CN111025291A - Ocean oil spill detection method based on new characteristics of fully-polarized SAR - Google Patents

Ocean oil spill detection method based on new characteristics of fully-polarized SAR Download PDF

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CN111025291A
CN111025291A CN201911076780.7A CN201911076780A CN111025291A CN 111025291 A CN111025291 A CN 111025291A CN 201911076780 A CN201911076780 A CN 201911076780A CN 111025291 A CN111025291 A CN 111025291A
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宋冬梅
任慧敏
王斌
甄宗晋
崔建勇
许明明
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China University of Petroleum East China
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Abstract

The invention discloses a marine oil spill detection method based on new characteristics of a fully-polarized SAR, which comprises the following steps: extracting a polarization coherent matrix; eigenvalue and eigenvector decomposition; constructing new characteristics of the kini coefficient; dividing a threshold value; the method of the invention provides a new polarization characteristic based on the eigenvalue decomposition theory, which is called polarization damping coefficient; the characteristic can reflect the polarization state among different targets in the set, can describe the purity of different scattering types in statistical sense, and indicates that the scattering mechanism types in the corresponding set are consistent when the purity is 0; the new characteristics obtain different scattering mechanism types through a target decomposition theory, oil spill information is extracted through calculating the impurity degree of the dominant scattering mechanism of the pixel in the statistical sense, the characteristics are extracted through the method for the fully polarized SAR oil spill image, the problem that a biological oil film and a mineral film cannot be distinguished is solved, and the new characteristic extraction technology has the capability of inhibiting coherent speckle noise.

Description

Ocean oil spill detection method based on new characteristics of fully-polarized SAR
Technical Field
The invention relates to the technical field of marine oil spill detection, in particular to a marine oil spill detection method based on new characteristics of a fully-polarized SAR.
Background
Offshore oil spill is one of the main factors causing pollution damage to the ocean environment, the ocean oil pollution caused by pipeline rupture, oil tanker collision and drilling platform explosion is a global concern, and since 1970, the oil pollution generated every year all over the world exceeds 10000 tons, so that how to effectively detect the offshore oil spill is very important. After the offshore oil spill occurs, the position, the oil spill amount and the diffusion trend of the occurrence are generally known, and in an already-put monitoring system, satellite remote sensing is one of the most important and effective means, and plays an increasingly important role in offshore oil spill monitoring and emergency response. At present, the most commonly used satellite remote sensing sensors include visible light, infrared, ultraviolet, laser fluorescence, microwave sensors and the like, wherein the sensors are widely applied by the characteristics that a Synthetic Aperture Radar (SAR) can penetrate through clouds and fog and is not influenced by day and night;
oil slick usually appears as dark spots in SAR images due to the main mechanism of SAR sea surface imaging. When an oil film exists on the sea surface, capillary waves and short gravity waves on the sea surface are continuously subjected to a damping effect, the roughness of the sea surface is reduced, and therefore radar backscatter echo signals are weakened. Generally, conventional single-polarized oil spill detection can be divided into the following three steps: (1) dark spot detection: for example, identification of a region of interest (ROI); (2) feature extraction: in order to further distinguish the composition of dark spots, it is necessary to extract various features of the image after dark spot detection, and to pick appropriate features for the subsequent classification task; (3) and (4) classification: the phenomenon of misclassification of an oil film and an oil-like film is reduced;
with the development of the polarization mode of the SAR from single polarization to dual polarization and full polarization, a new oil spill detection research based on polarized SAR data is carried out, compared with single polarization, a full polarization SAR image not only contains intensity information, but also contains phase information, so that the full polarization scattering characteristic of a sea surface target can be effectively obtained, and the geometric shape and the physical characteristic of the sea surface target can be more comprehensively reflected;
in the existing polarization characteristic extraction technology, oil films and seawater can be well distinguished, but for a full polarization SAR oil spill image, the image is not only oil films and seawater, but also comprises oil film phenomena such as a low wind speed area, a rainball, a biological oil film and the like, particularly, the distinction of the biological oil film and a mineral oil film is more and more emphasized in recent years, and the existing characteristic extraction technology cannot well distinguish the biological oil film and the mineral oil film and is easily influenced by sea coherent speckle noise, so that the invention provides a marine oil spill detection method based on a new characteristic of the full polarization SAR to overcome the defects in the prior art.
