WO2023116455A1 - 极化雷达的广义相似性度量方法、装置、设备及存储介质 - Google Patents

极化雷达的广义相似性度量方法、装置、设备及存储介质 Download PDF

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WO2023116455A1
WO2023116455A1 PCT/CN2022/137662 CN2022137662W WO2023116455A1 WO 2023116455 A1 WO2023116455 A1 WO 2023116455A1 CN 2022137662 W CN2022137662 W CN 2022137662W WO 2023116455 A1 WO2023116455 A1 WO 2023116455A1
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target
polarization
matrix
polarized
similarity
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李洪忠
孙鹭怡
韩宇
陈劲松
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Shenzhen Institute of Advanced Technology of CAS
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    • G06F18/22—Matching criteria, e.g. proximity measures
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  • the present application relates to the field of radar detection, in particular to a generalized similarity measurement method, device, equipment and storage medium for polarized radar.
  • the polarization radar obtains the target polarization matrix by measuring the transformation relationship between the incident wave and the scattered wave electric field vector or Stokes vector. For a single target, it is represented by a 2*2Sinclair scattering matrix, and for a distributed target, it is represented by a 3*3 polarization coherence matrix or polarization covariance matrix.
  • the physical scattering mechanism of the target can be inverted and geometric structure information, and classify objects.
  • Polarization similarity can measure the correlation coefficient between two polarization targets. Compared with polarization target decomposition, it does not require a perfect theoretical model, and the calculation process is simple and easy to operate. Polarization similarity can be used to measure the similarity between two independent scattering targets, and it can also be used to compare the target scattering with the normative scattering, and realize the target scattering classification according to the similarity of the two scattering.
  • the existing polarization similarity measurement methods can only measure a single situation, such as the polarization similarity measurement between single targets, or the polarization similarity between distributed targets Measurements, etc., have great limitations and cannot be applied to all polarimetric scattering targets, which reduces the applicability of polarization similarity in data processing and application of polarimetric SAR.
  • the present application provides a generalized similarity measurement method, device, equipment and storage medium for polarimetric radars, so as to solve the problems of large limitations and weak versatility of existing similarity measurement methods.
  • a technical solution adopted by the present application is to provide a generalized similarity measurement method for polarimetric radar, which includes: respectively obtaining the coherence matrices of the two polarized targets to be measured; combining the two polarized targets The coherence matrix is respectively decomposed according to the preset decomposition rules to obtain the first decomposed component of the first polarized target coherent matrix and the second decomposed component of the second polarized target coherent matrix; the first decomposed component and the second decomposed component are separately Repeatedly arrange the combinations, and calculate the polarization similarity of each combination to obtain multiple polarization similarity values; select the smallest polarization similarity value as the generalized polarization similarity measurement result of the two targets to be measured.
  • the two targets to be measured are one of a single target and a single target, a single target and a distributed target, a distributed target and a distributed target, and a distributed target and a normative scattering target.
  • the polarization target coherence matrices of two targets to be measured are obtained respectively, including: when the target to be measured is a single target, the 2 ⁇ 2 scattering matrix of the target to be measured is obtained, and the 2 ⁇ 2 scattering matrix Convert to the polarization target coherence matrix of 3 ⁇ 3; when the target to be measured is a distributed target, obtain the polarization coherence matrix or polarization covariance matrix of the target to be measured, and convert the polarization coherence matrix or polarization covariance matrix Convert to a 3 ⁇ 3 polarization target coherence matrix; when the target to be measured is a normative scattering target, obtain the normative scattering matrix of the normative scattering target, and convert the normative scattering matrix into a 3 ⁇ 3 polarized target coherence matrix.
  • the decomposition of the first polarization target coherence matrix and the second polarization target coherence matrix are expressed as:
  • T 1 is the first polarization target coherence matrix
  • T 2 is the second polarization target coherence matrix
  • det represents the determinant of the matrix
  • p i is the normalized eigenvalue of the first polarization target coherence matrix
  • q j is the normalized eigenvalue of the second polarization target coherence matrix
  • e i is the first decomposed component of the coherent matrix of the first polarized target
  • k j is the second decomposed component of the coherent matrix of the second polarized target.
  • the first decomposition component and the second decomposition component are arranged and combined without repetition, and the polarization similarity of each combination is calculated. Before obtaining multiple polarization similarity values, it also includes: The first decomposed component and the second decomposed component are de-orientated to obtain the first decomposed component and the second decomposed component whose orientation angle is 0.
  • the first decomposition component and the second decomposition component are arranged and combined without repetition, and the polarization similarity of each combination is calculated to obtain multiple polarization similarity values, including: combining two polarizations The normalized eigenvalues of the target coherence matrix, respectively calculate the single polarization similarity value between each first decomposition component and each second decomposition component; the first decomposition component and the second decomposition component are arranged and combined without repetition , and take the sum of all single polarization similarity values corresponding to each non-repeating permutation combination as the polarization similarity value of the non-repeating permutation combination.
