CTLR Compact Polarimetric SAR Data Scattering Component Decomposition Method, Device, Equipment and Medium

Abstract

The embodiment of the present invention discloses a method, device, equipment and medium for decomposing scattering components of CTLR compact polarimetric SAR data, wherein the method comprises: obtaining CTLR compact polarimetric SAR data; calling an improved m-α algorithm to decompose the scattering components of the acquired SAR data, wherein the proportion of volume scattering power in the decomposition result is f(1-m), and the proportion of surface scattering and secondary scattering power is 1-f(1-m), wherein f(1-m)<(1-m), m is a polarization degree greater than 0 and less than 1, and f(1-m) has a first extreme value of 1 at 1-m=1 and a second extreme value of 0 at 1-m=0; and storing the decomposition result of the scattering components of the SAR data. The technical solution provided by the embodiment of the present invention can be applied to the scenario of analyzing the scattering components of CTLR compact polarimetric SAR data in a synthetic aperture radar system, and can solve the problem of overestimating the proportion of scattering power of ground objects in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of synthetic aperture radar (SAR) remote sensing technology, and in particular to a method, device, equipment and medium for decomposing scattering components of CTLR compact polarimetric SAR data. Background Art

[0002] Synthetic Aperture Radar (SAR) is an active microwave imaging sensor carried on an aircraft or satellite for remote sensing observation of the ground. During the flight observation process, SAR continuously transmits electromagnetic waves to the ground through its antenna at a certain frequency, and then receives the electromagnetic wave signals scattered by the ground back to the SAR antenna. By imaging and processing the ground scattered electromagnetic wave signals received over a period of time during the flight, high-resolution microwave remote sensing images of the ground can be obtained.

[0003] SAR can be divided into single polarization, dual polarization, full polarization and other types according to the different polarization modes of transmitting and receiving electromagnetic waves. Single polarization means that both transmission and reception use horizontal polarization (H) or vertical polarization (V); dual polarization means that H or V single polarization is used for transmission, and H and V polarizations are used for simultaneous reception; full polarization means that H and V polarizations are used for alternating transmission, and H and V polarizations are used for simultaneous reception. Among them, full polarization data contains the complete electromagnetic scattering matrix of the ground object and has the largest amount of polarization information, but because H and V polarizations need to be used for alternating transmission, the observation width is limited, which is only half of that of single polarization and dual polarization. In order to have a large observation width and obtain as many electromagnetic scattering characteristics of the ground object as possible, compact polarization is proposed. Compact polarization means that circular polarization or 45° linear polarization is used for transmission, and H and V polarizations are used for simultaneous reception. In theory, compact polarization still belongs to the category of dual polarization, but because three different polarizations are used for transmission and reception, it will have certain observation advantages for some ground objects.

[0004] After the compact polarization was proposed, how to decompose its data to obtain the composition of each scattering component of the ground object has always been one of the research focuses. For the compact polarization SAR data of circular polarization transmitting H and V polarization receiving (Circular polarization Transmitting Linear polarization Receiving, CTLR), a variety of scattering component decomposition algorithms have been proposed one after another, such as: m-δ algorithm, m-χ algorithm and m-α algorithm. Among them, the m-α algorithm has the best performance among the three algorithms and is also the most widely used algorithm. The m-α algorithm decomposes the CTLR compact polarization SAR data into two scattering components. The first component corresponds to volume scattering, and the second component corresponds to surface scattering and secondary scattering. The power proportions of the two components are 1-m and m respectively, where m is the degree of polarization.

[0005] However, during the research process, it was found that the m-α algorithm has the problem of overestimating volume scattering, that is, the volume scattering power accounts for 1-m, which is too large compared to the actual situation, in the power of each scattering component of the ground object obtained by decomposing the CTLR compact polarimetric SAR data using the m-α algorithm. Summary of the invention

[0006] In view of this, the embodiments of the present invention provide a method, apparatus, device and medium for decomposing scattered components of CTLR compact polarimetric SAR data to solve the problem of overestimating the proportion of volume scattered power in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides a method for decomposing scattered components of CTLR compact polarimetric SAR data, the method comprising:

[0008] Acquire circular polarization transmission horizontal vertical polarization reception CTLR compact polarization SAR data;

[0009] Calling the improved m-α algorithm to decompose the scattering components of the acquired SAR data, the proportion of volume scattering power in the decomposition result is f(1-m), and the proportion of surface scattering and secondary scattering power is 1-f(1-m), where f(1-m)<(1-m), m is a polarization degree greater than 0 and less than 1, and f(1-m) has a first extreme value of 1 at 1-m=1, and a second extreme value of 0 at 1-m=0;

[0010] The scattering component decomposition result of the SAR data is stored.

