A polarimetric SAR target enhancement extraction method in complex ground scenes

Abstract

This invention discloses a polarimetric SAR target enhancement extraction method for complex ground scenes. This method, belonging to the field of radar technology, comprises the following steps: S1: full-scene self-correction of polarimetric distortion; S2: generation of equivalent subview images; S3: construction of a polarimetric statistical enhancement test model; S4: enhanced likelihood test based on subview data characteristics; and S5: CFAR enhanced detection based on the composite chi-squared distribution. This method effectively and quantitatively describes the differences between the target and background, significantly enhancing the target signal-to-clutter ratio and detection robustness, meeting the requirements for automatic target enhancement extraction and batch application in large-scale SAR imagery.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a polarimetric SAR target enhancement extraction method in complex ground scenes. Background Art

[0002] As an active microwave imaging system, synthetic aperture radar (SAR) has been widely used in military and national economic fields such as military reconnaissance, environmental monitoring, and land resource management. It has the advantages of all-day, all-weather, long-range, and wide mapping swaths, and can greatly improve the ability to quickly acquire information on radar targets in land and sea environments over long distances and over large areas under complex backgrounds.

[0003] SAR image target detection is the foundation of SAR image interpretation. The SAR image target detection module is a binary detection process, specifically determining the presence or absence of a target. Traditional detection and recognition algorithms have significant limitations in the universality of target feature extraction. Each feature extraction method is designed only for a specific scenario or target. For example, the CFAR detection method, based on different background clutter statistical distribution models, provides optimal CFAR detection for a variety of scene images, including uniform, generally non-uniform, and extremely non-uniform. Other methods incorporate polarization information and, through polarization target decomposition techniques, establish correlation models with target polarization scattering characteristics and typical targets. Deep learning methods, on the other hand, clearly highlight the importance of feature learning and can address the challenges of feature extraction and recognition decision design in traditional recognition algorithms. However, the key concept of SAR target detection is to exploit the characteristic differences between the target and background clutter to extract the target within complex background clutter.

[0004] The complex background conditions refer to the target existing in the bright background, the background edge, the coherent speckle noise and other areas where the target is easily submerged by the background clutter. The presence of the target in these background clutter areas is often a detection difficulty and bottleneck, resulting in the existing algorithms often having difficulty in achieving satisfactory results in terms of detection rate, false alarm rate, signal-to-clutter ratio gain, background suppression rate and other indicators. The difficulties are mainly manifested in the following aspects:

[0005] a. SAR targets usually do not have obvious shapes and textures, which is obviously not conducive to improving the detection rate of the algorithm;

[0006] b. The brightness of complex background areas in SAR images, such as bright background areas, background edges, and coherent speckle noise, is likely to overwhelm real targets, which may cause false alarms and reduce the algorithm's detection rate;

[0007] c. Polarimetric SAR images contain rich polarization information of targets, which is beneficial for target detection and recognition. However, polarimetric SAR images suffer from polarization crosstalk and amplitude imbalance between polarization channels, requiring polarization data correction to correctly interpret ground objects.

[0008] Generally speaking, when a target is located in a complex background environment, it may even be submerged by the complex background, resulting in a lower signal-to-clutter ratio (SCR), significantly increasing the difficulty of SAR target detection. Therefore, in-depth research on SAR target enhancement techniques under complex background conditions has extremely important theoretical significance and practical engineering application value for improving SAR target detection performance. To this end, a polarimetric SAR target enhancement extraction method for complex ground scenes is proposed. Summary of the Invention

[0009] The technical problem to be solved by the present invention is how to solve the problem that when a target is in a complex background environment, the target may even be submerged by the complex background, resulting in a lower signal-to-clutter ratio, which greatly increases the difficulty of SAR target detection. A polarimetric SAR target enhancement extraction method in complex ground scenes is provided.

