CN111553421A - SAR equipment task failure cause reasoning method based on double-layer nested structure - Google Patents
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Abstract
The invention discloses an SAR equipment task failure cause reasoning method based on a double-layer nested structure, which relates to the technical field of SAR equipment guarantee and comprises the following steps: collecting SAR image data of known abnormal types; reconstructing sample categories, and extracting a sample set; calculating image quality evaluation characteristics; image feature transformation, normalization processing and class marking combination; integrated acquisition of training data setsAndtraining a double-layer model; processing an unknown abnormal SAR image into a data set to be detected; and reasoning causes of the failure of the SAR equipment. The invention adopts double-layer stochastic SensenThe forest model combines failure causes which are easy to be wrongly classified, so that the total number of categories is reduced, the samples which are easy to be wrongly classified are secondarily classified by using the image local features on the basis of the result of the first-layer classifier, the precision of the random forest model for classifying abnormal images is enhanced, the problem that the effect of training the model by utilizing SAR image data of different terrains is poor is solved, and the accuracy of SAR equipment task failure cause reasoning is effectively improved.
Description
Technical Field
The invention relates to the technical field of SAR equipment guarantee, in particular to an SAR equipment task failure cause reasoning method based on a double-layer nested structure.
Background
At the present time, the army is in the revolution from mechanized army to information army, and the information collecting, transmitting and processing capability is increasingly improved. SAR as a novel efficient information acquisition weapon has become a new approach for military observation and reconnaissance. SAR imaging is susceptible to various factors, and when an SAR radar executes a task and cannot obtain an image which is high in interpretation degree, good in effect and clear enough, namely when the task fails, fault detection and maintenance need to be carried out on the radar.
The traditional fault diagnosis and elimination technology mainly depends on expert experience, and has the problems of poor reliability, low accuracy and high false alarm rate, so that the completion of daily drilling tasks is influenced, even irrecoverable loss is caused to actual military operations, the problems seriously influence the logistics capacity and operational performance of radar equipment, and a failure cause reasoning method facing the task completion degree is urgently needed at present.
Disclosure of Invention
The invention aims to provide a SAR equipment task failure cause reasoning method based on a double-layer nested structure, which can alleviate the problems.
In order to alleviate the above problems, the technical scheme adopted by the invention is as follows:
a SAR equipment task failure cause reasoning method based on a double-layer nested structure comprises the following steps:
s1, collecting an SAR image data set of known abnormal types of K-type terrains, wherein the SAR image data set comprises a normal SAR image and an abnormal SAR image set, the abnormal SAR image set comprises P-type abnormal SAR images, the number of the abnormal SAR images is M/P, and M is the total number of the abnormal SAR images in the abnormal SAR image set;
s2, combining R type abnormal images I and R type abnormal images II in the abnormal SAR image group of each type of terrain, forming a sample set A1 together with other types of abnormal SAR images in the group, independently extracting the R type abnormal images I and the R type abnormal images II and forming a sample set A2, wherein the R type abnormal images I and the R type abnormal images II are in one-to-one correspondence and are all abnormal SAR images included in the abnormal SAR image group;
s3, constructing a feature data set D1 with N image evaluation indexes for each type of terrain sample set A1kThe feature set Z1 used for feature transformation and having N image evaluation indexes is used for constructing a feature data set D2 having T texture features for a sample set A2 of the feature data setkThe feature set Z2 is used for feature transformation and has T texture features, the N image evaluation indexes comprise an SAR image evaluation index based on a surface target, an SAR image texture feature index based on a gray level co-occurrence matrix and a gradient image evaluation index, and the T texture features are texture features based on a gray level-gradient co-occurrence matrix;
s4, for each type of terrain, feature data set D1kCarrying out feature transformation, normalization processing and class label merging relative to the feature set Z1 to obtain a data set I, and carrying out feature data set D2 on the data set IkCarrying out feature transformation, normalization processing and class mark merging relative to the feature set Z2 to obtain a data set II;
