SAR Polarization Phase Imbalance Estimation Method Based on the Statistics of Equivalent Rotating Dihedral Angle Point Targets in Urban Areas

Abstract

The present invention proposes a method for estimating the polarization phase imbalance of SAR based on the statistics of equivalent rotation dihedral corner point targets in urban areas. For the L1A single-look complex data of dual-polarization and full-polarization SAR, the polarization phase imbalance of the SAR system can be calculated; point targets that are strong scatterers in both the co-polarization and cross-polarization channel data scenarios are extracted as polarization strong scatterer points; taking these polarization strong scatterer points as the center, the coherence coefficient between cross-polarization and co-polarization is calculated for the local area, and the point targets with strong polarization coherence obtained by screening are used as equivalent rotation dihedral corner point targets; the numerical value of the phase difference between the co-polarization and cross-polarization corresponding to the peak value of each equivalent rotation dihedral corner point target is calculated as the phase imbalance estimation value of this point; the aggregation center of the phase imbalance estimation value is obtained by statistically analyzing the phase imbalance estimation values of all equivalent rotation dihedral corner point targets; combined with the specific secondary reflection structure of the scene, 180-degree phase ambiguity determination is performed to obtain the final phase imbalance estimation value of the SAR system.

Description

Technical Field

[0001] The present invention relates to the processing of Synthetic Aperture Radar (SAR) images, and particularly to a method, system, computer, and storage medium for estimating SAR polarization phase imbalance based on the statistics of equivalent rotating dihedral corner point targets in urban areas. Background Art

[0002] The multi-polarization SAR technology is a mature technology widely used in current spaceborne and airborne SAR systems. The demand for quantitative applications of multi-polarization SAR data is increasing in various fields such as agriculture, forestry, disaster reduction, and oceanography. The quantitative application of multi-polarization SAR data highly depends on the quality of polarization data, including the accuracy of radiation intensity, polarization scattering matrix, etc., which is mainly achieved through radiation and polarization calibration and long-term data quality monitoring. Among the polarization distortion parameters, polarization crosstalk and amplitude imbalance of polarization channels can generally be controlled at a relatively excellent level with the development and maturity of antenna hardware and imaging compensation technologies. For many spaceborne SAR antenna systems, crosstalk can meet the index requirements without calibration. However, the polarization channel phase imbalance distortion parameter is generally difficult to effectively control and may vary with the wave position and depression angle, which is the most complex and worthy of attention polarization system distortion parameter.

[0003] Traditional SAR polarization calibration, etc., relies on specific calibration fields and calibrators, such as artificial corner reflectors, polarization active calibrators, etc., or specific ground object targets such as tropical rainforests. Although the method of artificial calibrators has advantages such as high accuracy and controllability and is widely used in the calibration of domestic and foreign SAR systems, the layout and maintenance of precise calibrators in the calibration field require a large amount of manpower and material resources, and there are significant limitations in the measurement range, calibration times, etc. It cannot provide a normalized continuous calibration reference during a long satellite operation cycle. The calibration method based on natural targets in tropical rainforests also has obvious regional limitations, and tropical rainforests themselves have certain dependencies or instabilities in terms of space, time, incident angle, etc., affecting the calibration accuracy. Therefore, it is of great significance to study the use of ground object targets widely existing in the SAR scene or to extract their polarization characteristics for multi-polarization SAR polarization calibration. In addition, for dual-polarization SAR data, due to the reduction of observed data, only two polarization channel data can be obtained, and the polarization scattering characteristics of some volume scattering distribution targets are difficult to use, and the field-free calibration of polarization distortion is underdetermined. Therefore, the calibration of current dual-polarization SAR polarization data all adopts the method of calibrators, and there are basically no mature research results using general natural ground objects.

