates that the sea surface target label in the first target identification image after clustering is the vertical axis azimuth cosine coordinate of the i-th pixel in S, It represents the horizontal axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, It represents the vertical axis azimuth cosine coordinate of the i-th pixel point in the wake of the sea surface target with the sea surface target label S, N S Indicates the total number of pixels with the label S as the sea surface target, N W Indicates the total number of pixels with the label W, which is the target tail wave on the sea surface; Construct the track map of each type of target as described in step 7, as follows: Among them, T ship Represents the track diagram of each type of target, T ship (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the track diagram of each type of target, F 2,m The second target identification image of the passive interference microwave image containing the sea surface target in the mth frame, F 2,m (ξ,η) represents the pixel value of the horizontal axis azimuth cosine coordinate ξ and the vertical axis azimuth cosine coordinate η in the second target identification diagram of the passive interference microwave image containing the sea surface target in the mth frame, t1...t s Indicates the frame range where the target is located.

9. A computer-readable medium, characterized in that It stores a computer program executed by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 8.

Images