  • s ij represents the polarization similarity value between the i-th first decomposition component and the j-th second decomposition component, Denotes the i-th first decomposition component after deorientation angle, Indicates the jth second decomposition component after deorientation.
  • a generalized similarity measurement device for polarimetric radar including: an acquisition module, which is used to respectively acquire the polarized target coherence matrices of two targets to be measured;
  • the decomposition module is used to decompose the two polarized target coherence matrices respectively according to the preset decomposition rules to obtain the first decomposed component of the first polarized target coherent matrix and the second decomposed component of the second polarized target coherent matrix;
  • the module is used to arrange and combine the first decomposition component and the second decomposition component without repetition, and calculate the polarization similarity of each combination to obtain multiple polarization similarity values;
  • the selection module is used to select the smallest polarization
  • the similarity value is taken as the generalized polarization similarity measurement result of two targets to be measured.
  • the computer device includes a processor, a memory coupled to the processor, and program instructions are stored in the memory, so When the program instructions are executed by the processor, the processor is made to execute the steps of the above-mentioned generalized similarity measurement method for polarimetric radar.
  • another technical solution adopted by the present application is to provide a storage medium storing program instructions capable of implementing the above-mentioned generalized similarity measurement method for polarimetric radar.
  • the beneficial effects of the application are: the generalized similarity measurement method of the polarization radar of the application obtains the polarization target coherence matrix of the target to be measured, decomposes the polarization target coherence matrix, obtains the decomposed components, and then divides the two poles The decomposed components of the coherence matrix of the target are combined in pairs, and the polarization similarity value of each combination is calculated, and finally the smallest polarization similarity value is selected as the generalized polarization similarity measurement result of the two targets to be measured.
  • This measurement process is no longer limited to fixed types of targets, but is applicable to targets that can convert matrix information into target coherence matrix information, which improves its versatility and is applicable to polarimetric SAR data processing and applications Stronger.
  • FIG. 1 is a schematic flow chart of a generalized similarity measurement method for a polarimetric radar according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of functional modules of a generalized similarity measurement device for a polarimetric radar according to an embodiment of the present invention
  • Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
  • first”, “second”, and “third” in this application are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, features defined as “first”, “second”, and “third” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back%) in the embodiments of the present application are only used to explain the relative positional relationship between the various components in a certain posture (as shown in the drawings) , sports conditions, etc., if the specific posture changes, the directional indication also changes accordingly.
  • FIG. 1 is a schematic flowchart of a generalized similarity measurement method for a polarimetric radar according to an embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in FIG. 1 if substantially the same result is obtained. As shown in Figure 1, the method includes steps:
  • Step S101 Obtain polarized target coherence matrices of two targets to be measured respectively.
  • the two targets to be measured are one of a single target and a single target, a single target and a distributed target, a distributed target and a distributed target, and a distributed target and a normative scattering target.
  • a single target refers to a certain target in the process of polarimetric radar detection, such as a house, a car, etc.
  • distributed target refers to all targets in a certain area in the process of polarimetric radar detection, such as all Buildings, vehicles, trees, etc.
  • normative scattering targets refer to targets detected in a specific way, in which normative scattering can be used to compare with target scattering, and target scattering classification can be realized according to the similarity between the two scattering.
  • the embodiments of the present invention use the 3 ⁇ 3 polarization coherence matrix as a unified expression of the target to be measured for calculation.
  • step S101 specifically includes:
  • the target to be measured is a single target, obtain the 2 ⁇ 2 scattering matrix of the target to be measured, and convert the 2 ⁇ 2 scattering matrix into a 3 ⁇ 3 polarization target coherence matrix.
  • the target to be measured is a distributed target
  • obtain the polarization coherence matrix or polarization covariance matrix of the target to be measured and convert the polarization coherence matrix or polarization covariance matrix into a 3 ⁇ 3 polarization target coherence matrix.
  • the target to be measured is a canonical scattering target
  • the canonical scattering matrix of the canonical scattering target is obtained, and the canonical scattering matrix is converted into a 3 ⁇ 3 polarized target coherence matrix.
  • Step S102 Decompose the two polarized target coherence matrices respectively according to preset decomposition rules to obtain a first decomposed component of the first polarized target coherent matrix and a second decomposed component of the second polarized target coherent matrix.
  • the decomposition is performed according to a preset decomposition rule, and the preset decomposition rule is preferably a Cloude-Pottier rule.
  • the polarization target coherence matrix is a matrix of 3 ⁇ 3
  • the number of its hierarchically decomposed components is three.
  • the decomposition of the first polarized target coherence matrix and the second polarized target coherent matrix are respectively expressed as:
  • T 1 is the first polarization target coherence matrix
  • T 2 is the second polarization target coherence matrix
  • det represents the determinant of the matrix
  • p i is the normalized eigenvalue of the first polarization target coherence matrix
  • q j is the normalized eigenvalue of the second polarization target coherence matrix
  • e i is the first decomposed component of the coherent matrix of the first polarized target
  • k j is the second decomposed component of the coherent matrix of the second polarized target.