[0004] Under such a demand background, based on the research results of American scholar Atwood and French scholar Thirion-Lefevre on the radar wave scattering performance in urban areas of SAR images, their research pointed out that double scattering widely exists in urban areas, and the polarization performance of double scattering in urban areas can be equivalent to the polarization scattering characteristics of a dihedral angle. Based on this, the author deeply studied the characteristics of double scattering widely existing in urban areas, innovatively proposed an effective extraction technology for equivalent rotating dihedral angle point targets in urban areas, and studied its performance under the polarization distortion of a multi-polarization SAR system. The author innovatively invented and implemented an effective information extraction method for an equivalent rotating dihedral angle target point target set and a corresponding estimation and statistical method for the polarization phase imbalance distortion parameters of the transmitting and receiving ends of the SAR system. Therefore, the present invention proposes a SAR polarization phase imbalance estimation method based on the statistics of equivalent rotating dihedral angle point targets in urban areas, and the estimation accuracy of this method can reach within 5 degrees. Summary of the Invention

[0005] The object of the present invention is to propose a SAR polarization phase imbalance estimation method, system, computer and storage medium based on the statistics of equivalent rotating dihedral angle point targets in urban areas.

[0006] The technical solution for realizing the object of the present invention is: a SAR polarization phase imbalance estimation method based on the statistics of equivalent rotating dihedral angle point targets in urban areas, including the following steps:

[0007] Step 1, extract point targets that are strong scatterers in both the co-polarization and cross-polarization channel scenarios as polarization strong scatterer points;

[0008] Step 2, taking the extracted polarization strong scatterer points as the center, calculate the coherence coefficient between cross-polarization and co-polarization in the local area, and use the point targets with strong polarization coherence obtained by screening as equivalent rotating dihedral angle point targets;

[0009] Step 3, calculate the numerical value of the phase difference between co-polarization and cross-polarization corresponding to the peak value of each equivalent rotating dihedral angle point target as the phase imbalance estimation value of this point;

[0010] Step 4, statistically obtain the aggregation center of the phase imbalance estimation values for all equivalent rotating dihedral angle point targets, and determine the SAR polarization phase imbalance estimation value of the scenario;

[0011] Step 5, combine the specific secondary reflection structure of the scenario, perform 180-degree phase ambiguity determination, and obtain the final SAR system phase imbalance estimation value.

[0012] Furthermore, in step 1, the method for extracting point targets that are strong scatterers in both the co-polarization and cross-polarization channel scenarios as polarization strong scatterer points is as follows:

[0013] Step 1.1: For dual-polarization SAR data, calculate the receiver phase imbalance using two polarization channels. For quad-polarization SAR data, calculate the receiver phase imbalance using the HH co-polarization channel and the HV cross-polarization channel, or the VV co-polarization channel and the VH cross-polarization channel. Calculate the transmitter phase imbalance using the HH co-polarization channel and the VH cross-polarization channel, or the VV co-polarization channel and the HV cross-polarization channel;

[0014] Step 1.2: Calculate the average value of the amplitudes of all pixels in the co-polarization and cross-polarization channel scenarios respectively. Select point targets where the pixel amplitudes in the cross-polarization scenario are all greater than or equal to K1 times the average amplitude of the cross-polarization scenario, and the pixel amplitudes in the co-polarization scenario are all greater than or equal to K2 times the average amplitude of the co-polarization scenario. The obtained point targets are called polarization strong scatterers, where the numerical values of the parameter factors K1 and K2 range from [1, 10].

[0015] Further, in Step 2: Taking these polarization strong scatterers obtained by extraction as the center, calculate the coherence coefficient between the cross-polarization and co-polarization for the local area, and use the point targets with strong polarization coherence obtained by screening as equivalent rotation dihedral angle point targets. The specific method is as follows:

[0016] Taking the polarization strong scatterers obtained by extraction in Step 1 as the center, select local area pixels with a size of 5 pixels * 5 pixels, calculate the complex coherence coefficient amplitude of the corresponding area pixels in the cross-polarization channel and the co-polarization channel, and extract the polarization strong scatterer targets whose values are greater than the threshold C as equivalent rotation dihedral angle point targets, where the value range of C is [0.6, 0.9].

[0017] Further, in Step 3: Calculate the numerical value of the phase difference between the co-polarization and cross-polarization corresponding to the peak value of each equivalent rotation dihedral angle point target as the phase imbalance estimation value of this point. The specific method is as follows:

[0018] Extract the complex value of the co-polarization channel and the complex value of the cross-polarization channel corresponding to the peak value of the pixel of the corresponding equivalent rotation dihedral angle point target. Multiply the complex value of the co-polarization of this point target by the conjugate of the complex value of the cross-polarization point target, and take the phase of the result, that is, the co-polarization cross-polarization phase difference. This phase value is the phase imbalance value estimated for this point target, and normalize the extracted phase imbalance values to between -180 degrees and 180 degrees.