Disclosure of Invention
Aiming at the problems, the invention provides a marine oil spill detection method based on a new fully-polarized SAR characteristic, a new polarization characteristic is provided based on a characteristic value decomposition theory, the polarization characteristic is called polarization damping coefficient, the characteristic can reflect the polarization state among different targets in a set and can describe the purity of different scattering types in a statistical sense, the new characteristic obtains different scattering mechanism types through a target decomposition theory, oil spill information is extracted by calculating the impurity degree of a dominant scattering mechanism of a pixel in the statistical sense, the problem that a biological oil film and a mineral film cannot be distinguished is solved, and a new characteristic extraction technology has the capability of inhibiting coherent speckle noise.
The invention provides a marine oil spill detection method based on new characteristics of a fully-polarized SAR (synthetic aperture radar), which comprises the following steps of:
the method comprises the following steps: extracting a polarized coherence matrix comprising an original scattering matrix S and a coherence matrix T3The polarized scattering matrix S can completely describe the electromagnetic scattering characteristics of the SAR target, and is defined asThe coherence matrix T is shown in equation (1)3Generated by the outer product of the target vector k and the conjugate transpose vector of the target vector k, and a coherent matrix T3The definition is shown in formula (2);
Figure BDA0002262717320000031
Figure BDA0002262717320000032
in the formula (2), T represents the conjugation transposition and conjugation respectively, and < > represents the overall average value;
step two: eigenvalue and eigenvector decomposition. Based on the target decomposition theory, 3 multiplied by 3 Hermite average coherent matrix T3The eigenvalues and eigenvectors obtained from the calculation produce a diagonalized version of the coherence matrix, coherence matrix T3Is defined as shown in formula (3):
Figure BDA0002262717320000033
where Σ is a 3 × 3 non-negative real diagonal matrix, eigenvalue (λ)1≥λ2≥λ3≥0)；U3＝[e1e2e3]Is a 3 x 3 special unitary matrix SU (3) where e1,e2,e3Are respectively a coherence matrix T3Of the feature vector of (1), then the coherence matrix T3Redefined, the new definition is shown in formula (4);
Figure BDA0002262717320000034
step three: and constructing new characteristics of the kini coefficient. And (3) defining a polarization-kini coefficient based on the eigenvalue and eigenvector decomposition in the step two as shown in a formula (5):
Figure BDA0002262717320000041
wherein，piCorresponding to the characteristic value lambdaiThe obtained pseudo probability satisfies
Figure BDA0002262717320000042
p1+p2+p3＝1，λ1≥λ2≥λ3The characteristic value is rotation invariant, and the polarization-based damping coefficient also has rotation invariance;
step four: and (5) carrying out threshold segmentation. Different scattering mechanism types and corresponding scattering mechanisms are obtained through a target decomposition theory, and the impure degree and the ordered degree of the different scattering mechanisms in the statistical sense are judged according to the new characteristics of the Gini coefficient to extract the oil spill information.
The further improvement lies in that: s in the formula (1)HVRepresenting horizontally polarized transmission, vertically polarized reception; sVHRepresenting vertical polarization transmission, horizontal polarization reception; sHHRepresenting horizontally polarized transmission and reception; sVVRepresenting vertically polarized transmission and reception.
The further improvement lies in that: when the reciprocity theorem is satisfied in the formula (1), SHV＝SVHAnd obtaining a target scattering vector k by using a Pauli base decomposition method for the polarized scattering matrix S, wherein the definition of the polarized scattering matrix S is shown as a formula (6):
Figure BDA0002262717320000043
wherein the factor
Figure BDA0002262717320000044
For ensuring that the norm of the target vector is equal to the total scattered power.
The further improvement lies in that: diagonal elements of the scattering matrix S in the first step represent the relationship between an incident field and a scattering field in the same polarization mode, and are represented by a 'same polarization' item; the off-diagonal elements represent the incident angle and scattered field relationship for the orthogonal polarization mode, represented by the term "cross polarization".