[0010] The present invention solves the above technical problems through the following technical solutions, which include the following steps:

[0011] S1: Full-scene self-correction of polarization distortion

[0012] The polarization distortion parameters of the acquired fully polarimetric SAR image are estimated and corrected using the natural scene distribution targets and artificial targets in the full scene to obtain the polarization-corrected SAR image.

[0013] S2: Equivalent subview image generation

[0014] The polarization-corrected SAR image is equivalently generated into two low-resolution sub-view images of the forward view and the rear view through SAR image spectrum segmentation;

[0015] S3: Constructing a polarization statistical enhancement test model

[0016] Based on the distribution of polarization covariance matrix, a polarization statistical enhancement detection model for target and background clutter is constructed.

[0017] S4: Enhanced likelihood test based on sub-view data characteristics

[0018] Considering the incoherence of background clutter in the subview image, the subview image after target enhancement is obtained by subview image operation. Combined with the background and target local window size setting and sliding window processing, the enhanced likelihood detection value of the image target is calculated.

[0019] S5: CFAR enhanced detection based on composite chi-square distribution

[0020] Based on the approximate distribution representation of enhanced likelihood detection value, constant false alarm rate detection is used to extract the target from complex background clutter.

[0021] Furthermore, in step S1, the matrix form of the full polarimetric SAR image obtained by the polarimetric SAR system is as follows:

[0022]

[0023] in, is the undistorted polarization scattering matrix, S HH is the undistorted HH polarization channel data, S HV is the undistorted HV polarization channel data, S VH is the undistorted VH polarization channel data, S VV is the undistorted VV polarization channel data, R e and T e are the distortion matrices introduced by reception and transmission, respectively; N is the system noise matrix; f1, f2, f3, and f4 are the amplitude and phase imbalance terms of the transmit and receive channels, respectively; and δ1, δ2, δ3, and δ4 are the crosstalk factors between polarization channels.

[0024] Furthermore, in step S1, the specific processing process is as follows:

[0025] S11: Ignoring the influence of absolute radiation calibration and noise term, let the imbalance parameter between polarization channels be Polarization channel imbalance parameters The matrix form of the fully polarized SAR image is rewritten into a vector form as follows:

[0026]

[0027] Among them, O HH is the observed HH polarization channel data, O HV is the observed HV polarization channel data, O VH is the observed VH polarization channel data, O VV is the observed VV polarization channel data, β is the imbalance parameter between polarization channels, and γ is the imbalance parameter within polarization channels;

[0028] S12: Selecting objects whose total power SPAN value is less than 70% of the maximum total power SPAN of the entire scene image as natural scene distribution targets, and calculating the imbalance parameter β between polarization channels according to the formula;

[0029] S13: Select the target with single scattering and dihedral scattering characteristics in the image as the artificial target. The HH and VV polarization observation values ​​of the artificial target are equal. Calculate the imbalance parameter γ in the polarization channel according to the formula;

[0030] S14: Substitute the estimated inter-polarization channel imbalance parameter β and intra-polarization channel imbalance parameter γ into the vector expression of the full-polarization SAR image in step S11 to obtain a polarization-corrected SAR image.

[0031] Furthermore, in step S12, the calculation formula of the imbalance parameter β between polarization channels is:

[0032]

[0033] Among them, O VH,nat and O HV,nat are the VH and HV polarization statistics of naturally distributed objects, respectively.

[0034] Furthermore, in step S13, the calculation formula of the imbalance parameter γ in the polarization channel is:

[0035]

[0036] Among them, O HH,tri and O VV,tri are the HH and VV polarization observation values ​​of the artificial target, respectively.

[0037] Furthermore, in step S2, the specific processing process is as follows:

[0038] S21: The entire image spectrum is divided into two sub-spectra of equal width and equal interval, namely:

[0039]

[0040] Among them, f ci represents the center frequency of subview image i;

[0041] S22: Obtain the equivalent spatial domain signal of the subview image:

[0042]

[0043] S23: All sub-view spectra are converted back to the time domain through IFFT to obtain two low-resolution sub-view images of the front view and the rear view.