[0011] Furthermore, f(1-m)=(1-m) n , n is a real number greater than 1.

[0012] Furthermore, the range of n is (1, 3).

[0013] Furthermore, n=2.

[0014] Further, the improved m-alpha algorithm is called to decompose the scattering components of the acquired SAR data, including:

[0015] The power value of each scattering component of the acquired SAR data is calculated according to the following formula:

[0016] P V =k1×g0×f(1-m)

[0017] P S =k2×g0(1-f(1-m))cos 2 (α)

[0018] P D =k3×g0(1-f(1-m))sin2 (α)

[0019]

[0020] in:

[0021]

[0022]

[0023] g0, g1, g2 and g3 represent four real number variables of the Stokes vector of the SAR data obtained respectively; V Represents the volume scattered power value, P S Indicates the surface scattering power value, P D Represents the secondary scattering power value, represents the phase; α is a parameter that identifies the ratio of surface scattering and secondary scattering power; arg(·) represents the phase of a complex number, and the symbol The upper symbol in the “±” corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the lower symbol corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; the adjustment coefficients k1, k2, and k3 are all greater than 1.

[0024] Furthermore, the method further comprises:

[0025] Obtain the scattering component decomposition results of real SAR data;

[0026] The stored scattering component decomposition result of the SAR data is compared with the scattering component decomposition result of the real SAR data in terms of their closeness.

[0027] Further, obtaining CTLR compact polarimetric SAR data includes: obtaining full polarimetric SAR data, and simulating and generating CTLR compact polarimetric SAR data;

[0028] Acquiring the scattering component decomposition result of the real SAR data includes: calling the full polarization decomposition algorithm to decompose the scattering component of the acquired full polarization SAR data, and using the decomposition result as the scattering component decomposition result of the real SAR data.

[0029] In a second aspect, an embodiment of the present invention provides a CTLR compact polarimetric SAR data scattering component decomposition device, the device comprising:

[0030] An acquisition unit is used to acquire circular polarization transmission horizontal vertical polarization reception CTLR compact polarization SAR data;

[0031] A decomposition unit is used to call the improved m-α algorithm to decompose the scattering components of the acquired SAR data, wherein the proportion of volume scattering power in the decomposition result is f(1-m), and the proportion of surface scattering and secondary scattering power is 1-f(1-m), wherein f(1-m)<(1-m), m is a polarization degree greater than 0 and less than 1, and f(1-m) has a first extreme value of 1 at 1-m=1, and a second extreme value of 0 at 1-m=0;

[0032] The storage unit is used to store the scattering component decomposition result of the SAR data.

[0033] Furthermore, the decomposition unit is used to call the improved m-alpha algorithm to decompose the scattering components of the acquired SAR data, including:

[0034] The power value of each scattering component of the acquired SAR data is calculated according to the following formula:

[0035] P V =k1×g0×f(1-m)

[0036] P S =k2×g0(1-f(1-m))cos 2 (α)

[0037] P D =k3×g0(1-f(1-m))sin 2 (α)

[0038]

[0039] in:

[0040]

[0041]

[0042] g0, g1, g2 and g3 represent four real number variables of the Stokes vector of the SAR data obtained respectively; V Represents the volume scattered power value, P S Indicates the surface scattering power value, P D Represents the secondary scattering power value, represents the phase; α is a parameter that identifies the ratio of surface scattering and secondary scattering power; arg(·) represents the phase of a complex number, and the symbol The upper symbol in the “±” corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the lower symbol corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; the adjustment coefficients k1, k2, and k3 are all greater than 1.

[0043] Furthermore, the acquisition unit is also used to obtain the scattering component decomposition result of the real SAR data;

[0044] The device further comprises: a comparing unit, which is used to compare the degree of proximity between the scattering component decomposition result of the SAR data stored in the storage unit and the scattering component decomposition result of the real SAR data acquired by the acquisition unit.