The further improvement lies in that: solving the feature vector e in the second step1,e2,e3Then, 3 mutually independent targets are obtained for constructing a statistical model, and a coherent matrix T is obtained3Expanding the mixture into the sum of 3 mutually independent targets, wherein each target corresponds to a determined scattering mechanism and then is represented by an equivalent simple scattering matrix, and the weight of a component i of the determined scattering mechanism in the whole scattering process is represented by a characteristic value lambdaiDescription, type of scattering mechanism and normalized feature vector eiAnd (4) correlating.
The further improvement lies in that: in the formula (5) in the third step, when the characteristic value has only one non-zero value (lambda)1≠0,λ2＝λ30), then the coherence matrix T3The scattering situation of the point object defined by the polarization scattering matrix S is degraded.
The further improvement lies in that: in the formula (5) in the third step, when all the characteristic values are not zero and are equal (lambda)1＝λ2＝λ3Not equal to 0), the coherence matrix T is obtained3It represents a completely depolarized random scattering state, and the object between these two extremes is partially polarized, when the coherence matrix T is present3Are greater than 1 and are not completely equal to each other.
The further improvement lies in that: in the fourth step, when the impurity degree is lower, the order degree of a corresponding scattering mechanism is higher, and the polarization coefficient is smaller; when the impurity degree is higher, the order degree of a corresponding scattering mechanism is lower, and the polarization coefficient is higher; when the impurity degree reaches the minimum value, the scattering mechanisms in the corresponding set are consistent in type; when the impurity reaches a maximum, it indicates that the scattering of the target in the set exhibits a random noise state.
The further improvement lies in that: from the perspective of the polarization state in the fourth step, when the value of the polarization kini coefficient is lower, the system can be regarded as weakly depolarized, the dominant scattering mechanism is regarded as a target scattering mechanism of a specific equivalent point, the feature vector corresponding to the maximum feature value is selected according to the mechanism, and other feature vectors are ignored; when the value of the polarization kiney coefficient is higher, the average scatterer in the set is in a depolarization state, and no single scattering target exists any more, so that the mixing proportion of all possible point target scattering types from the whole characteristic value distribution spectrum needs to be considered; when the polarization coefficient is further increased, the number of scattering mechanisms identified from the polarization measurement data will also gradually decrease, and when the polarization coefficient reaches a maximum value, the polarization information will be zero, and then the target scattering is completely a random noise process.
The invention has the beneficial effects that: the method provides a new polarization characteristic based on a characteristic value decomposition theory, wherein the polarization characteristic is called polarization prime coefficient, the characteristic can reflect polarization states among different targets in a set and can describe the impure degree of different scattering types in a statistical sense, when the impure degree is 0, the scattering mechanism types in the corresponding set are consistent, the new characteristic obtains the different scattering mechanism types through the target decomposition theory, and the oil spill information is extracted through calculating the impure degree of a dominant scattering mechanism of the pixel in the statistical sense; the method is used for extracting the characteristics of the fully polarized SAR oil spill image, the problem that a traditional method cannot effectively distinguish a biological oil film from a mineral film is well solved, and the new characteristic extraction technology has the capability of inhibiting speckle noise of the SAR image.