[0044] Furthermore, in step S3, the specific processing process is as follows:

[0045] S31: Incoherently average the n single-view covariance matrices to obtain the n-view covariance matrix W:

[0046]

[0047] Where n is the number of multi-views, S(k) is the k-th single-view sampling vector;

[0048] S32: Let A = n W, then the covariance matrix A has the form of complex Wishart distribution:

[0049]

[0050] Where Tr(·) is the trace of the matrix, Σ=E(A), K(l,p)=π p(p-1) / 2 Γ(l)…Γ(l-p+1), Γ(n) is the Gamma function, and the parameter p is the dimension of the vector S(k);

[0051] S33: Based on statistical hypothesis testing, the polarization statistical enhancement test model R is defined as follows:

[0052]

[0053] Furthermore, in step S33, the statistical hypothesis testing process is as follows:

[0054] S331: Obtain the distribution function of the n-view covariance matrix W:

[0055]

[0056] If the polarization matrices X and Y are independent and obey the complex Wishart distribution, that is, X∈W(p, k, Σ x ), Y∈W(p,l,Σ y ), then their sum X+Y also obeys the complex Wishart distribution, that is, (X+Y)∈W(p,k+l,Σ);

[0057] S332: When the data of the target window and the background window are different, Σ x ≠Σ y When:

[0058] S333: When the data of the target window and the background window are the same, Σ x =Σ y =Σ, then:

[0059]

[0060] Furthermore, in step S4, the specific processing process is as follows:

[0061] S41: The target data is the average of the sum of the two subview images in step S2, that is:

[0062]

[0063] The background data still uses full-polarization full-aperture image data;

[0064] S42: respectively set the target statistical area window size X wz and background statistics area window size Y wz , where X wz <Y wz ;

[0065] S43: Calculate the logarithmic polarization statistical enhancement test value of the image target by sliding window processing in the image, that is, the enhanced likelihood detection value. Remove the constant term and the logarithmic polarization statistical enhancement test value is simplified as follows:

[0066] -lnR=(k+l)ln|X+Y|-kln|X|-lln|Y|.

[0067] Furthermore, in step S5, the specific processing process is as follows:

[0068] S51: The probability density distribution function of the enhanced likelihood detection value is derived by the third-order Bernoulli polynomial and is approximately:

[0069] P{-lnR}≈P{χ 2 (p 2 )}+w·[P{χ 2 (p 2 +4)}-P{χ 2 (p 2 )}]

[0070] Among them, χ 2 is the chi-square distribution function, w is the weight factor;

[0071] S52: Under the condition of known enhanced likelihood detection value probability distribution function, using constant false alarm rate method and given constant false alarm probability P fa , to obtain the threshold for target enhanced detection.

[0072] Furthermore, in step S52, the threshold for target enhanced detection is obtained by solving an equation according to the CFAR detection principle. The equation is as follows:

[0073]

[0074] Where T is the threshold for target enhanced detection.

[0075] Compared with the existing technology, the present invention has the following advantages: the polarimetric SAR target enhancement extraction method in complex ground scenes effectively and quantitatively describes the difference between the target and the background, greatly enhances the target signal-to-clutter ratio and detection robustness, and meets the needs of automatic target enhancement extraction and batch application of large-scale SAR images. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 11 is a flow chart of a polarimetric SAR target enhancement extraction method in a complex ground scene according to the first embodiment of the present invention;

[0077] Figure 2 is the original SAR image (HH polarization) in the second embodiment of the present invention;

[0078] Figure 3 is the original SAR image (HV polarization) in the second embodiment of the present invention;

[0079] Figure 4 is the original SAR image (VV polarization) in the second embodiment of the present invention;

[0080] Figure 5 is the polarization-corrected SAR image in the second embodiment of the present invention;