S7, training the double-layer model according to the training data setTraining by using a random forest algorithm to obtain a first-layer classifier of the double-layer model, and obtaining a first-layer classifier of the double-layer model according to a training data setTraining by using a random forest algorithm to obtain a second-layer classifier of the double-layer model;
s8, acquiring a plurality of unknown abnormal SAR images of the K-type terrain, and preprocessing the plurality of unknown abnormal SAR images of each type of terrain to obtain a feature matrix with the N image evaluation indexesAnd having said T texture featuresFeature matrix of K-type terrainObtaining a data set D to be measured after integrationde3Feature matrix of K-type terrainObtaining a data set D to be measured after integrationde4;
S9, collecting the data set D to be measuredde3Inputting a two-layer model, using its first-layer classifier to the data set D to be measuredde3Classifying to obtain a first classification result, if the abnormal type in the R-type abnormal image in the step S2 does not exist in the first classification result, taking the first classification result as a reasoning result of SAR equipment task failure cause, and finishing reasoning, otherwise, continuing to execute the step S10;
s10, the first classification result is compared with the data set D to be measuredde4Corresponding to the same data, extracting and forming a characteristic data set Ar, taking the residual data in the first classification result as the reasoning result of the first part of SAR equipment task failure cause, inputting the characteristic data set Ar into a second layer classifier of the double-layer model to obtain a second classification result, and taking the second classification result as the second part of SAR equipment task failure cause reasoning resultAnd (5) reasoning a result of the equipment task failure cause, and finishing reasoning.
The technical effect of the technical scheme is as follows: the method adopts a double-layer random forest model to combine failure reasons which are easy to be wrongly classified, so that the total number of categories is reduced, and the samples which are easy to be wrongly classified are secondarily classified by using image local characteristics on the basis of the result of a first-layer classifier, so that the precision of the random forest model for classifying abnormal images is enhanced, the problem of poor effect of training the model by utilizing SAR image data of different terrains is solved, and the accuracy of SAR equipment task failure cause reasoning is effectively improved; and for special conditions of abnormal categories which are easy to be wrongly classified, the data are separately classified again by using the characteristics which can better reflect local information, so that the accurate judgment of failure causes under the task failure scene of the airborne SAR is realized.
Further, in the step S1, the abnormal SAR image includes N1 fault types, N2 interference types, and N1 × N2 fault interference superposition types.
The technical effect of the technical scheme is as follows: SAR radar imaging is susceptible to various factors, the conditions of low image interpretation degree, poor effect and poor definition often occur, and the influence on the actual work of the SAR radar can be comprehensively considered by dividing the influence factors.
Further, in step S2, it is determined whether the abnormal image i is similar to the abnormal image ii according to the abnormal type flag of the image, which is a flag made when the image is collected.
The technical effect of the technical scheme is as follows: the images which are easy to be wrongly distinguished are convenient to distinguish, and the training of the subsequent model is convenient.
Further, the feature data set D1kThe construction method of the sum feature set Z1 comprises the following steps: for each abnormal SAR image in the sample set A1, N image evaluation indexes are calculated, and a feature data set D1 is constructed according to the abnormal type labels and the N image evaluation indexes of the abnormal SAR images in the sample set A1kComputing a feature data set D1kN image evaluation indexes of normal SAR image of the terrain and form a feature setZ1;
Feature data set D2kThe construction method of the sum feature set Z2 comprises the following steps: for each abnormal SAR image in the sample set A2, calculating T texture features based on a gray-gradient co-occurrence matrix, and constructing a feature data set D2 according to the abnormal type marks of the abnormal SAR images in the sample set A2 and the T texture features based on the gray-gradient co-occurrence matrixkComputing a feature data set D2kThe T types of normal SAR images of the terrain are based on texture features of a gray-gradient co-occurrence matrix and form a feature set Z2.
The technical effect of the technical scheme is as follows: the SAR image evaluation index based on the surface target, the SAR image texture characteristic index based on the gray level co-occurrence matrix and the gradient image evaluation index are objective image evaluation indexes, and the image quality evaluation result can be guaranteed to have good stability.