[0045] Further, the acquisition unit is used to acquire CTLR compact polarization SAR data, including: acquiring full polarization SAR data, and simulating and generating CTLR compact polarization SAR data;

[0046] The acquisition unit is used to acquire the scattering component decomposition result of the real SAR data, including: calling the full polarization decomposition algorithm to decompose the scattering component of the acquired full polarization SAR data, and using the decomposition result as the scattering component decomposition result of the real SAR data.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a housing, a processor, a memory, a circuit board and a power supply circuit, wherein the circuit board is placed inside a space enclosed by the housing, and the processor and the memory are arranged on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the above-mentioned electronic device; the memory is used to store executable program codes; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the aforementioned CTLR compact polarization SAR data scattering component decomposition method.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more central processing units to implement the aforementioned CTLR compact polarimetric SAR data scattering component decomposition method.

[0049] The technical solution provided by the embodiment of the present invention adopts a new method for reducing the proportion of volume scattering power. This method no longer uses a single fixed coefficient, but instead uses a function related to the polarization degree m value to dynamically adjust the reduction ratio, and has good scattering component decomposition performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1A flow chart of a CTLR compact polarimetric SAR data scattering component decomposition method provided by an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of typical area selection for China's GF-3 satellite Barnaul experiment data;

[0053] Figure 3 Schematic diagram of typical area selection for India's RISAT satellite San Francisco experiment data;

[0054] Figure 4 A schematic diagram of the structure of a CTLR compact polarimetric SAR data scattering component decomposition device provided by an embodiment of the present invention;

[0055] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The embodiment of the present invention provides a CTLR compact polarimetric SAR data scattering component decomposition method, which can be performed by a corresponding CTLR compact polarimetric SAR data scattering component decomposition device, and the device can be integrated in an electronic device with data interaction and data processing functions. Figure 1 The method specifically includes the following steps 101-103.

[0059] Step 101: Acquire CTLR compact polarimetric SAR data.

[0060] Step 102: call the improved m-alpha algorithm to decompose the scattering components of the acquired CTLR compact polarimetric SAR data.

[0061] A large number of experiments show that the volume scattering power ratio 1-m obtained by using the existing m-α algorithm to decompose CTLR compact polarization SAR data is too high. Therefore, if a new and more reasonable volume scattering power ratio is found, its value is preferably less than 1-m. In the embodiment of the present invention, the new volume scattering power ratio is a function related to 1-m, that is, f(1-m). f(1-m)<(1-m), where m is a polarization degree greater than 0 and less than 1. Through analysis, it is found that the function f(1-m) has two extreme values:

[0062] The first extreme value corresponds to the case where the object contains only volume scattering, that is, 1-m = 1. In this case, it is reasonable to assume that f(1-m) = 1:

[0063] The second extreme value corresponds to the case where the ground object contains only completely coherent scattering. At this time, the Stokes vector of the CTLR compact polarimetric SAR data is completely polarized, that is, 1-m=0. In this case, it is reasonable to assume that f(1-m)=0.

[0064] At the same time, a large number of experiments also show that for multi-view full polarization scattering of actual ground objects, in addition to the volume scattering component that will generate the depolarized partial power of the Stokes vector of the CTLR compact polarimetric SAR data, the combination of the remaining two or more surface scattering or secondary scattering components will also generate a part of the depolarized power of the Stokes vector. The size of the second part of the depolarized power is related to the size of the surface scattering and secondary scattering power. Because the proportion of surface scattering and secondary scattering power in the total power of the Stokes vector is m, it is reasonable to assume that the proportion of the second part of the depolarized power in the total depolarized power is also m; the direct inference of this assumption is that the proportion of the depolarized partial power corresponding to the volume scattering to the total depolarized power is 1-m. The ratio of the above two depolarized partial powers just corresponds to the new volume scattering component power proportion f(1-m). The new volume scattering component power proportion f(1-m) is actually equivalent to dividing the Stokes vector depolarized partial power proportion 1-m into two parts, f(1-m) and 1-mf(1-m). The first part corresponds to the volume scattering component, and the second part corresponds to the depolarized power caused by the sum of more than two surface scattering or secondary scattering components. Therefore, the power is subsequently divided according to the ratio of surface scattering and secondary scattering power determined by the α parameter.