[0081] Figure 6 This is a schematic diagram of the statistical window and range in the second embodiment of the present invention;

[0082] Figure 7 This is a diagram of the enhanced likelihood detection result in the second embodiment of the present invention. DETAILED DESCRIPTION

[0083] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0084] Example 1

[0085] This embodiment provides a technical solution: a polarimetric SAR target enhancement and extraction method in complex ground scenes. First, polarimetric distortion parameters of the acquired SAR image are estimated and corrected using naturally distributed ground objects and a small number of artificial targets in the entire scene. Spectral segmentation of the SAR image is used to equivalently generate polarimetric subview images before and after polarization. Then, based on the distribution of the polarimetric covariance matrix, a polarimetric statistical enhancement detection model for the target and background clutter is constructed. Next, considering the incoherence of background clutter in the subview image, subview image operations are used to obtain an enhanced subview image of the target. Enhanced likelihood detection values ​​of the image targets are calculated by combining local window size settings for the background and target and sliding window processing. Finally, based on an approximate distribution representation of the enhanced likelihood detection values, constant false alarm rate detection is used to extract the target from the complex background clutter.

[0086] like Figure 1 As shown, the polarimetric SAR target enhancement extraction method in complex ground scenes of the present invention specifically includes the following steps:

[0087] Step 1: Full-scene polarization distortion self-correction

[0088] Based on the obtained fully polarimetric SAR image data to be processed, the polarimetric distortion parameters are quickly estimated by using the natural scene distribution targets and artificial targets in the imaging scene itself. Specifically, the following are included:

[0089] The original 2*2 polarization scattering matrix observed by the polarimetric SAR system for:

[0090]

[0091] in, is the undistorted polarization scattering matrix, S HH is the undistorted HH polarization channel data, S HV is the undistorted HV polarization channel data, S VH is the undistorted VH polarization channel data, S VV is the undistorted VV polarization channel data, R e and T e are the distortion matrices introduced by reception and transmission respectively, N is the system noise matrix, f1, f2, f3, and f4 are the amplitude and phase imbalance terms of the transmit and receive channels respectively, and their expected values ​​are 1. The crosstalk terms δ1, δ2, δ3, and δ4 are the crosstalk factors between polarization channels, which should be sufficiently small compared to 1, δ1 = δ2 = δ3 = δ4 = 0. Without considering the influence of absolute radiation calibration and noise terms, let the imbalance parameters between polarization channels be Polarization channel imbalance parameters Formula (1) is written in vector form as:

[0092]

[0093] Among them, O HH is the observed HH polarization channel data, O HV is the observed HV polarization channel data, O VH is the observed VH polarization channel data, O VV is the observed VV polarization channel data;

[0094] Then, the statistical ratio of the image cross-polarization HV and VH is directly obtained, and the imbalance parameter β between polarization channels is obtained by formula (2):

[0095]

[0096] Among them, O VH,nat and O HV,nat are the VH and HV polarization statistics of naturally distributed objects, respectively;

[0097] Let S of the ground artificial target HH =S VV , then the imbalance parameter γ in the polarization channel can be obtained:

[0098]

[0099] Among them, O HH,tri and O VV,tri are the HH and VV polarization observation values ​​of ground artificial targets, respectively.

[0100] Step 2: Equivalent subview image generation

[0101] In the SAR imaging process, the full-resolution SAR image of the target is obtained by synthesizing low-resolution echoes from many different oblique angles. Therefore, a SAR image target does not just correspond to a single radar line of sight direction, but to a series of oblique directions in azimuth. The movement of the radar platform relative to the ground target will cause the phase of the echo signal to change continuously over time, thereby causing the instantaneous frequency of the echo to change, forming a Doppler shift effect. The Doppler frequency f of the echo signal is d The relationship between (t) and the oblique angle θ (i.e. the angle between the radar line of sight and the positive side view direction) is:

[0102]

[0103] Where v is the speed of the radar platform, R a is the distance from the radar to the ground target, and λ is the radar wavelength. However, what is often obtained is SAR data after imaging. First, it is converted to the azimuth / range spectrum domain through a one-dimensional FFT. For a point target at a distance r0 from the SAR, its spatial domain response is expressed as:

[0104]

[0105] Where B is the signal bandwidth, t0 = 2r0 / c, is a constant. The Fourier transform X(f) of x(t) is:

[0106]

[0107] in,

[0108] Then the entire image spectrum is divided into two sub-spectra of equal width and equal interval, namely:

[0109]

[0110] Among them, f ci represents the center frequency of the sub-view image i. Therefore, the equivalent spatial domain signal of the sub-view image can be written as:

[0111]

[0112] Finally, all sub-view spectra are converted back to the time domain through IFFT to obtain two low-resolution sub-view images of the front view and the rear view.

[0113] Step 3: Construct a polarization statistical enhancement test model

[0114] The n-view covariance matrix W can be obtained by incoherently averaging the n single-view covariance matrices:

[0115]

[0116] Where n is the number of multi-views and S(k) is the k-th single-view sampling vector.

[0117] Let A = n W, then the matrix A has the form of complex Wishart distribution:

[0118]

[0119] Where Tr(·) is the trace of the matrix, Σ=E(A), K(l,p)=π p(p-1) / 2 Γ(l)…Γ(l-p+1), Γ(n) is the Gamma function, and the parameter p is the dimension of the vector S(k). For the reciprocal medium of the fully polarized SAR, p=3; for the reciprocal medium of the dual-polarized SAR, p=2.

[0120] In this way, it is easy to derive the distribution function of the n-view covariance matrix W:

[0121]

[0122] If the polarization matrices X and Y are independent and both obey the complex Wishart distribution, that is, X∈W(p,k,Σ x ), Y∈W(p,l,Σ y ), then their sum X+Y also obeys the complex Wishart distribution, that is, (X+Y)∈W(p,k+l,∑).

[0123] When the data of the target window and the background window are different, Σ x ≠Σ y , then:

[0124]

[0125] When the data of the target window and the background window are the same, Σ x =Σ y =Σ, then:

[0126]

[0127] Therefore, based on the above two statistical hypothesis tests, the polarization statistical enhancement test model can be defined as:

[0128]

[0129] Step 4: Enhanced likelihood test based on sub-view data characteristics

[0130] The value of R ranges from [1, +∞). When the target and background data are completely different, the two polarization matrices X and Y can be considered independent. According to the principles of probability, p(X,Y) = p(X)p(Y), and R is equal to 1. Conversely, the closer the target and background data are, the greater the value of R, p(X,Y) < p(X)p(Y).

[0131] Since the background clutter distribution targets have poor correlation in different subview images, the target data is averaged by the sum of the two equal spectral width left and right subview images generated in step 2, that is:

[0132]

[0133] The background data still uses full polarization full aperture image data. Then, the target statistical area window size X is given. wz and background statistics area window size Y wz ,in

[0134] X wz <Y wz (17)

[0135] In this way, the logarithmic polarization statistical enhancement test value of the image target is calculated by sliding window processing in the image. Removing the constant term, the logarithmic polarization statistical enhancement test value can be simplified to:

[0136] -lnR=(k+l)ln|X+Y|-kln|X|-lln|Y| (18)

[0137] Step 5: CFAR enhanced detection based on composite chi-square distribution

[0138] Since a certain order moment of the asymptotic expansion of the distribution of a random variable can generally be expressed as a specific function form, the probability density of -lnR can be derived through the third-order Bernoulli polynomial to be approximated as:

[0139] P{-lnR}≈P{χ 2 (p 2 )}+w·[P{χ 2 (p 2 +4)}-P{χ 2 (p 2 )}](19)

[0140] Among them, χ 2 is the chi-square distribution function, and w is the weight factor.