  • the purpose of increasing the normalized eigenvalue of the polarization target coherence matrix is to fully reflect the proportion of each decomposition component in the original polarization target, so that the final calculated similarity measurement result has higher credibility Spend.
  • Step S103 The first decomposition component and the second decomposition component are arranged and combined without repetition, and the polarization similarity of each combination is calculated to obtain multiple polarization similarity values.
  • the decomposed components are obtained by decomposing the polarization target coherence matrix
  • the decomposed components of the two polarization target coherence matrices are combined in pairs, and then the polarization similarity of each combination is calculated respectively.
  • step S103 also includes: performing de-orientation processing on the first decomposed component and the second decomposed component, respectively, to obtain the first decomposed component and the second decomposed component whose orientation angle is 0.
  • the first decomposition component is taken as an example for illustration, and the deorientation process is as follows:
  • the first decomposed component after deorientation Expressed as:
  • step S103 specifically includes:
  • s ij represents the polarization similarity value between the i-th first decomposition component and the j-th second decomposition component, Denotes the i-th first decomposition component after deorientation angle, Indicates the jth second decomposition component after deorientation.
  • GS 1 s 11 +s 22 +s 33 ;
  • GS 2 s 11 +s 23 +s 32 ;
  • GS 3 s 12 +s 21 +s 33 ;
  • GS 4 s 13 +s 22 +s 31 ;
  • GS 5 s 13 +s 21 +s 32 ;
  • Step S1014 Select the smallest polarization similarity value as the generalized polarization similarity measurement result of the two targets to be measured.
  • GS(T 1 ,T 2 ) min 1 ⁇ i ⁇ 6 ⁇ GS i ⁇ ;
  • the generalized similarity measurement result calculated by the above method satisfies the characteristics of rotation invariance, scale invariance and finiteness that all polarization similarities should satisfy, where:
  • GS(T 1 ,T 2 ) GS(a 1 T 1 ,a 2 T 2 )
  • the generalized similarity measurement method of the polarization radar in the embodiment of the present invention is applicable to any form of polarized targets, and thus also becomes the generalized polarization similarity.
  • the generalized similarity measurement method of the polarization radar in the embodiment of the present invention obtains the polarization target coherence matrix of the target to be measured, decomposes the polarization target coherence matrix to obtain the decomposed components, and then combines the two polarization target coherence matrices The decomposed components are combined in pairs, and the polarization similarity value of each combination is calculated, and finally the smallest polarization similarity value is selected as the generalized polarization similarity measurement result of the two targets to be measured.
  • the measurement process is no longer It is limited to fixed types of targets, but is applicable to targets that can convert matrix information into target coherent matrix information, which improves its versatility and has stronger applicability in polarimetric SAR data processing and applications.
  • Fig. 2 is a schematic diagram of functional modules of a generalized similarity measurement device for a polarimetric radar according to an embodiment of the present invention.
  • the device 20 includes an acquisition module 21 , a decomposition module 22 , a calculation module 23 and a selection module 24 .
  • An acquisition module 21 configured to acquire the polarization target coherence matrices of the two targets to be measured respectively;
  • the decomposition module 22 is configured to decompose the two polarization target coherence matrices respectively according to preset decomposition rules to obtain the first decomposition component of the first polarization target coherence matrix and the second decomposition component of the second polarization target coherence matrix;
  • a calculation module 23 configured to combine the first decomposed component and the second decomposed component without repeated arrangement, and calculate the polarization similarity of each combination to obtain multiple polarization similarity values;
  • the selection module 24 is configured to select the smallest polarization similarity value as the generalized polarization similarity measurement result of two targets to be measured.
  • the two targets to be measured are one of a single target and a single target, a single target and a distributed target, a distributed target and a distributed target, and a distributed target and a normative scattering target.
  • the obtaining module 21 executes the operation of obtaining the polarized target coherence matrices of the two targets to be measured respectively, specifically including: when the target to be measured is a single target, acquiring the 2 ⁇ 2 scattering matrix of the target to be measured, and converting 2 ⁇ 2 scattering matrix is converted into 3 ⁇ 3 polarization target coherence matrix; when the target to be measured is a distributed target, obtain the polarization coherence matrix or polarization covariance matrix of the target to be measured, and convert the polarization coherence matrix or polar Transform the covariance matrix into a 3 ⁇ 3 polarized target coherence matrix; when the target to be measured is a normative scattering target, obtain the normative scattering matrix of the normative scattering target, and convert the normative scattering matrix into a 3 ⁇ 3 polarized target coherence matrix.
  • the decomposition of the first polarization target coherence matrix and the second polarization target coherence matrix are respectively expressed as:
  • T 1 is the first polarization target coherence matrix
  • T 2 is the second polarization target coherence matrix
  • det represents the determinant of the matrix
  • p i is the normalized eigenvalue of the first polarization target coherence matrix
  • q j is the normalized eigenvalue of the second polarization target coherence matrix
  • e i is the first decomposed component of the coherent matrix of the first polarized target
  • k j is the second decomposed component of the coherent matrix of the second polarized target.