[0065] In the specific implementation, the improved m-α algorithm is called to decompose the scattering components of the acquired CTLR compact polarimetric SAR data, including:

[0066] The power value of each scattering component of the acquired CTLR compact polarimetric SAR data is calculated according to the following formula:

[0067] P V =k1×g0×f(1-m)

[0068] P S =k2×g0(1-f(1-m))cos 2 (α)

[0069] P D =k3×g0(1-f(1-m))sin 2 (α)

[0070]

[0071] in:

[0072]

[0073]

[0074] g0, g1, g2 and g3 respectively represent four real number variables of the Stokes vector of the acquired SAR data, and the four real number variables are the basic representation forms of compact polarization data, and their physical meanings are well known to those skilled in the art; V Represents the volume scattered power value, P S Indicates the surface scattering power value, P D Represents the secondary scattering power value, represents the phase; α is a parameter that identifies the ratio of surface scattering and secondary scattering power; arg(·) represents the phase of a complex number, and the symbol The symbol above "±" corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the symbol below corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; the adjustment coefficients k1, k2, and k3 are all greater than 1, so that the corresponding scattering component power values ​​are close to the true value in absolute size. and "±", which can be specifically understood as: symbol The negative sign above corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the positive sign below corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; the positive sign above the symbol "±" corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the negative sign below corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission.

[0075] Further, based on the above two extreme values, the two endpoints of the [1-m, f(1-m)] curve can be determined to be [0,0] and [1,1]. Therefore, the specific form of f(1-m) can be set as follows in the embodiment of the present invention:

[0076] f(1-m)=(1-m) n

[0077] Where n is a real number. Because f(1-m) needs to be less than 1-m, n needs to be greater than 1. A large number of experiments have found that if the value of n is greater than 3, the proportion of volume scattering power in the decomposition result will be too low, so it can be confirmed that the reasonable value range of n is (1,3). Typically, in the embodiment of the present invention, n = 2, that is, the new volume scattering power proportion is (1-m) 2 , verified (1-m) 2The value range of is still [0,1], and it is always less than 1-m. When n=2, the adjustment coefficients k1, k2, and k3 in the above formula are all equal to 2.

[0078] The improved m-α algorithm has an important property: no polarization information is lost during the decomposition process, and its output can completely reconstruct the input. For example, when f(1-m) = (1-m) 2 When the adjustment coefficients k1, k2, and k3 are all 2, the decomposition result output can be completely restored according to the following formula to obtain the four real variables g0, g1, g2, and g3 of the Stokes vector of the CTLR compact polarimetric SAR data, the polarization degree m, and the parameter α:

[0079] g0=(P V +P S +P D ) / 2

[0080]

[0081]

[0082] g1=mg0 sin2αcosφ

[0083]

[0084] g3=±mg0cos2α

[0085] The existing m-α decomposition algorithm is as follows:

[0086] P V =(1-m)g0

[0087] P S =mg0cos 2 (α)

[0088] P D =mg0 sin 2 (α)

[0089]

[0090] There are three improvements in the improved m-α decomposition algorithm in the embodiment of the present invention. First, the most significant improvement is that the proportion of volume scattering power in its decomposition result is changed from 1-m in the original m-α decomposition to a function f(1-m) less than 1-m; then the corresponding surface scattering and secondary scattering power and proportion are changed from 1-(1-m)=m to 1-f(1-m); finally, the improved m-α algorithm multiplies the power of all scattering components by an adjustment coefficient, so that its decomposition result is also comparable to the decomposition result of full polarization data in absolute size.

[0091] Step 103: Store the scattering component decomposition result of the CTLR compact polarimetric SAR data.

[0092] On the basis of the above scheme, the CTLR compact polarimetric SAR data scattering component decomposition method provided by the embodiment of the present invention further includes the following steps:

[0093] Obtain the scattering component decomposition results of real SAR data;

[0094] The stored scattering component decomposition results of the CTLR compact polarimetric SAR data are compared with the scattering component decomposition results of the real SAR data in terms of their closeness.

[0095] By comparing the results of decomposing the scattering components of the acquired CTLR compact polarimetric SAR data using the improved m-α algorithm with the actual decomposition results, the accuracy of the improved m-α algorithm can be known. The closer the two decomposition results are, the better the compact polarimetric decomposition performance is. Conversely, the greater the difference between the two decomposition results, the worse the compact polarimetric decomposition performance is. When the difference exceeds the preset threshold, the specific form of f(1-m) can be adjusted or the adjustment coefficients k1, k2 and k3 can be modified according to the degree of comparison.