Drawings
FIG. 1 is a schematic diagram of a new polarization feature extraction process based on eigenvalue decomposition according to the present invention;
FIG. 2 is a diagram illustrating the extraction of new polarization features based on eigenvalue decomposition according to the present invention;
FIG. 3 is a schematic diagram of a decomposed feature image of a coherence matrix according to the present invention;
FIG. 4 is a schematic diagram of the polar Keyny coefficient of the new feature map extracted by the method of the present invention;
FIG. 5 is a diagram illustrating the threshold segmentation of the new characteristic polarization Boyni coefficient of the present invention;
FIG. 6 shows the polarization Boyny coefficient and span,
Figure BDA0002262717320000061
H. P, A, CPD, Rho feature extraction results comparison diagram;
FIG. 7 shows an embodiment of the present inventionMedium polarization coefficient and span,
Figure BDA0002262717320000062
H. The evaluation results of the probability density curves of P, A, CPD and Rho are compared with a schematic diagram;
FIG. 8 shows the polarization Boyny coefficient and span,
Figure BDA0002262717320000071
H. The evaluation results of the probability density curves of P, A, CPD and Rho are compared with a schematic diagram;
FIG. 9 shows the polarization Boyny coefficient and span,
Figure BDA0002262717320000072
H. The evaluation results of the probability density curves of P, A, CPD and Rho are compared with a schematic diagram;
FIG. 10 shows the polarization Boyny coefficient and span,
Figure BDA0002262717320000073
H. The evaluation results of the probability density curves of P, A, CPD and Rho are shown in the comparison chart.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
According to fig. 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, the present embodiment provides a method for detecting marine oil spill based on a new characteristic of a fully-polarized SAR, including the following steps:
the method comprises the following steps: extracting a polarized coherence matrix comprising an original scattering matrix S and a coherence matrix T3The polarized scattering matrix S can completely describe the electromagnetic scattering characteristics of the SAR target and is defined as formula (1), and the coherence matrix T3Generated by the outer product of the target vector k and the conjugate transpose vector of the target vector k, and a coherent matrix T3The definition is shown in formula (2);
Figure BDA0002262717320000074
Figure BDA0002262717320000075
in the formula (1), SHVRepresenting horizontally polarized transmission, vertically polarized reception; sVHRepresenting vertical polarization transmission, horizontal polarization reception; sHHRepresenting horizontally polarized transmission and reception; sVVRepresenting vertically polarized transmission and reception;
in the formula (2), T represents the conjugation transposition and conjugation respectively, and < > represents the overall average value;
when the reciprocity theorem is satisfied, SHV＝SVHAnd obtaining a target scattering vector k by using a Pauli base decomposition method for the polarized scattering matrix S, wherein the definition of the polarized scattering matrix S is shown as a formula (6):
Figure BDA0002262717320000081
wherein the factor
Figure BDA0002262717320000082
The system is used for ensuring that the norm of the target vector is equal to the total scattering power;
diagonal elements of the scattering matrix S represent the relationship between an incident field and a scattering field in the same polarization mode and are represented by a 'homopolarization' item; the off-diagonal element represents the relationship between the incident angle and the scattered field in an orthogonal polarization mode and is represented by a cross polarization term;
step two: decomposing the eigenvalue and eigenvector, and based on the target decomposition theory, dividing the 3X 3 Hermite flat phase coherence matrix T3The eigenvalues and eigenvectors obtained from the calculation produce a diagonalized version of the coherence matrix, coherence matrix T3As shown in equation (3):
Figure BDA0002262717320000083
where Σ is 3 × 3 negationNegative real diagonal array, U3Is a 3 x 3 special unitary matrix SU (3) if λ1,λ2,λ3Respectively representing a coherence matrix T3Is not a negative characteristic value, and λ1≥λ2≥λ3≥0；e1,e2,e3Respectively representing a coherence matrix T3The eigenvector of (1), the coherence matrix T3Further development is possible as shown in equation (4):
Figure BDA0002262717320000084
solving for the feature vector e1,e2,e3Then, 3 mutually independent targets are obtained for constructing a statistical model, and a coherent matrix T is obtained3Expanding the mixture into the sum of 3 mutually independent targets, wherein each target corresponds to a determined scattering mechanism and then is represented by an equivalent simple scattering matrix, and the weight of a component i of the determined scattering mechanism in the whole scattering process is represented by a characteristic value lambdaiDescription, type of scattering mechanism and normalized feature vector eiIn this embodiment, the decomposed picture is shown in fig. 3;
step three: and constructing new characteristics of the kini coefficient. And (3) defining a polarization-kini coefficient based on the eigenvalue and eigenvector decomposition in the step two as shown in a formula (5):
Figure BDA0002262717320000091
wherein p isiCorresponding to the characteristic value lambdaiThe obtained pseudo probability satisfies
Figure BDA0002262717320000092
And p is1+p2+p3＝1，λ1≥λ2≥λ3The characteristic value is rotation invariant, and the polarization-based damping coefficient also has rotation invariance;
when the characteristic value has only one non-zero value (lambda)1≠0,λ2＝λ30), then the coherence matrixT3Degenerates to the scattering behavior of the point object defined by the polarization scattering matrix S; when all the characteristic values are not zero and are equal (lambda)1＝λ2＝λ3Not equal to 0), the coherence matrix T is obtained3It represents a completely depolarized random scattering state, and the object between these two extremes is partially polarized, when the coherence matrix T is present3Are greater than 1 and are not completely equal to each other;
step four: and (4) threshold segmentation, obtaining different scattering mechanism types and corresponding scattering mechanisms through a target decomposition theory, and judging the impure degree and the ordered degree of the different scattering mechanisms in the statistical sense according to the new characteristics of the Gini coefficient to extract the oil spill information.