[0019] Further, in Step 4: Statistically obtain the clustering center of the phase imbalance estimation values for all equivalent rotation dihedral angle point targets, and determine the SAR polarization phase imbalance estimation value of the scene. The specific method is as follows:

[0020] For the numerous phase imbalance estimation values obtained from all equivalent rotation dihedral angle point targets in step 3 and normalized to the range of -180 degrees to 180 degrees, a statistical distribution histogram is made. The abscissa is the phase imbalance value at 1-degree intervals in the range of -180 degrees to 180 degrees, and the ordinate is the number of equivalent rotation dihedral angles corresponding to each interval. The distribution histogram of this phase imbalance is statistically analyzed. Within the range of plus or minus 40 degrees of the interval where the highest peak in the histogram is located, using the center points at the tops of the columns in each interval as fitting points, a normal curve fitting is performed, and the abscissa where the maximum value of the fitting curve is located is calculated to obtain the aggregation center of this phase imbalance, which is used as the phase imbalance estimation value determined for this scenario.

[0021] Further, in step 5, in combination with the specific secondary reflection structure of the scenario, a 180-degree phase ambiguity determination is performed to obtain the specific method for the final SAR system phase imbalance estimation value:

[0022] For the equivalent rotation dihedral angle point target corresponding to the highest peak where the aggregation center is located in the statistics of step 4, randomly select a point target. According to the position of the point target in the scenario, determine the wall orientation of its secondary reflection structure, and in combination with the down-view angle, calculate the rotation angle of its equivalent dihedral angle. According to the co-polarization and cross-polarization phase relationship of this equivalent rotation dihedral angle point target, determine whether there is a 180-degree phase deviation in the phase imbalance estimation value obtained from the statistics. If not, directly output it. If so, perform a 180-degree phase shift on the result of step 4 as the final result.

[0023] A SAR polarization phase imbalance estimation system based on the statistics of equivalent rotation dihedral angle point targets in urban areas, which realizes the SAR polarization phase imbalance estimation based on the described SAR polarization phase imbalance estimation method based on the statistics of equivalent rotation dihedral angle point targets in urban areas.

[0024] A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it realizes the SAR polarization phase imbalance estimation based on the described SAR polarization phase imbalance estimation method based on the statistics of equivalent rotation dihedral angle point targets in urban areas.

[0025] A computer-readable storage medium has a computer program stored thereon. When the computer program is executed by a processor, it realizes the SAR polarization phase imbalance estimation based on the described SAR polarization phase imbalance estimation method based on the statistics of equivalent rotation dihedral angle point targets in urban areas.

[0026] Compared with the prior art, the present invention has the following remarkable advantages: 1) The present invention can accurately estimate the parameter of polarization phase imbalance without field distortion for dual-polarization SAR data, which cannot be achieved by the current prior art. This is realized by using a brand-new ground reference target, i.e., the secondary scattering feature widely existing in urban areas. The present invention creatively applies it to polarization distortion estimation. Compared with the problem of underdetermined equations that is difficult to solve for distributed targets such as rainforests in a dual-polarization SAR system, the present invention can preferably estimate the phase imbalance without a calibrator. 2) The present invention has a wide range of applicable scenarios, with little spatial limitation for calibration, and does not require the invocation of satellite imaging resources, so the cost is low. This is realized by the secondary scattering structure within the scenario. Due to the wide range of human activities, the artificial buildings composed of horizontal planes and vertical walls have a wide distribution range. As long as there is a certain amount of artificial buildings in villages and towns in the scenario, it can be used not only in urban areas. This makes the calibration space limitation of the present invention very small, unlike traditional methods based on calibration fields and tropical rainforests that require imaging of specific areas. Therefore, the present invention has low cost and small limitation, which is a huge advantage compared with the prior art. 3) The present invention has low requirements for ground targets, good robustness and applicability of the method. This is because of the method for extracting the equivalent rotating dihedral angle target within the scenario. Strong polarization scattering screening can effectively avoid the influence of noise clutter and remove the influence of other scattering types such as single scattering. Strong polarization coherence screening can effectively avoid the influence of complex multipath scattering in urban areas and ensure the polarization information of the target. In addition, the present invention adopts the method of statistical phase aggregation center, so it avoids the interference of considering a few discrete values, which also reduces the requirements for ground targets. Compared with the traditional method of precisely processing and analyzing point targets, the present invention has strong robustness and stability and has good tolerance for sidelobe interference of strong points in SAR data. 4) The present invention has good applicability to SARs of multiple different bands and can be applied to airborne and spaceborne SAR polarization data in multiple bands such as P, L, S, C, X, Ka, Ku, etc. The data imaging modes can include multiple SAR imaging modes such as spotlight, stripmap, and scan. This is because of the secondary scattering signal feature of the artificial buildings adopted by the present invention, which has wide applicability to the wavelength of the SAR radar. The longest wavelength of the SAR radar does not exceed the meter level, and the shortest wavelength can also be applicable because the surface of general building structures is relatively smooth. The traditional calibrator method needs to formulate calibrators of different sizes due to different radar wavelengths, and the rainforest distribution target method also has applicability problems due to the penetration of low-band radar waves into vegetation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of a method for estimating SAR polarization phase imbalance based on the statistics of equivalent rotating dihedral angle points in urban areas.