[0096] Exemplarily, the step 101 of obtaining CTLR compact polarimetric SAR data includes: obtaining full polarimetric SAR data, and simulating and generating CTLR compact polarimetric SAR data;

[0097] Accordingly, obtaining the scattering component decomposition result of the real SAR data includes: calling the full polarization decomposition algorithm to decompose the scattering component of the acquired full polarization SAR data, and using the decomposition result as the scattering component decomposition result of the real SAR data.

[0098] Below, the improved m-α algorithm is taken as an example with the following calculation formula:

[0099] P V =2g0(1-m) 2

[0100] P S =2g0(1-(1-m) 2 )cos 2 (α)

[0101] P D =2g0(1-(1-m) 2 )sin 2 (α)

[0102]

[0103]

[0104]

[0105] The specific implementation method and implementation effect of the improved m-α algorithm according to the embodiment of the present invention are described in detail with reference to two specific examples. Experiments have verified that the improved m-α algorithm has advantages in the decomposition performance of the scattering components of SAR data compared with other existing compact polarization decomposition algorithms.

[0106] Example 1

[0107] The experimental data used in this example is the C-band full polarization data obtained by China's Gaofen-3 satellite (GF-3) on May 5, 2018, observing the urban area of ​​Barnaul, Russia and its surrounding areas. The reason for using full polarization data is that various types of compact polarization data can be simulated based on full polarization data to conduct compact polarization decomposition experiments. At the same time, based on full polarization data and full polarization decomposition algorithms, the true value of the power proportion of each scattering component of the ground object can be obtained, thereby providing a basis for judging the quality of each compact polarization decomposition result. The closer the power proportion of each scattering component in the compact polarization decomposition result is to the power proportion of each scattering component in the full polarization decomposition result, the better the compact polarization decomposition performance is, and vice versa.

[0108] The specific experimental process is as follows.

[0109] (1) Generate CTLR compact polarization data from GF-3 full polarization data simulation

[0110] After the full polarization scattering matrix data is converted into polarization coherence matrix data through spatial multi-viewing and polarization filtering, the method of generating multi-view CTLR compact polarization data from the polarization coherence matrix is ​​as shown in the following formula:

[0111]

[0112] where t ij is the element of the polarization coherence matrix T, as follows

[0113]

[0114] A large number of example experiments show that the experimental results of left-hand circular polarization emission and right-hand circular polarization emission are very similar, so only the experimental results of right-hand circular polarization emission are shown in the following.

[0115] (2) Multi-algorithm compact polarization decomposition

[0116] The original m-α algorithm decomposition, Wang algorithm decomposition and the improved m-α decomposition of the present invention are used to process the multi-view CTLR compact polarization data obtained by the above simulation to obtain their respective decomposition results. Wang et al. proposed a decomposition algorithm (called Wang algorithm) to reduce the proportion of volume scattering power to 0.65 (1-m).

[0117] (3) Full polarization decomposition

[0118] The classic Freeman-Durden algorithm is used to decompose and process the full polarization data, and the decomposition result can be considered as the true value of the power of each scattering component of the ground object.

[0119] (4) Comparative analysis of the above four decomposition test results

[0120] In order to analyze and compare the results more quantitatively, three typical landforms, bare land, forest and urban area, were selected from the experimental area. Figure 2 The three white rectangular boxes A, B, and C in the figure are shown; then the average of the scattering power proportions of all pixels in the rectangular box is calculated, and the results are shown in Table 1. In Table 1, the surface scattering power proportion of the results in area A is the highest, among which the surface scattering power proportion decomposed by the original m-α algorithm is significantly lower than that of other algorithms, and the surface scattering power proportion decomposed by the improved m-α algorithm is the highest. Considering that the bare ground should be dominated by the surface scattering mechanism, the decomposition result of the improved m-α algorithm is relatively optimal. In Table 1, the volume scattering power ratio in area B is the highest, which is consistent with the physical reality that the forest area is dominated by the volume scattering mechanism; the volume scattering power ratio of the improved m-α algorithm decomposition is very close to the result of the Freeman-Durden algorithm decomposition but slightly smaller. Considering that the Freeman-Durden algorithm decomposition has the problem of overestimating the volume scattering, the improved m-α algorithm decomposition has the best result; the volume scattering power ratio of the original m-α algorithm decomposition is even higher than the result of the Freeman-Durden algorithm decomposition, which verifies that the original m-α algorithm has a more serious overestimation of the volume scattering problem; the volume scattering power ratio of the Wang algorithm decomposition is significantly lower than that of other decomposition algorithms. This poor result is unacceptable, which proves that only using the fixed coefficient of 0.65 will cause the volume scattering in the forest area to be underestimated. In Table 1, the secondary scattering ratio in area C is the highest, which is consistent with the physical reality that there should be more secondary scattering in the urban area; the improved m-α algorithm decomposition and the Wang algorithm decomposition have results that are relatively close to the Freeman-Durden decomposition, while the result of the original m-α decomposition is significantly lower than that of other decomposition algorithms.