Still further, the surface target-based SAR image evaluation indicators include a mean, a variance, a radiation resolution, an equivalent vision, and a sharpness, the gray level co-occurrence matrix-based SAR image texture feature indicators include an angular second moment, a contrast, a correlation, an entropy, an inverse difference, a sum mean, a sum variance, a sum entropy, a variation difference, a difference entropy, a mutual information metric, a maximum correlation coefficient, a maximum probability, a dissimilarity, a contrast, a median, a dark cluster, and a salient cluster, and the gradient image evaluation indicators include an energy gradient, an average gradient, a gray level difference product, a Brenner gradient, and a Laplacian gradient.
The technical effect of the technical scheme is as follows: the SAR image can be comprehensively evaluated by comprehensively considering the information of the surface target, texture, gradient and the like of the SAR image.
Still further, the texture features based on the gray-gradient co-occurrence matrix include small gradient dominance, large gradient dominance, gray distribution heterogeneity, gradient distribution heterogeneity, energy, gray mean, gradient mean, gray standard deviation, gradient standard deviation, correlation, gray entropy, gradient entropy, mixture entropy, difference moment, inverse difference moment.
The technical effect of the technical scheme is as follows: the texture features based on the gray-gradient co-occurrence matrix can better reflect local information of the SAR image, so that overall statistical information and local difference information of the SAR image are considered, and multi-level and multi-index image quality evaluation indexes are established to comprehensively infer the task failure cause by combining the overall characteristic evaluation index and the local characteristic evaluation index of the SAR image, thereby comprehensively evaluating the overall quality and the local distortion condition of the SAR image.
Further, in step S8, the preprocessing the unknown abnormal SAR image includes:
constructing a feature data set D3, comprising: for each unknown abnormal SAR image, calculating the N image evaluation indexes, and constructing a feature data set D3 according to the N image evaluation indexes of each abnormal SAR image;
constructing a feature data set D4, comprising: for each unknown abnormal SAR image, calculating the T texture features of the unknown abnormal SAR image, and constructing a feature data set D4 according to the T texture features of each abnormal SAR image;
performing feature transformation on the feature data set D3 relative to the feature set Z1, and performing normalization processing on the transformation result to obtain the feature matrix
Performing feature transformation on the feature data set D4 relative to the feature set Z2, and performing normalization processing on the transformation result to obtain the feature matrix
The technical effect of the technical scheme is as follows: the method solves the problem that the accuracy of the inference result is influenced due to inconsistent performance of the characteristic factors of the inference problem of SAR task failure causes under different terrains by a new characteristic transformation strategy of mapping the original characteristic space to a difference space with the standard normal image characteristics, and can accelerate the training of a subsequent model through data normalization.
Further, the feature transformation is specifically: and performing difference operation on the features in the feature data set and the features in the feature set for feature transformation in a one-to-one correspondence manner, and taking the obtained difference result as the transformation result of the feature data set.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method of a two-level nested structure model employed in the present invention;
FIG. 2 is a flowchart of a specific embodiment of the SAR equipment task failure cause reasoning method based on a double-layer nested structure;
FIG. 3 is an example of a SAR image used by the present invention for training and testing.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1 and fig. 2, the present embodiment provides a method for reasoning cause of task failure of SAR equipment based on a double-layer nested structure, including the following steps:
s1, collecting an SAR image data set of known abnormal types of K types of terrains, wherein the SAR image data set comprises a normal SAR image and an abnormal SAR image set, the abnormal SAR image set comprises P types of abnormal SAR images, the number of the abnormal SAR images is M/P, and M is the total number of the abnormal SAR images in the abnormal SAR image set.
In the present embodiment, the SAR image data of known abnormal type is from some type of onboard SAR radar, where K is 6, 6 types of terrain are mountainous area, typical building, lake, hill, island and small airport, respectively, and M is 290, 6 types of terrain total 1740 abnormal SAR images and 6 normal SAR images, as shown in fig. 3.
In the present embodiment, the total number of abnormal types P is 29, where the number of fault types N1 is 4, the number of interference types N2 is 5, the number of superimposed fault interference types N1 × N2 is 20, and the number of superimposed fault interference types is obtained by superimposing 5 types of interference with 4 types of faults. For each terrain, the number of abnormal images of each type is M/P-10, and the total number is 1740.