[0028] Figure 2It is the polarization scattering characteristic diagram of a standard dihedral angle; (a) The normalized polarization scattering intensity diagrams of HH co-polarization and HV cross-polarization for dihedral angles with different rotation angles; (b) The scattering signals of the two polarization channels for the dihedral angle obtained by screening with a scattering intensity threshold of 0.5 (only the signals in some rotation angle intervals are retained, corresponding to two types: HH-HV in-phase and HH-HV out-of-phase).

[0029] Figure 3 It is a schematic geometric diagram of an equivalent rotating dihedral angle of a secondary scattering structure composed of a building wall and a horizontal plane.

[0030] Figure 4 It is the experimental result and verification of the phase imbalance estimation of GF-3 uncalibrated full-polarization data in the present invention; (a) The result diagram of equivalent rotating dihedral angle point targets extracted from an uncalibrated local scene of GF-3. It can be seen that the point targets are densely distributed in the building group area; (b) The result diagram of the receiver phase imbalance of this scene calculated by the full-polarization calibration algorithm based on distributed targets in this scene, which is used as a reference for the actual phase imbalance of this SAR system to verify the effectiveness of the method of the present invention; (c) The statistical histogram of the receiver phase imbalance of this scene obtained by the present invention. The right peak is the target peak after 180-degree phase ambiguity determination, and the final phase imbalance estimation is obtained after curve fitting. Specific implementation manners

[0031] In order to make the purpose, technical solutions and advantages of this application clearer, the following further elaborates on this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0032] As Figure 1 shown, a method for estimating the SAR polarization phase imbalance based on the statistics of equivalent rotating dihedral angle point targets in urban areas according to the present invention includes the following steps:

[0033] S101. For dual-polarization and full-polarization SAR data, use the co-polarization and cross-polarization data of single-look complex data at L1A level as the data input for the method of the present invention. In the example of the present invention, the uncalibrated full-polarization data of GF-3 imaged in 2016 is used as the example data. The input data does not require additional preprocessing operations and directly uses the L1A raw data product. The scene is most suitable for the scene imaged in the urban area. However, the invention does not require urban area screening operations or scene geographical location screening operations. In order to realize the normalized polarization phase imbalance monitoring of any scene, the invention does not make requirements for specific scenes. However, the number of equivalent rotation dihedral corner point targets extracted from the scene will be used as a reference index for the reliability of the distortion parameter estimation result. The example scene is imaged in the urban-rural area near Jiangmen City, Guangzhou, including mountains, forests, farmlands and a large number of urban areas. For full-polarization SAR data, use the HH co-polarization plus HV cross-polarization channel or the VV co-polarization plus VH cross-polarization channel to calculate the receiver phase imbalance. Use the HH co-polarization plus VH cross-polarization channel or the VV co-polarization plus HV cross-polarization channel of the full-polarization data to calculate the transmitter phase imbalance. For the dual-polarization data of single-transmission and dual-reception, directly use the co-polarization and cross-polarization data of the two polarization channels of the dual-polarization data as the data input to calculate the transmitter phase imbalance. In the example, use the HH polarization and HV polarization of this scene to estimate the receiver phase imbalance of the system.