  • the computing module 23 executes the non-repetitive permutation and combination of the first decomposition component and the second decomposition component, and calculates the polarization similarity of each combination to obtain multiple polarization similarity values, it is also used : De-orientation processing is performed on the first decomposed component and the second decomposed component respectively, to obtain the first decomposed component and the second decomposed component whose orientation angle is 0.
  • the computing module 23 executes the operation of combining the first decomposition component and the second decomposition component without repeated arrangement, and calculating the polarization similarity of each combination to obtain multiple polarization similarity values, specifically including: combining The normalized eigenvalues of the coherence matrices of the two polarized targets are used to calculate the single polarization similarity value between each first decomposed component and each second decomposed component respectively; the first decomposed component and the second decomposed component are Non-repetitive permutations and combinations, and the sum of all single polarization similarity values corresponding to each non-repetitive permutation combination is used as the polarization similarity value of the non-repetitive permutation combination.
  • the formula for calculating the similarity of a single polarization is:
  • s ij represents the polarization similarity value between the i-th first decomposition component and the j-th second decomposition component, Denotes the i-th first decomposition component after deorientation angle, Indicates the jth second decomposition component after deorientation.
  • FIG. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
  • the computer device 60 includes a processor 61 and a memory 62 coupled to the processor 61.
  • Program instructions are stored in the memory 62.
  • the processor 61 executes any of the above-mentioned operations. The steps of the method for measuring the generalized similarity of the polarimetric radar described in the embodiment.
  • the processor 61 may also be called a CPU (Central Processing Unit, central processing unit).
  • the processor 61 may be an integrated circuit chip with signal processing capabilities.
  • the processor 61 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components .
  • DSP digital signal processor
  • ASIC application-specific integrated circuit
  • FPGA field programmable gate array
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • FIG. 3 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
  • the storage medium in the embodiment of the present invention stores program instructions 71 capable of realizing all the above-mentioned methods, wherein the program instructions 71 can be stored in the above-mentioned storage medium in the form of software products, including several instructions to make a computer device (which can It is a personal computer, a server, or a network device, etc.) or a processor (processor) that executes all or part of the steps of the methods described in the various embodiments of the present application.