[0121] In summary, the following conclusions can be drawn through quantitative analysis of the scattering power ratio: 1) The surface scattering power ratio and secondary scattering power ratio of the original m-α algorithm decomposition are significantly weaker than the full polarization decomposition results, while the volume scattering power ratio is too high. 2) The Wang algorithm decomposition has the problem of underestimating the volume scattering power ratio in the forest area. 3) The performance of the improved m-α algorithm decomposition is significantly better than the original m-α algorithm decomposition and the Wang algorithm decomposition, and the results are very close to the results of the Freeman-Durden algorithm decomposition.

[0122] Table 1

[0123] The average power ratio of each scattering component in three typical ground object areas in the GF-3 satellite Barnaul experimental data

[0124]

[0125]

[0126] Where Span = P S +P D +P V .

[0127] Example 2

[0128] The experimental data used in this example is the CTLR compact polarization data of actual right-hand circular polarization emission obtained by the Indian RISAT satellite observing San Francisco, USA on August 9, 2016, with an incident angle of 38.2°. After spatial multi-viewing of the data, the obtained multi-view Stokes vector image contains 1384×1089 pixels.

[0129] The original m-α algorithm decomposition, Wang algorithm decomposition and the improved m-α algorithm decomposition according to the embodiment of the present invention are respectively applied to the multi-view compressed polarization data.

[0130] Three typical landforms, ocean, forest and urban area, were selected from the experimental area. Figure 3 The three white rectangles D, E, and F in the figure are shown in the figure; then the average of the scattering power ratio of all pixels in the rectangle is calculated, and the results are shown in Table 2. Because the full polarization data of the RISAT data in this scene is unknown, the full polarization decomposition algorithm cannot be used, that is, the true value of the scattering power ratio of each typical land feature area cannot be known, so only the following analysis is performed. As shown in Table 2, the ocean in area D is mainly surface scattering, and the improved m-α algorithm decomposition has the highest surface scattering power ratio result, and the performance is relatively optimal; the forest in area E is mainly volume scattering, and the volume scattering power ratio of the Wang algorithm decomposition is the lowest, and the original m-α decomposition result is high; the urban area in area F is mainly secondary scattering, and the secondary scattering power ratio of the improved m-α decomposition is significantly better than the original m-α decomposition, but slightly lower than the Wang algorithm decomposition, but the volume scattering power ratio of the Wang algorithm decomposition is significantly higher than the improved m-α algorithm decomposition, and the performance of the two is relatively similar.

[0131] In summary, through quantitative analysis of the power proportion of the scattered components, it can be found that the conclusion is consistent with the simulation experiment using full polarization data, that is, the performance of the improved m-α algorithm decomposition is relatively optimal.

[0132] Table 2

[0133] The average power ratio of each scattering component in three typical ground feature areas in the RISAT satellite San Francisco data

[0134]

[0135] Where Span = P S +P D +P V .

[0136] Accordingly, an embodiment of the present invention further provides a CTLR compact polarimetric SAR data scattering component decomposition device, which can be implemented by software and / or hardware. Figure 4 , the device comprises:

[0137] An acquisition unit 401 is used to acquire circular polarization transmission horizontal vertical polarization reception CTLR compact polarization SAR data;

[0138] A decomposition unit 402 is used to call the improved m-α algorithm to decompose the scattering components of the acquired SAR data, wherein the proportion of volume scattering power in the decomposition result is f(1-m), and the proportion of surface scattering and secondary scattering power is 1-f(1-m), wherein f(1-m)<(1-m), m is a polarization degree greater than 0 and less than 1, and f(1-m) has a first extreme value of 1 at 1-m=1, and has a second extreme value of 0 at 1-m=0;

[0139] The storage unit 403 is used to store the scattering component decomposition result of the SAR data.