And S2, combining the R type abnormal images I and the R type abnormal images II in the abnormal SAR image group of each type of terrain, forming a sample set A1 together with other types of abnormal SAR images in the group, and separately extracting the R type abnormal images I and the R type abnormal images II to form a sample set A2, wherein the R type abnormal images I and the R type abnormal images II are in one-to-one correspondence and are all the abnormal SAR images included in the abnormal SAR image group.
In this embodiment, R is 3, which is the dominant wave error plus the deception interference, the platform vibration plus the deception interference, and the inertial navigation error plus the deception interference, respectively, these 3 anomalies are easily separated from the dominant wave error, the platform vibration, and the inertial navigation error, respectively, and these 3 categories are merged into 3 categories of the dominant wave error, the platform vibration, and the inertial navigation error, respectively, and then constitute a sample set a1 together with other categories of anomalous SAR images in the anomalous SAR image group. These 6 abnormal image structure sample sets a2 were extracted separately, where a2 has S-360 images.
In the present embodiment, it is determined whether the abnormal image i is similar to the abnormal image ii based on the abnormal type class flag of the image, which is a flag made at the time of collecting the image.
Whether the abnormal image I is similar to the abnormal image II or not is judged through manual judgment, for example, the deception jamming effect means that a false target exists in the image, which is not beneficial to military judgment, and when the deception jamming is weak, the two kinds of anomalies, namely a single fault condition and fault superposition weak deception jamming, are close to each other, and the difference is difficult to judge by human eyes.
S3, constructing a feature data set D1 with N image evaluation indexes for each type of terrain sample set A1kThe feature set Z1 used for feature transformation and having N image evaluation indexes is used for constructing a feature data set D2 having T texture features for a sample set A2 of the feature data setkThe feature set Z2 is used for feature transformation and has T texture features, the N image evaluation indexes comprise SAR image evaluation indexes based on a surface target, SAR image texture feature indexes based on a gray level co-occurrence matrix and gradient image evaluation indexes, and the T texture features are texture features based on the gray level-gradient co-occurrence matrix.
For the kth terrain, calculating N image evaluation indexes of each abnormal SAR image in a sample set A1, and constructing a feature data set D1 of M abnormal SAR images in the kth terrain according to the image evaluation indexes and the abnormal type class marksk;
Calculating N image evaluation indexes of the normal SAR image, and forming a feature set Z1;
wherein,Xka feature data set (i.e. a feature matrix) representing an abnormal image of the a1 sample set,a column vector formed by the characteristics corresponding to the n-th evaluation index of the M abnormal images, YkA column vector which represents the abnormal type constitution corresponding to the M abnormal images, class labels that are exception types;
in this embodiment, the image quality evaluation characteristic is set to be N ═ 30, where the SAR image evaluation indexes based on the surface target include mean, variance, radiation resolution, equivalent view, and sharpness, the SAR image texture characteristic indexes based on the gray level co-occurrence matrix include angular second moment, contrast, correlation, entropy, inverse difference, sum average, sum variance, sum entropy, variation difference, difference entropy, mutual information metric, maximum correlation coefficient, maximum probability, difference, contrast, median, dark cluster, and salient cluster, and the conventional gradient image evaluation indexes include energy gradient, average gradient, gray level difference product, Brenner gradient, and Laplacian gradient.
S images are set in a sample set A2, T texture features based on a gray-gradient co-occurrence matrix of each abnormal SAR image in the sample set A2 are calculated, and a feature data set D2 is constructed by utilizing the texture features based on the gray-gradient co-occurrence matrix and the abnormal type marks of the imagesk;
wherein, a feature data set (i.e. a feature matrix) representing an abnormal image of the a2 sample set,a column vector formed by the characteristics corresponding to the t-th evaluation index of the S abnormal images,a column vector which represents the abnormal type constitution corresponding to the S abnormal images,s=1,2,…,S,class labels that are exception types;
in this embodiment, the image quality assessment feature is set to T15, and is mainly based on texture features of the gray-gradient co-occurrence matrix, including small gradient dominance, large gradient dominance, gray distribution heterogeneity, gradient distribution heterogeneity, energy, gray mean, gradient mean, gray standard difference, gradient standard difference, correlation, gray entropy, gradient entropy, mixed entropy, differential moment, and inverse differential moment.