[0141] The -lnR value represents the degree of difference of the corresponding features.

[0142] Finally, under the condition that the probability density distribution function form of -lnR is known, the constant false alarm rate method and the given constant false alarm probability P are used. fa , to obtain the threshold for target enhanced detection.

[0143] Example 2

[0144] The present invention uses polarization crosstalk and polarization channel imbalance parameter estimation and correction for naturally distributed objects and a small number of artificial targets in a SAR image. Polarization left and right sub-view images are equivalently generated through SAR image spectrum segmentation. Based on the polarization covariance matrix distribution, a polarization statistical enhancement detection model for the target and background clutter is constructed, effectively and quantitatively describing the differences between the target and background. Next, considering the incoherence of background clutter in the sub-view image, an enhanced sub-view image of the target is obtained through sub-view image calculation. Combined with the local window size setting and sliding window processing of the background and target, the enhanced likelihood detection value of the image target is calculated, greatly enhancing the target signal-to-clutter ratio and detection robustness. Finally, based on the approximate distribution representation of the enhanced likelihood detection value, a constant false alarm rate (CFAR) detection method can be used to extract the target from complex background clutter, meeting the requirements of large-scale SAR image automatic target enhancement extraction and batch application. Finally, programming software based on the present invention is developed and implemented on a standard computer.

[0145] The following combination Figure 2-Figure 7 The present invention will be further described.

[0146] First, obtain the fully polarimetric SAR image according to step 1. Figure 2-Figure 4 They are the original HH polarization, HV polarization, and VV polarization SAR images, respectively. The image size is 4000 pixels * 8192 pixels, the radar wavelength λ is 0.03125m, and the image resolution is 0.5m.

[0147] More specifically, objects below 70% of the maximum value of the total power SPAN image are used as natural scene distribution targets to calculate the VH and HV polarization statistics of the natural distribution objects, and the imbalance parameter β between polarization channels is calculated according to equation (3) in step 1.

[0148] As a more specific example, ground artificial targets are selected as targets that generally exhibit single scattering and dihedral scattering characteristics, such as electric towers and electric poles in the image. In this way, the HH and VV polarization observation values ​​of the ground artificial targets satisfy S HH =S VV, then according to formula (4), the imbalance parameter γ in the polarization channel can be obtained. Finally, according to the estimated imbalance parameters between polarization channels and within the channel, substitute them into formula (2) to obtain the polarization-corrected SAR image, as shown in Figure 5 shown.

[0149] According to step 2, the HH, HV, VH, and VV image spectra are divided into two sub-video spectra with equal width and equal intervals, namely:

[0150]

[0151] Among them, f ci represents the center frequency of the sub-view image i. Therefore, the equivalent spatial domain signal of the sub-view image can be written as:

[0152]

[0153] All sub-view spectra of HH, HV, VH, and VV are reconverted to the time domain through IFFT to obtain two sets of low-resolution full-polarization sub-view images for the forward view and the rear view.

[0154] According to step 3, the covariance matrix A can be obtained by incoherently averaging the single-view covariance matrix. Then the matrix A has the form of complex Wishart distribution:

[0155]

[0156] Where Tr(·) is the trace of the matrix, Σ=E(A), K(l,p)=π p(p-1) / 2 Γ(l)…Γ(l-p+1), Γ(n) is the Gamma function, parameter p is the dimension of vector X, for fully polarized SAR reciprocal media, p = 3;

[0157] Based on the statistical hypothesis test in step 3, the polarization statistical enhancement test model R can be calculated as:

[0158]

[0159] According to step 4, since the background clutter distribution target has poor correlation in different subview images, the target data adopts the average of the sum of the two equal spectral width left and right subviews generated in step 2, that is:

[0160]

[0161] The background data still uses full-aperture image data, that is:

[0162] Y=x(t) (24)

[0163] Then, according to the target of interest, this embodiment takes the tower target as an example, and sets the target statistical area window size X wzis 31, and the background statistics area window size Y wz is 43, the statistical window and range diagram are as follows Figure 6 Then, according to formula (18) (where k=l=1), the logarithmic polarization statistical enhancement test values ​​of the image targets are calculated in sequence through image sliding window processing.