  • a computer device which can It is a personal computer, a server, or a network device, etc.
  • processor processor
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. , or computer equipment such as computers, servers, mobile phones, and tablets.
  • the disclosed computer equipment, devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components can be combined or integrated. to another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. The above is only the implementation mode of this application, and does not limit the scope of patents of this application. Any equivalent structure or equivalent process conversion made by using the contents of this application specification and drawings, or directly or indirectly used in other related technical fields, All are included in the scope of patent protection of the present application in the same way.

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Abstract

本发明公开了一种极化雷达的广义相似性度量方法、装置、设备及存储介质，其中方法包括：分别获取两个待度量目标的极化目标相干矩阵；将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相干矩阵的第二分解分量；将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值；选取最小的极化相似性值作为两个待度量目标的广义极化相似性度量结果。通过上述方式，本发明能够对多种不同类型的目标进行相似性度量，提高其通用性，在极化SAR数据处理和应用中的适用性更强。

Description

极化雷达的广义相似性度量方法、装置、设备及存储介质 技术领域
本申请涉及雷达探测领域，特别是涉及一种极化雷达的广义相似性度量方法、装置、设备及存储介质。
背景技术
雷达是进行地物探测的重要手段，随着人们对目标电磁极化散射机理认识的深入及雷达极化测量技术的发展，雷达极化研究逐渐受到关注。极化雷达通过测量入射波与散射波电场矢量或Stokes矢量间的变换关系，获得目标极化矩阵。对单目标，以2*2Sinclair散射矩阵表示，对分布式目标，以3*3极化相干矩阵或极化协方差矩阵表示，通过对这些矩阵的分析与处理，可以反演目标的物理散射机制和几何结构信息，以及对目标进行归类。
极化相似性能够度量两个极化目标之间的相关系数，相比于极化目标分解，不需要完善的理论模型，解算过程也简单易操作。极化相似性既可以用于两个独立的散射目标来度量目标之间的相似程度，也可以用于将目标散射与规范散射进行比较，根据两者散射相似程度实现目标散射分类。然而，目前存在的几种极化相似性度量方法只能进行单一情况的度量，如只能实现单目标之间的极化相似性度量，或只能实现分布式目标之间的极化相似性度量等，存在很大的局限性，不能适用于所有极化散射目标，降低了极化相似性在极化SAR数据处理和应用中的适用性。
发明内容
本申请提供一种极化雷达的广义相似性度量方法、装置、设备及存储介质，以解决现有的相似性度量方法局限性大、通用性弱的问题。
为解决上述技术问题，本申请采用的一个技术方案是：提供一种极 化雷达的广义相似性度量方法，包括：分别获取两个待度量目标的极化目标相干矩阵；将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相干矩阵的第二分解分量；将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值；选取最小的极化相似性值作为两个待度量目标的广义极化相似性度量结果。
作为本申请的进一步改进，两个待度量目标为单目标与单目标、单目标与分布式目标、分布式目标与分布式目标、分布式目标与规范散射目标中的一种。
作为本申请的进一步改进，分别获取两个待度量目标的极化目标相干矩阵，包括：当待度量目标为单目标时，获取待度量目标的2╳2散射矩阵，并将2╳2散射矩阵转换为3╳3的极化目标相干矩阵；当待度量目标为分布式目标时，获取待度量目标的极化相干矩阵或极化协方差矩阵，并将极化相干矩阵或极化协方差矩阵转换为3╳3的极化目标相干矩阵；当待度量目标为规范散射目标时，获取规范散射目标的规范散射矩阵，并将规范散射矩阵转换为3╳3的极化目标相干矩阵。
作为本申请的进一步改进，第一极化目标相干矩阵和第二极化目标相干矩阵的分解分别表示为：
Figure PCTCN2022137662-appb-000001
Figure PCTCN2022137662-appb-000002
其中，T 1为第一极化目标相干矩阵，T 2为第二极化目标相干矩阵，det表示矩阵的行列式，p i为第一极化目标相干矩阵的归一化特征值，q j为第二极化目标相干矩阵的归一化特征值，且满足：
Figure PCTCN2022137662-appb-000003
e i为第一极化目标相干矩阵的第一分解分量，k j第二极化目标相干矩阵的第二分解分量。
作为本申请的进一步改进，将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值之前，还包括：分别对第一分解分量和第二分解分量进行去取向角处理，得到取向角为0的第一分解分量和第二分解分量。
作为本申请的进一步改进，将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值，包括：结合两个极化目标相干矩阵的归一化特征值，分别计算每个第一分解分量与每个第二分解分量之间的单一极化相似性值；将第一分解分量和第二分解分量进行不重复排列组合，并将每个不重复排列组合对应的所有单一极化相似性值之和作为不重复排列组合的极化相似性值。
作为本申请的进一步改进，单一极化相似性的计算公式为：