[0034] S102. Screen the point targets with strong co-polarization and cross-polarization scattering in the scene. First, calculate the average value of the complex pixel values of the cross-polarization channel and the co-polarization channel data in the whole scene, and then traverse the scene pixels to screen out the point targets where the pixel amplitude in the cross-polarization scene is greater than or equal to K1 times the average value of the cross-polarization scene amplitude, and the pixel amplitude in the co-polarization scene is greater than or equal to K2 times the average value of the co-polarization scene amplitude. The following calculation formula is used is the amplitude mean value of the whole scene corresponding to the HV polarization channel image, is the amplitude mean value of the whole scene corresponding to the HH polarization channel image, is the amplitude of the i-th pixel in the corresponding HH image, is the amplitude of the i-th pixel in the corresponding HV image. The symbol ∩ represents the logical AND operation on the two screening conditions. The multiplication parameters K1 and K2 are both selected as 3 here. These extracted pixel point targets are called polarization strong scattering points. The effect of this polarization strong scattering screening is the same as that of Figure 2 (b). The polarization strong scattering screening extracts the signals of the dihedral type scattering characteristics in some specific rotation angle intervals, and at the same time can effectively reduce the interference of noise clutter and remove the interference of some other strong scattering characteristics such as trihedral scattering characteristics and single scattering characteristics.

[0035] S103. For the polarized strong scatter points extracted from this scene, traverse each polarized strong scatter point. Taking the polarized strong scatter point as the center, slice the pixels in the local area of size 5*5, and calculate the coherence coefficient of the slice in the HH image (co-polarization) and the slice in the HV image (cross-polarization). When the coherence coefficient of the polarized strong scatter point is greater than a threshold, the equivalent rotation dihedral corner target can be screened out. It can be expressed as In the formula, the Coherence() operation represents calculating the magnitude of the complex coherence coefficient. In the formula represents the slice data of the i-th polarized strong scatter point in the HH polarization channel, represents the slice data of the i-th polarized strong scatter point in the HV polarization channel. C is the coherence coefficient threshold for screening. In technical processing, this value is preferably set between 0.6 and 0.9. In the example, this value is optimally set to 0.8. The output of this step is the finally extracted set of equivalent rotation dihedral corner targets. The strong polarization coherence filtering in this step can prevent the extracted point targets from depolarizing due to the complex multipath scattering in the urban area, and at the same time, it can also remove some invalid strong scatter point targets that exist in bare rocks, ridges, etc. and cause interference. In the example, the equivalent rotation dihedral corner targets extracted from the local area in this GF-3 scene are highlighted in Figure 4 (a), and it can be seen that they are densely distributed in the building complex. In addition, the present invention calculates the number of equivalent rotation dihedral corner targets in the entire scene as the parameter "effective number", which can be used to evaluate the reliability of the parameter estimation of the method of the present invention. When the effective number is small, it means that there are insufficient building targets in the scene, or the scene is imaged in mountainous waters, and finally the estimated result is unreliable. An ideal scene requires the effective number to be greater than 10,000.

[0036] S104. For the numerous equivalent rotation dihedral corner targets in the scene extracted in S103, extract the complex value at the pixel of the point target, and use the co-polarization cross-polarization phase difference of each point target as the phase imbalance estimation value of the point target. In this step, the phase imbalance estimation of each point is realized. The following gives a simple theoretical explanation. Since the polarization scattering matrix of the rotation dihedral is In the formula, Θ is the rotation angle of the standard dihedral relative to the radar line of sight, S hh / S hv / S vh / S vv respectively represent the true ground scatter signals of the HH / HV / VH / VV four polarization channels. The scattering intensities of HH and HV change with the rotation angle of the dihedral as shown in Figure 2 (a). In the present invention, the equivalent rotation dihedral corner targets extracted in step S103 are strong scatter point targets for both the HH and HV polarization channels. Therefore, as shown in Figure 2As shown in (b), the rotation angles of the equivalent rotated dihedral corner point targets are restricted within certain intervals centered around the four values of -67.5 degrees, -22.5 degrees, 22.5 degrees, and 67.5 degrees. As Figure 2 As shown in (b), for such point targets, the phase relationships of the HH and HV polarization channels are arg(S hh ) = arg(S hv ) and arg(S hh ) = -arg(S hv ), where arg() in the formula is to calculate the phase of a complex number.