S4, for each type of terrain, feature data set D1kCarrying out feature transformation, normalization processing and class label merging relative to the feature set Z1 to obtain a data set I, and carrying out feature data set D2 on the data set IkAnd (5) performing feature transformation, normalization processing and class mark merging relative to the feature set Z2 to obtain a data set II.
In this embodiment, the feature transformation refers to performing difference operation on each feature in the feature data set and each feature in the feature set for feature transformation in a one-to-one correspondence manner, and taking an obtained difference result as a transformation result of the feature data set, which is specifically as follows:
for the feature data set D1kIs characterized by that it has
For the feature data set D2kIs characterized by that it has
Wherein, X'kIs XkThe feature matrix after the feature transformation is carried out,is composed ofAnd (5) feature matrix after feature transformation.
Then respectively aligning the feature matrices X′kAndnormalizing by columns to obtain normalized feature matrix X'kAnd
wherein, the normalization operation according to the columns is as follows:
therein, maxnFor the n-th dimension, i.e. feature matrix X′kMaximum of the n-th element, minnFor the n-th dimension, i.e. feature matrix X′kMinimum value of the nth column element;for the t-th dimension, i.e. feature matrixThe maximum value of the element in the t-th column,for the t-th dimension, i.e. feature matrixThe t-th column element minimum;
finally, the normalized feature matrix X "kAnd class label column vector YkMerging to obtain the characteristic data set D of N abnormal images under the kth terrain "kI.e. data set i; will be provided withAnd class label column vectorMerging to obtain the characteristic data set of S abnormal images in the kth terrainNamely a data set II;
S5、Data sets of various terrains ID'kIntegrating to obtain a training data set
Wherein G ═ M × K.
Wherein F ═ sxk.
S7, training the double-layer model according to the training data setTraining by using a random forest algorithm to obtain a first-layer classifier of the double-layer model, and obtaining a first-layer classifier of the double-layer model according to a training data setAnd training by using a random forest algorithm to obtain a second-layer classifier of the double-layer model.
S8, acquiring a plurality of unknown abnormal SAR images of the K-type terrain, and preprocessing the plurality of unknown abnormal SAR images of each type of terrain to obtain a feature matrix with N image evaluation indexesAnd having T texture featuresFeature matrix of K-type terrainObtaining a data set D to be measured after integrationde3Feature matrix of K-type terrainObtaining a data set D to be measured after integrationde4;
In this embodiment, the process of preprocessing the unknown abnormal SAR image includes the same image processing process as that in the model training process, including:
constructing a feature data set D3, comprising: for each unknown abnormal SAR image, calculating the N image evaluation indexes, and constructing a feature data set D3 according to the N image evaluation indexes of each abnormal SAR image;
constructing a feature data set D4, comprising: for each unknown abnormal SAR image, calculating the T texture features of the unknown abnormal SAR image, and constructing a feature data set D4 according to the T texture features of each abnormal SAR image;
performing feature transformation on the feature data set D3 relative to the feature set Z1, and performing normalization processing on the transformation result to obtain the feature matrix
Performing feature transformation on the feature data set D4 relative to the feature set Z2, and performing normalization processing on the transformation result to obtain the feature matrix
Then, the feature matrix of the K-type terrainObtaining a data set D to be measured after integrationde3Feature matrix of K-type terrainObtaining a data set D to be measured after integrationde4:
In this example, 500 abnormal SAR images are selected as Q abnormal SAR images of unknown abnormal types for verification.
S9, collecting the data set D to be measuredde3Inputting a two-layer model, using its first-layer classifier to the data set D to be measuredde3And (4) classifying to obtain a first classification result, namely an abnormal type corresponding to each of the Q abnormal SAR images, if the abnormal type in the R abnormal images in the step (S2) does not exist in the first classification result (namely the abnormal type is not the R type which is easy to be wrongly classified), the abnormal type is used as an inference result of the SAR equipment task failure cause, the inference is finished, and otherwise, the step (S10) is continuously executed.