Figure PCTCN2022137662-appb-000004
其中，s ij表示第i个第一分解分量和第j个第二分解分量之间极化相似性值，
Figure PCTCN2022137662-appb-000005
表示去取向角后的第i个第一分解分量，
Figure PCTCN2022137662-appb-000006
表示去取向角后的第j个第二分解分量。
为解决上述技术问题，本申请采用的另一个技术方案是：提供一种极化雷达的广义相似性度量装置，包括：获取模块，用于分别获取两个待度量目标的极化目标相干矩阵；分解模块，用于将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相干矩阵的第二分解分量；计算模块，用于将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值；选取模块，用于选取最小的极化相似性值作为两个待度量目标的广义极化相似性度量结果。
为解决上述技术问题，本申请采用的再一个技术方案是：提供一种计算机设备，所述计算机设备包括处理器、与所述处理器耦接的存储器，所述存储器中存储有程序指令，所述程序指令被所述处理器执行时，使得所述处理器执行上述的极化雷达的广义相似性度量方法的步骤。
为解决上述技术问题，本申请采用的再一个技术方案是：提供一种存储介质，存储有能够实现上述极化雷达的广义相似性度量方法的程序指令。
本申请的有益效果是：本申请的极化雷达的广义相似性度量方法通过获取待度量目标的极化目标相干矩阵后，对极化目标相干矩阵进行分解，得到分解分量，再将两个极化目标相干矩阵的分解分量两两进行组 合，并计算每个组合的极化相似性值，最后选取其中最小的极化相似性值作为该两个待度量目标的广义极化相似性度量结果，该度量过程不再局限于固定类型的目标之间，而是适用于能够将矩阵信息转换为目标相干矩阵信息的目标之间，提高了其通用性，在极化SAR数据处理和应用中的适用性更强。
附图说明
图1是本发明实施例的极化雷达的广义相似性度量方法的流程示意图；
图2是本发明实施例的极化雷达的广义相似性度量装置的功能模块示意图；
图3是本发明实施例的计算机设备的结构示意图；
图4是本发明实施例的存储介质的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本申请的一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请中的术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”、“第三”的特征可以明示或者隐含地包括至少一个该特征。本申请的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。本申请实施例中所有方向性指示(诸如上、下、左、右、前、后……)仅用于解释在某一特定姿态(如附图所示)下各部件之间的相对位置关系、运动情况等，如果该特定姿态发生改变时，则该方向性指示也相应地随之改变。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出 的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
图1是本发明实施例的极化雷达的广义相似性度量方法的流程示意图。需注意的是，若有实质上相同的结果，本发明的方法并不以图1所示的流程顺序为限。如图1所示，该方法包括步骤：
步骤S101：分别获取两个待度量目标的极化目标相干矩阵。
需要理解的是，两个待度量目标为单目标与单目标、单目标与分布式目标、分布式目标与分布式目标、分布式目标与规范散射目标中的一种。其中，单目标是指极化雷达探测过程中所针对的某一个目标，如一栋房屋、一辆汽车等；分布式目标是指极化雷达探测过程中所针对某一区域的所有目标，如所有楼房、车辆、树木等；规范散射目标是指以某种特定方式所探测的目标，其中规范散射可以用于与目标散射进行比较，根据两者散射相似程度实现目标散射分类。
具体地，本实施例中，为了提高该极化雷达的广义相似性度量方法的适用性，对需要进行相似性度量的目标不做限定，只需从该待度量目标处获取极化目标相干矩阵即可。需要说明的是，为了更好的进行计算，本发明实施例均以3╳3的极化相干矩阵作为待度量目标的统一表达方式进行计算。
因此，步骤S101具体包括：
1、当待度量目标为单目标时，获取待度量目标的2╳2散射矩阵，并将2╳2散射矩阵转换为3╳3的极化目标相干矩阵。
2、当待度量目标为分布式目标时，获取待度量目标的极化相干矩阵或极化协方差矩阵，并将极化相干矩阵或极化协方差矩阵转换为3╳3的极化目标相干矩阵。
3、当待度量目标为规范散射目标时，获取规范散射目标的规范散射矩阵，并将规范散射矩阵转换为3╳3的极化目标相干矩阵。
步骤S102：将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相干矩阵的第二分解分量。
具体地，在得到两个待度量目标的极化目标相干矩阵之后，根据预设分解规则进行分解，该预设分解规则优选为Cloude-Pottier规则。并且，鉴于极化目标相干矩阵为3╳3的矩阵，其分级的分解分量数量均为3个。
其中，该第一极化目标相干矩阵和第二极化目标相干矩阵的分解分别表示为：
Figure PCTCN2022137662-appb-000007
Figure PCTCN2022137662-appb-000008
其中，T 1为第一极化目标相干矩阵，T 2为第二极化目标相干矩阵，det表示矩阵的行列式，p i为第一极化目标相干矩阵的归一化特征值，q j为第二极化目标相干矩阵的归一化特征值，且满足：
Figure PCTCN2022137662-appb-000009
e i为第一极化目标相干矩阵的第一分解分量，k j第二极化目标相干矩阵的第二分解分量。
本实施例中，增加极化目标相干矩阵的归一化特征值的目的在于充分体现每个分解分量的在原极化目标中的比重，从而使得最终计算的相似性度量结果具有更高的可信度。
步骤S103：将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值。
具体地，在对极化目标相干矩阵进行分解得到分解分量之后，对两个极化目标相干矩阵的分解分量进行两两组合，再分别计算每个组合的极化相似性。
需要说明的是，在步骤S103之前，还包括：分别对第一分解分量和第二分解分量进行去取向角处理，得到取向角为0的第一分解分量和第二分解分量。
具体地，本实施例中，以第一分解分量为例进行说明，去取向处理过程如下所示：
Figure PCTCN2022137662-appb-000010
取向角
Figure PCTCN2022137662-appb-000011
通过下式计算：
Figure PCTCN2022137662-appb-000012
去取向角后的第一分解分量
Figure PCTCN2022137662-appb-000013
表示为：
Figure PCTCN2022137662-appb-000014
其中
Figure PCTCN2022137662-appb-000015
为旋转算子，