[0037] Considering the influence of polarization distortion, the observed HH channel data M hh and the HV channel data M hv are respectively where Ae jΦ in the formula is the fixed system gain and phase relative to the HH polarization channel, and f in the formula is the polarization channel imbalance and its phase is the phase imbalance calculated in the present invention. Here, δ1 / δ2 / δ3 / δ4 are respectively the crosstalk factors between the polarization channels at the transmitting and receiving ends of the system. Under the modern SAR radar antenna processing and control technology, they are very small values. Generally, the antennas of spaceborne SAR can easily reach a level of -30 dB or better. When processing with the relationship of the same polarization cross-polarization anti-phase of the dihedral angle (arg(S hh ) = -arg(S hv )), so here we can obtain where * in the formula is to take the conjugate of a complex number, and <·> is to calculate the local spatial average. It can be seen that the phase imbalance, that is, the phase of the complex number f, can be approximated as Therefore, the present invention uses the method of calculating the same polarization cross-polarization phase difference to calculate the estimated value of the phase imbalance for each equivalent rotated dihedral corner point target. Theoretical verification shows that the estimation accuracy of this estimation method can reach a phase error significantly less than 5 degrees under the condition of -35 dB crosstalk. In addition, in this step, the calculated estimated values of each phase imbalance are normalized to between -180 degrees and 180 degrees.

[0038] S105. For the estimated values of the phase imbalance of the numerous equivalent rotated dihedral corner point targets calculated in the previous step, make a statistical distribution histogram. The abscissa is the phase imbalance value from -180 degrees to 180 degrees with an interval of 1 degree, and the ordinate is the number of equivalent rotated dihedral corner point targets corresponding to each interval. In the example scenario, this statistical histogram can be seen Figure 4(c) Due to the irregularity of the orientation of the scene building complex, a bimodal phenomenon will be formed on the statistical distribution histogram. In this step, the data clustering center is statistically calculated, that is, the phase imbalance value corresponding to the peak in its histogram is statistically calculated. The central frequency of the histogram interval within a total of 80 degrees, 40 degrees before and after the mode interval where the peak is located, is used as the fitting point for normal curve fitting, and the position where the maximum value of the fitting curve is located is calculated, that is, the phase imbalance value corresponding to the peak is obtained. Since the co-polarized cross-polarized anti-phase relationship of the dihedral angle is used in step S104, which corresponds to the dihedral angle rotation interval of -22.5 degrees and 67.5 degrees, but when the rotation angles of the equivalent rotation dihedral angle point targets corresponding to the building orientations in the complex scene are 22.5 degrees and -67.5 degrees, the phase relationship between co-polarization and cross-polarization is the same, and the calculated results differ by 180 degrees at this time, forming a pseudo-peak, that is Figure 4 There will be two peaks in (c). For such 180-degree phase ambiguity, it is necessary to make a determination to determine which of the two peaks is correct.

[0039] S106. If the application scenario is to conduct long-term polarization quality monitoring on calibrated multi-polarization SAR data, the phase imbalance estimation result obtained in this step can be used as the final phase imbalance result, because the polarization phase imbalance after calibration cannot suddenly change by more than 90 degrees, and the value of its target peak should be near 0 degrees. If it is near 180 degrees, it means that the secondary peak of this scene should be the target peak. At this time, the 180-degree phase ambiguity determination can be skipped and the result can be directly output. If the application scenario is the polarization distortion parameter estimation or calibration of an uncalibrated SAR system, for the phase imbalance value at the position of a peak extracted, it is determined whether this peak is the target peak through 180-degree phase ambiguity determination processing. If so, the phase imbalance estimation result is directly output. Otherwise, the phase imbalance value statistically obtained for this peak is added with a 180-degree offset as the final phase imbalance estimation value. The 180-degree phase ambiguity determination method here is to determine whether this peak is the target peak by combining the rotation angle of the equivalent rotation dihedral angle inversely calculated from the wall orientation of the building reflection structure corresponding to an equivalent dihedral angle point target corresponding to this peak, so as to determine the final phase imbalance estimation value. The geometric schematic diagram of the secondary reflection structure is as Figure 3 shown, and the calculation formula for the rotation angle of the equivalent rotation dihedral angle corresponding to this structure is where α is the angle of the vertical wall relative to the horizontal projection of the satellite illumination direction, with the front being 0 degrees and counterclockwise rotation being positive. And φ is the depression angle of the satellite. Θ is the rotation angle of the equivalent rotation dihedral angle. When the co-polarized cross-polarized anti-phase hypothesis is adopted, when the calculated rotation angle is within the range of plus or minus 10 degrees centered at -22.5 degrees or +67.5 degrees, it is considered that this peak is the target peak. Otherwise, when the rotation angle is near +22.5 degrees or -67.5 degrees, it is considered that this peak is not the target peak, and the final phase imbalance result needs to be added with a 180-degree offset.