[0164] Finally, according to step 5, let p = 3, calculate the probability density distribution function of the log-polarization statistical enhancement test value according to formula (19), and set the constant false alarm probability P fa =1.0e-5, solve the following equation according to the CFAR detection principle to calculate the threshold T for enhanced detection and obtain the enhanced likelihood detection result, see Figure 7 .

[0165]

[0166] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A polarimetric SAR target enhancement extraction method in complex ground scenes, characterized by: The following steps are involved: S1: Full-scene self-correction of polarization distortion The polarization distortion parameters of the acquired fully polarimetric SAR image are estimated and corrected using the natural scene distribution targets and artificial targets in the full scene to obtain the polarization-corrected SAR image. S2: Equivalent subview image generation The polarization-corrected SAR image is equivalently generated into two low-resolution sub-view images of the forward view and the rear view through SAR image spectrum segmentation; S3: Constructing a polarization statistical enhancement test model Based on the distribution of polarization covariance matrix, a polarization statistical enhancement detection model for target and background clutter is constructed. S4: Enhanced likelihood test based on sub-view data characteristics Considering the incoherence of background clutter in the subview image, the subview image after target enhancement is obtained by subview image operation. Combined with the background and target local window size setting and sliding window processing, the enhanced likelihood detection value of the image target is calculated. S5: CFAR enhanced detection based on composite chi-square distribution Based on the approximate distribution representation of enhanced likelihood detection value, constant false alarm rate detection is used to extract the target from complex background clutter.

2. The polarimetric SAR target enhancement extraction method in a complex ground scene according to claim 1, characterized in that: In step S1, the matrix form of the full polarimetric SAR image obtained by the polarimetric SAR system is as follows: in, is the undistorted polarization scattering matrix, S HH is the undistorted HH polarization channel data, S HV is the undistorted HV polarization channel data, S VH is the undistorted VH polarization channel data, S VV is the undistorted VV polarization channel data, R e and T e are the distortion matrices introduced by reception and transmission, respectively; N is the system noise matrix; f1, f2, f3, and f4 are the amplitude and phase imbalance terms of the transmit and receive channels, respectively; and δ1, δ2, δ3, and δ4 are the crosstalk factors between polarization channels.

3. The polarimetric SAR target enhancement extraction method in a complex ground scene according to claim 2, characterized in that: In step S1, the specific processing process is as follows: S11: Ignoring the influence of absolute radiation calibration and noise term, let the imbalance parameter between polarization channels be Polarization channel imbalance parameters The matrix form of the fully polarized SAR image is rewritten into a vector form as follows: Among them, O HH is the observed HH polarization channel data, O HV is the observed HV polarization channel data, O VH is the observed VH polarization channel data, O VV is the observed VV polarization channel data, β is the imbalance parameter between polarization channels, and γ is the imbalance parameter within polarization channels; S12: Selecting objects whose total power SPAN value is less than 70% of the maximum total power SPAN of the entire scene image as natural scene distribution targets, and calculating the imbalance parameter β between polarization channels according to the formula; S13: Select the target with single scattering and dihedral scattering characteristics in the image as the artificial target. The HH and VV polarization observation values ​​of the artificial target are equal. Calculate the imbalance parameter γ in the polarization channel according to the formula; S14: Substitute the estimated inter-polarization channel imbalance parameter β and intra-polarization channel imbalance parameter γ into the vector expression of the full-polarization SAR image in step S11 to obtain a polarization-corrected SAR image.