Figure PCTCN2022137662-appb-000016
进一步的，步骤S103具体包括：
1、结合两个极化目标相干矩阵的归一化特征值，分别计算每个第一分解分量与每个第二分解分量之间的单一极化相似性值。
其中，单一极化相似性的计算公式为：
Figure PCTCN2022137662-appb-000017
其中，s ij表示第i个第一分解分量和第j个第二分解分量之间极化相似性值，
Figure PCTCN2022137662-appb-000018
表示去取向角后的第i个第一分解分量，
Figure PCTCN2022137662-appb-000019
表示去取向角后的第j个第二分解分量。
2、将第一分解分量和第二分解分量进行不重复排列组合，并将每个不重复排列组合对应的所有单一极化相似性值之和作为不重复排列组合的极化相似性值。
具体地，鉴于每个极化目标相干矩阵的分解分量的数量均为3个，在进行不重复排列组合后，即可得到6中组合结果，具体如下：
1、
Figure PCTCN2022137662-appb-000020
2、
Figure PCTCN2022137662-appb-000021
3、
Figure PCTCN2022137662-appb-000022
4、
Figure PCTCN2022137662-appb-000023
5、
Figure PCTCN2022137662-appb-000024
6、
Figure PCTCN2022137662-appb-000025
因此，分别对上述6种组合方式计算对应的极化相似性，计算公式如下所示：
GS 1＝s 11+s 22+s 33；
GS 2＝s 11+s 23+s 32；
GS 3＝s 12+s 21+s 33；
GS 4＝s 13+s 22+s 31；
GS 5＝s 13+s 21+s 32；
GS 6＝s 12+s 23+s 31。
步骤S1014：选取最小的极化相似性值作为两个待度量目标的广义极化相似性度量结果。
具体地，计算两个极化目标相干矩阵之间的广义相似性GS(T 1,T 2)，计算公式如下所示：
GS(T 1,T 2)＝min 1≤i≤6{GS i}；
本实施例中，采用上述方式计算得到的广义相似性度量结果，满足所有极化相似性应当满足的旋转不变性、尺度不变性和有限性等特征，其中：
1、旋转不变性：
Figure PCTCN2022137662-appb-000026
其中
Figure PCTCN2022137662-appb-000027
是任意角度，
Figure PCTCN2022137662-appb-000028
表示T矩阵按角度
Figure PCTCN2022137662-appb-000029
进行旋转变换；
2、尺度不变性：
GS(T 1,T 2)＝GS(a 1T 1,a 2T 2)
其中a 1，a 2为任意复数。
3、有限性：
0≤GS(T 1,T 2)≤1
当且仅当存在角度
Figure PCTCN2022137662-appb-000030
使得
Figure PCTCN2022137662-appb-000031
时，GS(T 1,T 2)＝1。
因此，本发明实施例的极化雷达的广义相似性度量方法适用于任何形式的极化目标，因此也成为广义极化相似性。
本发明实施例的极化雷达的广义相似性度量方法通过获取待度量目标的极化目标相干矩阵后，对极化目标相干矩阵进行分解，得到分解分量，再将两个极化目标相干矩阵的分解分量两两进行组合，并计算每个组合的极化相似性值，最后选取其中最小的极化相似性值作为该两个待度量目标的广义极化相似性度量结果，该度量过程不再局限于固定类型的目标之间，而是适用于能够将矩阵信息转换为目标相干矩阵信息的目标之间，提高了其通用性，在极化SAR数据处理和应用中的适用性更强。
图2是本发明实施例的极化雷达的广义相似性度量装置的功能模块示意图。如图2所示，该装置20包括获取模块21、分解模块22、计算模块23和选取模块24。
获取模块21，用于分别获取两个待度量目标的极化目标相干矩阵；
分解模块22，用于将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相干矩阵的第二分解分量；
计算模块23，用于将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值；
选取模块24，用于选取最小的极化相似性值作为两个待度量目标的广义极化相似性度量结果。
可选地，两个待度量目标为单目标与单目标、单目标与分布式目标、分布式目标与分布式目标、分布式目标与规范散射目标中的一种。
可选地，获取模块21执行分别获取两个待度量目标的极化目标相干矩阵的操作，具体包括：当待度量目标为单目标时，获取待度量目标的2╳2散射矩阵，并将2╳2散射矩阵转换为3╳3的极化目标相干矩阵；当待度量目标为分布式目标时，获取待度量目标的极化相干矩阵或极化协方差矩阵，并将极化相干矩阵或极化协方差矩阵转换为3╳3的极化目标相干矩阵；当待度量目标为规范散射目标时，获取规范散射目标的规范散射矩阵，并将规范散射矩阵转换为3╳3的极化目标相干矩阵。
可选地，第一极化目标相干矩阵和第二极化目标相干矩阵的分解分别表示为：
Figure PCTCN2022137662-appb-000032
Figure PCTCN2022137662-appb-000033
其中，T 1为第一极化目标相干矩阵，T 2为第二极化目标相干矩阵，det表示矩阵的行列式，p i为第一极化目标相干矩阵的归一化特征值，q j为第二极化目标相干矩阵的归一化特征值，且满足：
Figure PCTCN2022137662-appb-000034
e i为第一极化目标相干矩阵的第一分解分量，k j第二极化目标相干矩阵的第二分解分量。
可选地，计算模块23执行将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值的操作之前，还用于：分别对第一分解分量和第二分解分量进行去取向角处理，得到取向角为0的第一分解分量和第二分解分量。
可选地，计算模块23执行将第一分解分量和第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值的操作，具体包括：结合两个极化目标相干矩阵的归一化特征值，分别计算每个第一分解分量与每个第二分解分量之间的单一极化相似性值；将第一分解分量和第二分解分量进行不重复排列组合，并将每个不重复排列组合对应的所有单一极化相似性值之和作为不重复排列组合的极化相似性值。
可选地，单一极化相似性的计算公式为：
Figure PCTCN2022137662-appb-000035
其中，s ij表示第i个第一分解分量和第j个第二分解分量之间极化相似性值，
Figure PCTCN2022137662-appb-000036
表示去取向角后的第i个第一分解分量，
Figure PCTCN2022137662-appb-000037
表示去取向角后的第j个第二分解分量。
关于上述实施例极化雷达的广义相似性度量装置中各模块实现技术方案的其他细节，可参见上述实施例中的极化雷达的广义相似性度量方法中的描述，此处不再赘述。