[0040] In this embodiment, the method implementation adopts the processing method of co-polarization and cross-polarization anti-phase. Therefore, by analyzing Figure 4 (c) the wall orientation of the building structure of an equivalent rotating dihedral angle strong point target corresponding to the right peak, and combining the downward viewing angle of this scene, through the above calculation formula, it is obtained that the strong point target is near -20 degrees. Combining Figure 2 (b), it conforms to the co-polarization cross-polarization anti-phase relationship, and it is determined as the target peak. Therefore, -94.5 degrees obtained by the above curve fitting statistics is the final result of the estimated phase imbalance. In this scene, the receiving-end phase imbalance result map calculated by the full-polarization calibration algorithm based on distributed targets is as shown in Figure 4 (b). By making a histogram and curve fitting statistics, the phase imbalance result corresponding to its peak is -94.5 degrees. Comparing its result with other four-polarization calibration algorithms based on forest area distributed targets, the phase imbalance result can be seen in Figure 4 (b), which is -91.1 degrees. It can be seen that the results of the two are highly consistent, and the estimated error on GF-3 is about 3 degrees, which verifies the effectiveness of the proposed method.

[0041] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0042] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for estimating SAR polarization phase imbalance based on the statistics of equivalent rotating dihedral corner targets in urban areas, characterized in that, It includes the following steps: Step 1: Extract point targets that are strong scatterers in both the co-polarized and cross-polarized channel scenes as polarized strong scatterers; Step 2: Centered on these extracted polarized strong scatterers, calculate the coherence coefficient between the cross-polarization and co-polarization for the local area, and regard the point targets with strong polarization coherence obtained by screening as equivalent rotation dihedral angle point targets; Step 3: Calculate the numerical value of the phase difference between the co-polarization and cross-polarization corresponding to the peak of each equivalent rotation dihedral angle point target as the phase imbalance estimation value of this point; Step 4: Statistically obtain the aggregation center of the phase imbalance estimation values for all equivalent rotation dihedral angle point targets, and determine the SAR polarization phase imbalance estimation value of the scene; Step 5: Combine the specific secondary reflection structure of the scene to perform 180-degree phase ambiguity determination to obtain the final SAR system phase imbalance estimation value. The specific method is as follows: For the equivalent rotation dihedral angle point target corresponding to the highest peak where the aggregation center obtained in Step 4 is located, randomly select a point target. According to the position of the point target in the scene, determine the wall orientation of its secondary reflection structure, and combine the down-view angle to calculate the rotation angle of its equivalent dihedral angle. According to the co-polarization cross-polarization phase relationship of this equivalent rotation dihedral angle point target, determine whether there is a 180-degree phase deviation in the statistically obtained phase imbalance estimation value. If not, directly output it. If so, perform a 180-degree phase shift on the result of Step 4 as the final result.