[0140] Furthermore, f(1-m)=(1-m) n , n is a real number greater than 1.

[0141] Furthermore, the range of n is (1, 3).

[0142] Furthermore, n=2.

[0143] Furthermore, the decomposition unit 402 is used to call the improved m-alpha algorithm to decompose the scattering components of the acquired SAR data, including:

[0144] The power value of each scattering component of the acquired SAR data is calculated according to the following formula:

[0145] P V =k1×g0×f(1-m)

[0146] P S =k2×g0(1-f(1-m))cos 2 (α)

[0147] P D =k3×g0(1-f(1-m))sin 2 (α)

[0148]

[0149] in:

[0150]

[0151]

[0152] g0, g1, g2 and g3 represent four real number variables of the Stokes vector of the SAR data obtained respectively; V Represents the volume scattered power value, P S Indicates the surface scattering power value, P D Represents the secondary scattering power value, represents the phase; α is a parameter that identifies the ratio of surface scattering and secondary scattering power; arg(·) represents the phase of a complex number, and the symbol The upper symbol in the “±” corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the lower symbol corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; the adjustment coefficients k1, k2, and k3 are all greater than 1.

[0153] Furthermore, the acquisition unit 401 is also used to obtain the scattering component decomposition result of the real SAR data;

[0154] The CTLR compact polarimetric SAR data scattering component decomposition device provided by the embodiment of the present invention further includes:

[0155] The comparing unit 404 is used to compare the degree of proximity between the scattering component decomposition result of the SAR data stored in the storage unit and the scattering component decomposition result of the real SAR data acquired by the acquisition unit.

[0156] Further, the acquisition unit 401 is used to acquire CTLR compact polarimetric SAR data, including:

[0157] Acquire full polarization SAR data and simulate and generate CTLR compact polarization SAR data;

[0158] The acquisition unit 401 is used to obtain the scattering component decomposition result of the real SAR data, including:

[0159] The full polarization decomposition algorithm is called to decompose the scattering components of the acquired full polarization SAR data, and the decomposition result is used as the scattering component decomposition result of the real SAR data.

[0160] The CTLR compact polarimetric SAR data scattering component decomposition device provided in this embodiment and the aforementioned CTLR compact polarimetric SAR data scattering component decomposition method embodiment belong to the same inventive concept. For technical details not described in this embodiment, reference can be made to the relevant description in the aforementioned method embodiment, which will not be repeated here.

[0161] In addition, an embodiment of the present invention further provides an electronic device. Figure 5 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention, which can implement the present invention Figure 1 The process of the embodiment shown is as follows: Figure 5 As shown, the electronic device may include: a housing 51, a processor 52, a memory 53, a circuit board 54 and a power supply circuit 55, wherein the circuit board 54 is arranged inside the space enclosed by the housing 51, and the processor 52 and the memory 53 are arranged on the circuit board 54; the power supply circuit 55 is used to supply power to various circuits or devices of the electronic device; the memory 53 is used to store executable program codes; the processor 52 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 53, so as to execute the CTLR compact polarization SAR data scattering component decomposition method described in any of the aforementioned embodiments.

[0162] For details on the specific execution process of the above steps by the processor 52 and the steps further executed by the processor 52 by running the executable program code, please refer to the present invention. Figure 1 The description of the illustrated embodiment will not be repeated here.

[0163] The electronic device may exist in various forms, including but not limited to:

[0164] (1) Mobile communication devices: These devices are characterized by having mobile communication functions and providing voice and data communications as their main purpose. These terminals include: smart phones (such as iPhones), multimedia phones, functional phones, and low-end phones.

[0165] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers and have computing and processing capabilities, and generally also have mobile Internet access features; these terminals include: PDAs, MIDs, and UMPC devices, such as the iPad;

[0166] (3) Portable entertainment devices: These devices can display and play multimedia content; such devices include: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices;

[0167] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0168] (5) Other electronic devices with data interaction and processing functions.

[0169] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more central processing units to implement the CTLR compact polarimetric SAR data scattering component decomposition method described in the above embodiment.