需要说明的是，本说明书中的各个实施例均采用递进的方式描述， 每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似的部分互相参见即可。对于装置类实施例而言，由于其与方法实施例基本相似，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
请参阅图3，图3为本发明实施例的计算机设备的结构示意图。如图3所示，该计算机设备60包括处理器61及和处理器61耦接的存储器62，存储器62中存储有程序指令，程序指令被处理器61执行时，使得处理器61执行上述任一实施例所述的极化雷达的广义相似性度量方法的步骤。
其中，处理器61还可以称为CPU(Central Processing Unit，中央处理单元)。处理器61可能是一种集成电路芯片，具有信号的处理能力。处理器61还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
参阅图3，图3为本发明实施例的存储介质的结构示意图。本发明实施例的存储介质存储有能够实现上述所有方法的程序指令71，其中，该程序指令71可以以软件产品的形式存储在上述存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本申请各个实施方式所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质，或者是计算机、服务器、手机、平板等计算机设备设备。
在本申请所提供的几个实施例中，应该理解到，所揭露的计算机设备，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所 显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。以上仅为本申请的实施方式，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (10)

  1. 一种极化雷达的广义相似性度量方法，其特征在于，包括：
    分别获取两个待度量目标的极化目标相干矩阵；
    将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相干矩阵的第二分解分量；
    将所述第一分解分量和所述第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值；
    选取最小的极化相似性值作为所述两个待度量目标的广义极化相似性度量结果。
  2. 根据权利要求1所述的极化雷达的广义相似性度量方法，其特征在于，所述两个待度量目标为单目标与单目标、单目标与分布式目标、分布式目标与分布式目标、分布式目标与规范散射目标中的一种。
  3. 根据权利要求2所述的极化雷达的广义相似性度量方法，其特征在于，所述分别获取两个待度量目标的极化目标相干矩阵，包括：
    当所述待度量目标为所述单目标时，获取所述待度量目标的2╳2散射矩阵，并将所述2╳2散射矩阵转换为3╳3的极化目标相干矩阵；
    当所述待度量目标为所述分布式目标时，获取所述待度量目标的极化相干矩阵或极化协方差矩阵，并将所述极化相干矩阵或极化协方差矩阵转换为3╳3的极化目标相干矩阵；
    当所述待度量目标为所述规范散射目标时，获取所述规范散射目标的规范散射矩阵，并将所述规范散射矩阵转换为3╳3的极化目标相干矩阵。
  4. 根据权利要求1所述的极化雷达的广义相似性度量方法，其特征在于，所述第一极化目标相干矩阵和所述第二极化目标相干矩阵的分解分别表示为：
    Figure PCTCN2022137662-appb-100001
    Figure PCTCN2022137662-appb-100002
    其中，T 1为所述第一极化目标相干矩阵，T 2为所述第二极化目标相干矩阵，det表示矩阵的行列式，p i为所述第一极化目标相干矩阵的归一化特征值，q j为所述第二极化目标相干矩阵的归一化特征值，且满足：
    Figure PCTCN2022137662-appb-100003
    e i为所述第一极化目标相干矩阵的第一分解分量，k j所述第二极化目标相干矩阵的第二分解分量。
  5. 根据权利要求4所述的极化雷达的广义相似性度量方法，其特征在于，所述将所述第一分解分量和所述第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值之前，还包括：
    分别对所述第一分解分量和所述第二分解分量进行去取向角处理，得到取向角为0的第一分解分量和第二分解分量。
  6. 根据权利要求5所述的极化雷达的广义相似性度量方法，其特征在于，所述将所述第一分解分量和所述第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值，包括：
    结合两个极化目标相干矩阵的归一化特征值，分别计算每个第一分解分量与每个第二分解分量之间的单一极化相似性值；
    将所述第一分解分量和所述第二分解分量进行不重复排列组合，并将每个不重复排列组合对应的所有单一极化相似性值之和作为所述不重复排列组合的极化相似性值。
  7. 根据权利要求6所述的极化雷达的广义相似性度量方法，其特征在于，所述单一极化相似性的计算公式为：
    Figure PCTCN2022137662-appb-100004
    其中，s ij表示第i个第一分解分量和第j个第二分解分量之间极化相似性值，
    Figure PCTCN2022137662-appb-100005
    表示去取向角后的第i个第一分解分量，
    Figure PCTCN2022137662-appb-100006
    表示去取向角后的第j个第二分解分量。
  8. 一种极化雷达的广义相似性度量装置，其特征在于，包括：
    获取模块，用于分别获取两个待度量目标的极化目标相干矩阵；
    分解模块，用于将两个极化目标相干矩阵分别按照预设分解规则进行分解，得到第一极化目标相干矩阵的第一分解分量和第二极化目标相 干矩阵的第二分解分量；
    计算模块，用于将所述第一分解分量和所述第二分解分量进行不重复排列组合，并计算每个组合的极化相似性，得到多个极化相似性值；
    选取模块，用于选取最小的极化相似性值作为所述两个待度量目标的广义极化相似性度量结果。
  9. 一种计算机设备，其特征在于，所述计算机设备包括处理器、与所述处理器耦接的存储器，所述存储器中存储有程序指令，所述程序指令被所述处理器执行时，使得所述处理器执行如权利要求1-7中任一项权利要求所述的极化雷达的广义相似性度量方法的步骤。
  10. 一种存储介质，其特征在于，存储有能够实现如权利要求1-7中任一项所述的极化雷达的广义相似性度量方法的程序指令。
PCT/CN2022/137662 2021-12-20 2022-12-08 极化雷达的广义相似性度量方法、装置、设备及存储介质 Ceased WO2023116455A1 (zh)

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