2. The SAR polarization phase imbalance estimation method based on the statistical of equivalent rotating dihedral corner point targets in urban areas according to claim 1, characterized in that, Step 1: Extract point targets that are strong scatterers in both the co-polarized and cross-polarized channel scenes as polarized strong scatterers. The specific method is as follows: Step 1.1: For dual-polarization SAR data, calculate the receiver phase imbalance using two polarization channels. For quad-polarization SAR data, calculate the receiver phase imbalance using the HH co-polarized channel and the HV cross-polarized channel, or the VV co-polarized channel and the VH cross-polarized channel. Calculate the transmitter phase imbalance using the HH co-polarized channel and the VH cross-polarized channel, or the VV co-polarized channel and the HV cross-polarized channel; Step 1.2: Calculate the average value of the amplitudes of all pixels in the co-polarization and cross-polarization channel scenes respectively, and screen out the point targets where the pixel amplitude in the cross-polarization scene is greater than or equal to K1 times the average amplitude of the cross-polarization scene, and the pixel amplitude in the co-polarization scene is greater than or equal to K2 times the average amplitude of the co-polarization scene. The obtained point targets are called polarized strong scatterers, where the numerical values of the parameter factors K1 and K2 range from [1, 10].

3. The SAR polarization phase imbalance estimation method based on the statistical of equivalent rotating dihedral corner point targets in urban areas according to claim 1, characterized in that, Step 2: Centered on these extracted polarized strong scatterers, calculate the coherence coefficient between the cross-polarization and co-polarization for the local area, and regard the point targets with strong polarization coherence obtained by screening as equivalent rotation dihedral angle point targets. The specific method is as follows: Taking the polarized strong scattering points extracted in Step 1 as the center, select local area pixels with a size of 5 pixels * 5 pixels, calculate the magnitude of the complex coherence coefficient of the corresponding area pixels in the cross-polarization channel and the co-polarization channel, and extract the polarized strong scattering point targets whose values are greater than the threshold C as equivalent rotated dihedral corner point targets, where the value range of C is [0.6, 0.9].

4. The SAR polarization phase imbalance estimation method based on the statistical of equivalent rotation dihedral corner point targets in urban areas according to claim 1, characterized in that, Step 3, calculate the numerical value of the phase difference between the co-polarization and cross-polarization corresponding to the peak value of each equivalent rotated dihedral corner point target as the phase imbalance estimation value of this point. The specific method is as follows: Extract the complex value of the co-polarization channel and the complex value of the cross-polarization channel of the peak value of the corresponding equivalent rotated dihedral corner point target pixel. Multiply the complex value of the co-polarization of this point target by the conjugate of the complex value of the cross-polarization point target, and take the phase of the result, that is, the co-polarization cross-polarization phase difference. This phase value is the phase imbalance value estimated for this point target, and normalize each of the extracted phase imbalance values to the range between -180 degrees and 180 degrees.

5. The SAR polarization phase imbalance estimation method based on the statistical of equivalent rotating dihedral corner point targets in urban areas according to claim 1, characterized in that, Step 4, statistically obtain the aggregation center of the phase imbalance estimation values for all equivalent rotated dihedral corner point targets to determine the SAR polarization phase imbalance estimation value of the scene. The specific method is as follows: For the numerous phase imbalance estimation values normalized to -180 degrees to 180 degrees calculated from all equivalent rotated dihedral corner point targets obtained in Step 3, make a statistical distribution histogram. The abscissa is the phase imbalance value of 1 degree in the range of -180 degrees to 180 degrees, and the ordinate is the number of equivalent rotated dihedral angles corresponding to each interval. Statistically analyze this phase imbalance distribution histogram. In the range of plus or minus 40 degrees of the interval where the highest peak in the histogram is located, use the center point at the top of each interval column as the fitting point to perform a normal curve fitting, and calculate the abscissa where the maximum value of the fitting curve is located to obtain the aggregation center of this phase imbalance as the phase imbalance estimation value determined for this scene.

6. A SAR polarization phase imbalance estimation system based on the statistics of equivalent rotating dihedral corner targets in urban areas, characterized in that, Based on the SAR polarization phase imbalance estimation method based on the statistics of equivalent rotated dihedral corner point targets in urban areas described in any one of claims 1-5, realize the SAR polarization phase imbalance estimation.

7. A computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, based on the SAR polarization phase imbalance estimation method based on the statistics of equivalent rotated dihedral corner point targets in urban areas described in any one of claims 1-5, realize the SAR polarization phase imbalance estimation.

8. A computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, based on the SAR polarization phase imbalance estimation method based on the statistics of equivalent rotated dihedral corner point targets in urban areas described in any one of claims 1-5, realize the SAR polarization phase imbalance estimation.

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