When the impurity degree is lower, the order degree of a corresponding scattering mechanism is higher, and the polarization coefficient is smaller; when the impurity degree is higher, the order degree of a corresponding scattering mechanism is lower, and the polarization coefficient is higher; when the impurity degree reaches the minimum value, the scattering mechanisms in the corresponding set are consistent in type; when the impurity degree reaches the maximum value, indicating that the scattering of the targets in the set presents a random noise state;
from the perspective of the polarization state, when the value of the polarization kini coefficient is low, the system can be regarded as weakly depolarized, the dominant scattering mechanism is regarded as a target scattering mechanism of a specific equivalent point, and the eigenvector corresponding to the maximum eigenvalue is selected according to the mechanism, while other eigenvectors are ignored; when the value of the polarization kiney coefficient is higher, the average scatterer in the set is in a depolarization state, and no single scattering target exists any more, so that the mixing proportion of all possible point target scattering types from the whole characteristic value distribution spectrum needs to be considered; when the polarization coefficient is further increased, the number of scattering mechanisms identified from the polarization measurement data will also gradually decrease, and when the polarization coefficient reaches a maximum value, the polarization information will be zero, and then the target scattering is completely a random noise process.
In the image of this embodiment, bragg scattering is dominant in clean seawater, and the scattering mechanism is single, and at this time, the coherent matrix T is3Is one greaterSo that the polarization-damping coefficient is small; the scattering mechanisms of mineral oils are complex and the number of scattering mechanisms that can be identified from the measurement data is small, when the coherence matrix T is present3Three approximately equal characteristic values exist, so that the polarization coefficient of the floating oil is larger than that of the clean seawater; besides the dominant scattering, the bio-oil film also comprises other complex target scattering mechanisms, such as mirror scattering and the like, and Bragg scattering is not dominant, so that the coherence matrix T is3There are two to three characteristic values that are not equal to each other, so the polarization coefficient should be between that of mineral oil and seawater. In general, the order of the big to the small of the Giny coefficient is mineral oil>Biological oil film>Seawater, the polarization damping coefficient characteristic extracted by the method of the invention is shown in fig. 4, mineral oil, a biological oil film and seawater can be clearly seen to show completely different distribution intervals on the polarization damping coefficient characteristic image, the polarization damping coefficient of the mineral oil is between 0.45 and 0.68, the polarization damping coefficient of the biological oil film is between 0.23 and 0.32, the polarization damping coefficient of the seawater is near 0.1, the polarization damping coefficient characteristic image is subjected to threshold segmentation by using an OTSU algorithm, the segmented image is shown in fig. 5, the mineral oil coverage area is well segmented, and the biological oil film and the seawater are combined into one class.
Using the process of the present invention and H, A,
Figure BDA0002262717320000111
Comparing the polarization feature extraction results obtained by 7 other classical methods such as span, P, CPD and Rho, and qualitatively and quantitatively evaluating the feature extraction results by using a probability density curve, image contrast and local standard deviation, wherein FIGS. 7, 8 and 9 are qualitative evaluation results of the probability density curve, Table 1 is quantitative evaluation results of the image contrast, and Table 2 is quantitative evaluation results of the local standard deviation.