S10, the first classification result is compared with the data set D to be measuredde4And taking out the same corresponding data and forming a characteristic data set Ar, taking the residual data in the first classification result as a first part SAR equipment task failure cause reasoning result, inputting the characteristic data set Ar into a second layer classifier of the double-layer model to obtain a second classification result, taking the second classification result as a second part SAR equipment task failure cause reasoning result, and finishing reasoning.
The final result can be expressed as Yde={yde_1,yde_2,…,yde_QAnd each element represents the abnormal type of the corresponding numbered abnormal SAR image.
In the present embodiment, the generalization ability of the two-layer model is evaluated using the Accuracy (ACC).
Wherein TN, TP, FN and FP represent the number of true negative, true positive, false negative and false positive, respectively.
Table 1 shows the accuracy index result of the inference method for the cause of task failure of the SAR equipment based on the double-layer nested structure in this embodiment.
TABLE 1
Number of training samples | Number of samples tested | Test set ACC | |
Adaboost | 1740 | 500 | 0.149 |
Decision tree | 1740 | 500 | 0.300 |
Support vector machine | 1740 | 500 | 0.497 |
K nearest neighbor | 1740 | 500 | 0.586 |
Random forest | 1740 | 500 | 0.866 |
Double-layer nested random forest | 1740 | 500 | 0.930 |
The accuracy comparison of the algorithm models shows that the constructed multi-classification random forest model based on the double-layer nested structure has the highest classification precision, and the purpose of the invention is realized.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A SAR equipment task failure cause reasoning method based on a double-layer nested structure is characterized by comprising the following steps:
s1, collecting an SAR image data set of known abnormal types of K-type terrains, wherein the SAR image data set comprises a normal SAR image and an abnormal SAR image set, the abnormal SAR image set comprises P-type abnormal SAR images, the number of the abnormal SAR images is M/P, and M is the total number of the abnormal SAR images in the abnormal SAR image set;
s2, combining R type abnormal images I and R type abnormal images II in the abnormal SAR image group of each type of terrain, forming a sample set A1 together with other types of abnormal SAR images in the group, independently extracting the R type abnormal images I and the R type abnormal images II and forming a sample set A2, wherein the R type abnormal images I and the R type abnormal images II are in one-to-one correspondence and are all abnormal SAR images included in the abnormal SAR image group;
s3, constructing a feature data set D1 with N image evaluation indexes for each type of terrain sample set A1kThe feature set Z1 used for feature transformation and having N image evaluation indexes is used for constructing a sample set A2 with T patternsFeature data set D2 of physical featureskThe feature set Z2 is used for feature transformation and has T texture features, the N image evaluation indexes comprise an SAR image evaluation index based on a surface target, an SAR image texture feature index based on a gray level co-occurrence matrix and a gradient image evaluation index, and the T texture features are texture features based on a gray level-gradient co-occurrence matrix;
s4, for each type of terrain, feature data set D1kCarrying out feature transformation, normalization processing and class label merging relative to the feature set Z1 to obtain a data set I, and carrying out feature data set D2 on the data set IkCarrying out feature transformation, normalization processing and class mark merging relative to the feature set Z2 to obtain a data set II;
S7, training the double-layer model according to the training data setTraining by using a random forest algorithm to obtain a first-layer classifier of the double-layer model, and obtaining a first-layer classifier of the double-layer model according to a training data setTraining by using a random forest algorithm to obtain a second-layer classifier of the double-layer model;
s8, acquiring a plurality of unknown abnormal SAR images of the K-type terrain, and preprocessing the plurality of unknown abnormal SAR images of each type of terrain to obtain a feature matrix with the N image evaluation indexesAnd having said T texture featuresFeature matrix of K-type terrainObtaining a data set D to be measured after integrationde3Feature matrix of K-type terrainObtaining a data set D to be measured after integrationde4;
S9, collecting the data set D to be measuredde3Inputting a two-layer model, using its first-layer classifier to the data set D to be measuredde3Classifying to obtain a first classification result, if the abnormal type in the R-type abnormal image in the step S2 does not exist in the first classification result, taking the first classification result as a reasoning result of SAR equipment task failure cause, and finishing reasoning, otherwise, continuing to execute the step S10;
s10, the first classification result is compared with the data set D to be measuredde4And taking out the same corresponding data and forming a characteristic data set Ar, taking the residual data in the first classification result as a first part SAR equipment task failure cause reasoning result, inputting the characteristic data set Ar into a second layer classifier of the double-layer model to obtain a second classification result, taking the second classification result as a second part SAR equipment task failure cause reasoning result, and finishing reasoning.