4. The method for polarimetric SAR target enhancement extraction in complex ground scenes according to claim 3, characterized in that: In step S12, the calculation formula of the imbalance parameter β between polarization channels is: Among them, O VH,nat and O HV,nat are the VH and HV polarization statistics of naturally distributed objects, respectively.

5. The polarimetric SAR target enhancement extraction method in a complex ground scene according to claim 3 is characterized in that: In step S13, the calculation formula of the imbalance parameter γ in the polarization channel is: Among them, O HH,tri and O VV,tri are the HH and VV polarization observation values ​​of the artificial target, respectively.

6. The method for polarimetric SAR target enhancement extraction in complex ground scenes according to claim 3, characterized in that: In step S2, the specific processing process is as follows: S21: The entire image spectrum is divided into two sub-spectra of equal width and equal interval, namely: Among them, f ci represents the center frequency of subview image i, is a constant; S22: Obtain the equivalent spatial domain signal of the subview image: Where B is the signal bandwidth, t0 = 2r0 / c; S23: All sub-view spectra are converted back to the time domain through IFFT to obtain two low-resolution sub-view images of the front view and the rear view.

7. The method for polarimetric SAR target enhancement extraction in complex ground scenes according to claim 6, characterized in that: In step S3, the specific processing process is as follows: S31: Incoherently average the n single-view covariance matrices to obtain the n-view covariance matrix W: Where n is the number of multi-views, S(k) is the k-th single-view sampling vector; S32: Let A = n W, then the covariance matrix A has the form of complex Wishart distribution: Where Tr(·) is the trace of the matrix, Σ=E(A), K(l,p)=π p(p-1) / 2 Γ(l)…Γ(l-p+1), Γ(n) is the Gamma function, and the parameter p is the dimension of the vector S(k); S33: Based on statistical hypothesis testing, the polarization statistical enhancement test model R is defined as follows: Where X and Y are polarization matrices.

8. The method for polarimetric SAR target enhancement extraction in complex ground scenes according to claim 7, characterized in that: In step S33, the statistical hypothesis testing process is as follows: S331: Obtain the distribution function of the n-view covariance matrix W: If the polarization matrices X and Y are independent and obey the complex Wishart distribution, that is, X∈W(p, k, Σ x ), Y∈W(p,l,Σ y ), then their sum X+Y also obeys the complex Wishart distribution, that is, (X+Y)∈W(p,k+l,∑); S332: When the data of the target window and the background window are different, Σ x ≠Σ y When: S333: When the data of the target window and the background window are the same, Σ x =Σ y =Σ, then:

9. The method for polarimetric SAR target enhancement extraction in complex ground scenes according to claim 8, characterized in that: In step S4, the specific processing process is as follows: S41: The target data is the average of the sum of the two subview images in step S2, that is: The background data still uses full-polarization full-aperture image data; S42: respectively set the target statistical area window size X wz and background statistics area window size Y wz , where X wz <Y wz ; S43: Calculate the logarithmic polarization statistical enhancement test value of the image target by sliding window processing in the image, that is, the enhanced likelihood detection value. Remove the constant term and the logarithmic polarization statistical enhancement test value is simplified as follows: -lnR=(k+l)ln|X+Y|-kln|X|-lln|Y|.

10. The method for polarimetric SAR target enhancement extraction in complex ground scenes according to claim 9, characterized in that: In step S5, the specific processing process is as follows: S51: The probability density distribution function of the enhanced likelihood detection value is derived by the third-order Bernoulli polynomial and is approximately: P{-lnR}≈P{x 2 (p 2 )}+w·[P{χ 2 (p 2 +4)}-P{x 2 (p 2 )}] Among them, χ 2 is the chi-square distribution function, w is the weight factor; S52: Under the condition of known enhanced likelihood detection value probability distribution function, using constant false alarm rate method and given constant false alarm probability P fa , to obtain the threshold for target enhanced detection.

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