[0170] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0171] In the embodiments of the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0172] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0173] In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0174] For the convenience of description, the above device is described by dividing the functions into various units / modules. Of course, when implementing the present invention, the functions of each unit / module can be implemented in the same or multiple software and / or hardware.

[0175] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0176] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for decomposing scattering components of CTLR compact polarimetric SAR data, characterized in that: The method comprises: Acquire circular polarization transmission horizontal vertical polarization reception CTLR compact polarization SAR data; Calling the improved m - α The algorithm decomposes the scattering components of the acquired SAR data, and the proportion of volume scattering power in the decomposition result is f (1- m ), the surface scattering and secondary scattering power account for 1- f (1- m ),in f (1- m )<(1- m ), m is a polarizability greater than 0 and less than 1, f (1- m ) in 1- m= 1 has the first extreme value 1, at 1- m= There is a second extreme value 0 at 0; Storing the scattering component decomposition result of the SAR data; Among them, the call improved m -α algorithm, decomposing the scattering components of the acquired SAR data, including: The power value of each scattering component of the acquired SAR data is calculated according to the following formula: ； in: ； ； g 0. g 1. g 2 and g 3 respectively represent four real number variables of the Stokes vector of the SAR data obtained; P V Indicates the volume scattered power value, P S Indicates the surface scattering power value, P D Represents the secondary scattering power value, Indicates phase; α is a parameter that identifies the ratio of surface scattering to secondary scattering power; arg(·) represents the phase of a complex number, symbol" The upper symbol in " and "±" corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the lower symbol corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; adjustment coefficient , , Both are greater than 1.

2. The method according to claim 1, characterized in that , n is a real number greater than 1.

3. The method according to claim 2, characterized in that n The range is (1, 3).

4. The method according to claim 3, characterized in that n =2。 5. The method according to claim 1, characterized in that: The adjustment factor , , Both are equal to 2.

6. The method according to claim 1, characterized in that The method further comprises: Obtain the scattering component decomposition results of real SAR data; The stored scattering component decomposition result of the SAR data is compared with the scattering component decomposition result of the real SAR data in terms of their closeness.

7. The method according to claim 6, characterized in that Acquiring CTLR compact polarimetric SAR data, including: acquiring full polarimetric SAR data, and simulating and generating CTLR compact polarimetric SAR data; Acquiring the scattering component decomposition result of the real SAR data includes: calling the full polarization decomposition algorithm to decompose the scattering component of the acquired full polarization SAR data, and using the decomposition result as the scattering component decomposition result of the real SAR data.

8. A CTLR compact polarimetric SAR data scattering component decomposition device, characterized in that: The device comprises: An acquisition unit is used to acquire circular polarization transmission horizontal vertical polarization reception CTLR compact polarization SAR data; Decomposition unit, used to call the improved m - α The algorithm decomposes the scattering components of the acquired SAR data, and the proportion of volume scattering power in the decomposition result is f (1- m ), the surface scattering and secondary scattering power account for 1- f (1- m ),in f (1- m )<(1- m ), m is a polarizability greater than 0 and less than 1, f (1- m ) in 1- m= 1 has the first extreme value 1, at 1- m= There is a second extreme value 0 at 0; A storage unit, used for storing the scattering component decomposition result of the SAR data; Decomposition unit is used to call the improved m - α The algorithm decomposes the scattering components of the acquired SAR data, including: The power value of each scattering component of the acquired SAR data is calculated according to the following formula: ； in: ； ； g 0. g 1. g 2 and g 3 respectively represent four real number variables of the Stokes vector of the SAR data obtained; P V Indicates the volume scattered power value, P S Indicates the surface scattering power value, P D Represents the secondary scattering power value, Indicates phase; α is a parameter that identifies the ratio of surface scattering to secondary scattering power; arg(·) represents the phase of a complex number, symbol" The upper symbol in " and "±" corresponds to the CTLR compact polarization SAR data of left-hand circular polarization transmission, and the lower symbol corresponds to the CTLR compact polarization SAR data of right-hand circular polarization transmission; adjustment coefficient , , Both are greater than 1.

9. An electronic device, characterized in that: The electronic device comprises: a housing, a processor, a memory, a circuit board and a power supply circuit, wherein the circuit board is placed inside the space enclosed by the housing, and the processor and the memory are arranged on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the above-mentioned electronic device; the memory is used to store executable program codes; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in any one of the preceding claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more central processing units to implement the method described in any one of claims 1-7.

Images