2. The SAR equipment task failure cause reasoning method based on the double-layer nested structure as claimed in claim 1, wherein in the step S1, the abnormal SAR image includes N1 fault types, N2 interference types and N1 XN 2 fault interference superposition types.
3. The SAR equipment task failure cause reasoning method based on the double-layer nested structure as claimed in claim 1, wherein in step S2, it is determined whether an abnormal image I is similar to an abnormal image II according to an abnormal type mark of the image, where the abnormal type mark is a mark made when the image is collected.
4. The SAR equipment task failure cause reasoning method based on the double-layer nested structure as claimed in claim 1,
feature data set D1kThe construction method of the sum feature set Z1 comprises the following steps: for each abnormal SAR image in the sample set A1, N image evaluation indexes are calculated, and a feature data set D1 is constructed according to the abnormal type labels and the N image evaluation indexes of the abnormal SAR images in the sample set A1kComputing a feature data set D1kN image evaluation indexes of the normal SAR image of the terrain to which the image belongs form a feature set Z1;
feature data set D2kThe construction method of the sum feature set Z2 comprises the following steps: for each abnormal SAR image in the sample set A2, calculating T texture features based on a gray-gradient co-occurrence matrix, and constructing a feature data set D2 according to the abnormal type marks of the abnormal SAR images in the sample set A2 and the T texture features based on the gray-gradient co-occurrence matrixkComputing a feature data set D2kThe T types of normal SAR images of the terrain are based on texture features of a gray-gradient co-occurrence matrix and form a feature set Z2.
5. The SAR equipment task failure cause reasoning method based on the double-layer nested structure is characterized in that the SAR image evaluation indexes based on the plane target comprise a mean value, a variance, a radiation resolution, an equivalent vision and a sharpness, the SAR image texture feature indexes based on the gray level co-occurrence matrix comprise an angular second moment, a contrast, a correlation, an entropy, an inverse difference, a mean, a variance, a sum entropy, a variation difference, a difference entropy, a mutual information measure, a maximum correlation coefficient, a maximum probability, a difference, a contrast, a median, a dark cluster and a salient cluster, and the gradient image evaluation indexes comprise an energy gradient, a mean gradient, a gray level difference product, a Brenner gradient and a Laplacian gradient.
6. The SAR equipment task failure cause reasoning method based on the double-layer nested structure as claimed in claim 5, wherein the texture features based on the gray-gradient co-occurrence matrix include small gradient dominance, large gradient dominance, gray distribution heterogeneity, gradient distribution heterogeneity, energy, gray mean, gradient mean, gray standard deviation, gradient standard deviation, correlation, gray entropy, gradient entropy, mixed entropy, difference moment, and inverse difference moment.
7. The SAR equipment task failure cause reasoning method based on the double-layer nested structure as claimed in claim 6, wherein in the step S8, the process of preprocessing the unknown abnormal SAR image comprises:
constructing a feature data set D3, comprising: for each unknown abnormal SAR image, calculating the N image evaluation indexes, and constructing a feature data set D3 according to the N image evaluation indexes of each abnormal SAR image;
constructing a feature data set D4, comprising: for each unknown abnormal SAR image, calculating the T texture features of the unknown abnormal SAR image, and constructing a feature data set D4 according to the T texture features of each abnormal SAR image;
performing feature transformation on the feature data set D3 relative to the feature set Z1, and performing normalization processing on the transformation result to obtain the feature matrix
8. The SAR equipment task failure cause reasoning method based on the double-layer nested structure as claimed in claim 7, wherein the feature transformation specifically comprises: and performing difference operation on the features in the feature data set and the features in the feature set for feature transformation in a one-to-one correspondence manner, and taking the obtained difference result as the transformation result of the feature data set.
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