TABLE 1 evaluation of image contrast of crude oil and vegetable oil under different feature extraction methods
Figure BDA0002262717320000112
TABLE 2 local standard deviation evaluation of crude and vegetable oils under different feature extraction methods
Figure BDA0002262717320000121
The probability density curve can show the capability of each feature for distinguishing a bio-oil film (simulated by vegetable oil in the embodiment) from a mineral oil film, and in the probability density curves with different features, the longer the distance between the peaks of the curves of the bio-oil film and the mineral film is, the better the capability of the feature for distinguishing the bio-oil film from the mineral film is; the image contrast can measure the capability of the polarization characteristic to distinguish the information of the bio-oil film and the mineral oil, and if the contrast between the bio-oil film and the mineral film of a certain characteristic is higher, the characteristic is more beneficial to extracting the mineral oil film on the sea surface, namely the characteristic is more suitable for oil spill detection; the local standard deviation is used for measuring the speckle noise suppression capability of the extraction method of different polarization characteristics on the complete polarization SAR image, if the local standard deviation value is smaller, the noise of the image is smaller, and the characteristic is proved to have stronger noise suppression performance.
From qualitative angle evaluation, it can be visually seen in fig. 6 that the feature extraction result of the method is visually superior to most existing methods, after the polarization kiney coefficient is extracted, the bio-oil film and the mineral oil film have obvious difference, and from the probability density curves of fig. 7, 8, 9 and 10, it can be seen that the polarization kiney coefficients, H, span and P all show better separability;
from the quantitative evaluation, it can be seen from tables 1 and 2 that the image contrast and the local standard deviation of the polarization-damping coefficient exceed all the other features, which indicates that the polarization-damping coefficient can more effectively extract the oil spill information for other features, and therefore, the method is better than other polarization-feature extraction methods in oil spill and seawater separability and image noise suppression.
The method provides a new polarization characteristic based on a characteristic value decomposition theory, wherein the polarization characteristic is called polarization prime coefficient, the characteristic can reflect polarization states among different targets in a set and can describe the impure degree of different scattering types in a statistical sense, when the impure degree is 0, the scattering mechanism types in the corresponding set are consistent, the new characteristic obtains the different scattering mechanism types through the target decomposition theory, and the oil spill information is extracted through calculating the impure degree of a dominant scattering mechanism of the pixel in the statistical sense; the method is used for extracting the characteristics of the fully polarized SAR oil spill image, the problem that a traditional method cannot effectively distinguish a biological oil film from a mineral film is well solved, and the new characteristic extraction technology has the capability of inhibiting speckle noise of the SAR image.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. A marine oil spill detection method based on new characteristics of a fully-polarized SAR is characterized by comprising the following steps:
the method comprises the following steps: extracting a polarized coherence matrix comprising an original scattering matrix S and a coherence matrix T3The extraction of (1). The polarized scattering matrix S can completely describe the electromagnetic scattering characteristics of the SAR target and is defined as the formula (1), and the coherence matrix T3Generated by the outer product of the target vector k and the conjugate transpose vector of the target vector k, and a coherent matrix T3The definition is shown in formula (2);
Figure FDA0002262717310000011
Figure FDA0002262717310000012
in the formula (2), T represents the conjugation transposition and conjugation respectively, and < > represents the overall average value;
step two: eigenvalue and eigenvector decomposition. Based on the target decomposition theory, 3 multiplied by 3 Hermite average coherent matrix T3The eigenvalues and eigenvectors obtained from the calculation produce a diagonalized version of the coherence matrix, coherence matrix T3Is defined as shown in formula (3):
Figure FDA0002262717310000013
where Σ is a 3 × 3 non-negative real diagonal matrix, U3Is a 3 x 3 special unitary matrix SU (3) if λ1,λ2,λ3Respectively representing a coherence matrix T3Is not a negative characteristic value, and λ1≥λ2≥λ3≥0；e1,e2,e3Respectively representing a coherence matrix T3The eigenvector of (1), the coherence matrix T3Further development is possible as shown in equation (4):
Figure FDA0002262717310000014
step three: and constructing new characteristics of the kini coefficient. And (3) defining a polarization-kini coefficient based on the eigenvalue and eigenvector decomposition in the step two as shown in a formula (5):
Figure FDA0002262717310000021
wherein p isiCorresponding to the characteristic value lambdaiThe obtained pseudo probability satisfies
Figure FDA0002262717310000022
p1+p2+p3＝1，λ1≥λ2≥λ3The characteristic value is rotation invariant, and the polarization-based damping coefficient also has rotation invariance;
step four: and (5) carrying out threshold segmentation. Different scattering mechanism types and corresponding scattering mechanisms are obtained through a target decomposition theory, and the impure degree and the ordered degree of the different scattering mechanisms in the statistical sense are judged according to the new characteristics of the Gini coefficient to extract the oil spill information.
2. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: s in the formula (1)HVRepresenting horizontally polarized transmission, vertically polarized reception; sVHRepresenting vertical polarization transmission, horizontal polarization reception; sHHRepresenting horizontally polarized transmission and reception; sVVRepresenting vertically polarized transmission and reception.
3. The marine oil spill detection method based on the new fully-polarized SAR characteristics as claimed in claim 2, characterized in that: when the reciprocity theorem is satisfied in the formula (1), SHV＝SVHAnd obtaining a target scattering vector k by using a Pauli base decomposition method for the polarized scattering matrix S, wherein the definition of the polarized scattering matrix S is shown as a formula (6):
Figure FDA0002262717310000023
wherein the factor
Figure FDA0002262717310000024
For ensuring that the norm of the target vector is equal to the total scattered power.
4. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: diagonal elements of the scattering matrix S in the first step represent the relationship between an incident field and a scattering field in the same polarization mode, and are represented by a 'same polarization' item; the off-diagonal elements represent the incident angle and scattered field relationship for the orthogonal polarization mode, represented by the term "cross polarization".
5. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: solving the features in the second stepVector e1,e2,e3Then, 3 mutually independent targets are obtained for constructing a statistical model, and a coherent matrix T is obtained3Expanding the mixture into the sum of 3 mutually independent targets, wherein each target corresponds to a determined scattering mechanism and then is represented by an equivalent simple scattering matrix, and the weight of a component i of the determined scattering mechanism in the whole scattering process is represented by a characteristic value lambdaiDescription, type of scattering mechanism and normalized feature vector eiAnd (4) correlating.
6. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: in the formula (5) in the third step, when the characteristic value has only one non-zero value (lambda)1≠0,λ2＝λ30), then the coherence matrix T3The scattering situation of the point object defined by the polarization scattering matrix S is degraded.
7. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: in the formula (5) in the third step, when all the characteristic values are not zero and are equal (lambda)1＝λ2＝λ3Not equal to 0), the coherence matrix T is obtained3It represents a completely depolarized random scattering state, and the object between these two extremes is partially polarized, when the coherence matrix T is present3Are greater than 1 and are not completely equal to each other.
8. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: in the fourth step, when the impurity degree is lower, the order degree of a corresponding scattering mechanism is higher, and the polarization coefficient is smaller; when the impurity degree is higher, the order degree of a corresponding scattering mechanism is lower, and the polarization coefficient is higher; when the impurity degree reaches the minimum value, the scattering mechanisms in the corresponding set are consistent in type; when the impurity reaches a maximum, it indicates that the scattering of the target in the set exhibits a random noise state.
9. The marine oil spill detection method based on the new fully-polarized SAR characteristics according to claim 1, characterized in that: from the perspective of the polarization state in the fourth step, when the value of the polarization kini coefficient is lower, the system can be regarded as weakly depolarized, the dominant scattering mechanism is regarded as a target scattering mechanism of a specific equivalent point, the feature vector corresponding to the maximum feature value is selected according to the mechanism, and other feature vectors are ignored; when the value of the polarization kiney coefficient is higher, the average scatterer in the set is in a depolarization state, and no single scattering target exists any more, so that the mixing proportion of all possible point target scattering types from the whole characteristic value distribution spectrum needs to be considered; when the polarization coefficient is further increased, the number of scattering mechanisms identified from the polarization measurement data will also gradually decrease, and when the polarization coefficient reaches a maximum value, the polarization information will be zero, and then the target scattering is completely a random noise process.
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