CN110458137A - A kind of diameter radar image recognition methods based on two-stage multi-task learning - Google Patents

A kind of diameter radar image recognition methods based on two-stage multi-task learning Download PDF

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CN110458137A
CN110458137A CN201910765203.2A CN201910765203A CN110458137A CN 110458137 A CN110458137 A CN 110458137A CN 201910765203 A CN201910765203 A CN 201910765203A CN 110458137 A CN110458137 A CN 110458137A
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张新征
王亦坚
谭志颖
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Chongqing University
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Abstract

The invention discloses a kind of diameter radar image recognition methods based on two-stage multi-task learning, comprising: extracts the heterogeneous feature of multiclass of target image and training image collection；The linear combination of the corresponding heterogeneous feature of all training images is concentrated to indicate with training image every heterogeneous feature of one kind of target image；Extract the coefficient vector of every heterogeneous feature of one kind；Obtain the M neighbour part subset and the new dictionary of composition of every heterogeneous feature of one kind；Obtaining collaboration indicates coefficient matrix；The recognition result of target image is obtained into criterion using minimal reconstruction error.First stage of the invention obtains best local subset of the target image in training sample, and accordingly update the dictionary of each feature, greatly reduce the interference of the training sample far from target image, it is interfered so as to avoid caused by exceptional value, classification performance is substantially better than the study of multitask rarefaction representation and multitask coordinated expression learning algorithm, and has robustness to extensive regularization parameter.

Description

A kind of diameter radar image recognition methods based on two-stage multi-task learning
Technical field
The present invention relates to Technology of Radar Target Identification fields, and in particular to a kind of synthesis hole based on two-stage multi-task learning Aperture radar image recognition methods.
Background technique
Radar image target identification is the important subject in science and techniques of defence field.It is thunder using the method for indicating study Up to approach more effective in image domains, key technology is exactly to indicate the foundation of learning model.
The common radar image target identification method based on expression study has: (1) based on sparse representation method, indicating Under the sparse constraint of coefficient, obtain uniquely indicating coefficient solution, what rarefaction representation utilized is " competition " relationship between sample, The method that algorithm complexity higher (2) is indicated based on collaboration, collaboration indicate to utilize " cooperation " relationship between sample, actually L2 Optimization problem under norm constraint greatly reduces computation complexity, but need in the accuracy rate of algorithm and robustness into one Step improves (3) based on multi-task learning and the method for indicating that study is combined, and this method has become at present indicates the another of study Development trend.Multi-task learning takes full advantage of that identification mission is similar enough or this related characteristic to a certain extent, favorably In the generalization ability for improving recognizer.The a variety of different types of features for extracting image see the expression identification of every category feature Work is a task, and establishing multiple features combining indicates model.Multi-task learning combine indicate study method image recognition with And there is very extensive utilization in class object identification field, can excavate the shared training sample mode between different task, from And ensure to select correct training sample, reduce the interference of error category training sample.
The present invention proposes a kind of base on the basis of indicating the method for learning to be combined based on multi-task learning existing In the diameter radar image recognition methods of two-stage multi-task learning, it is more to indicate that learning method is utilized based on two-stage multitask Feature cooperates with discriminating power, and rarefaction representation is combined with the expression learning ability that collaboration indicates, utilizes in the first stage L2,1The multitask rarefaction representation of norm regularization obtains best local subset of the target image in training sample, and accordingly more The dictionary of new each feature, greatly reduces the interference of the training sample far from target image, causes so as to avoid exceptional value Interference, classification performance is substantially better than the study of multitask rarefaction representation and multitask coordinated expression learning algorithm, and to extensive Regularization parameter has robustness.
Summary of the invention
In view of the above shortcomings of the prior art, the present invention is in the existing side combined based on multi-task learning with expression study A kind of diameter radar image recognition methods based on two-stage multi-task learning is proposed on the basis of method, is based on two-stage more Business indicates that learning method is utilized multiple features and cooperates with discriminating power, and by rarefaction representation and cooperates with the expression learning ability indicated It combines, utilizes L in the first stage2,1The multitask rarefaction representation of norm regularization obtains target image in training sample Best part subset, and the dictionary of each feature is accordingly updated, the interference of the training sample far from target image is greatly reduced, It is interfered so as to avoid caused by exceptional value, classification performance is substantially better than the study of multitask rarefaction representation and multitask coordinated expression Learning algorithm, and there is robustness to extensive regularization parameter.
Present invention employs the following technical solutions:
A kind of diameter radar image recognition methods based on two-stage multi-task learning, comprising:
S1, target image and training image collection are obtained；
S2, the heterogeneous feature of multiclass for extracting the target image and the training image collection；
S3, concentrate all training images corresponding with the training image every heterogeneous feature of one kind of the target image The linear combination of heterogeneous feature indicates；
S4, the linear combination based on the heterogeneous feature indicate to extract the coefficient vector of every heterogeneous feature of one kind；
S5, the coefficient vector based on every heterogeneous feature of one kind obtain the M neighbour part of every heterogeneous feature of one kind Subset and the new dictionary of composition, M neighbour part subset are the proper subclass of coefficient vector；
S6, new dictionary is formed to indicate target image based on M neighbour part subset, obtaining collaboration indicates coefficient matrix；
S7, coefficient matrix is indicated based on collaboration, obtains the recognition result of target image into criterion using minimal reconstruction error.
Preferably, the heterogeneous feature of the multiclass includes any one in PCA feature, Wavelet Transform Feature and 2DSZM feature Kind is a variety of.
Preferably, in step S3:
……
In formula, ykFor the heterogeneous feature of kth class of target image, P is heterogeneous feature class number,For ykCorresponding i-th of instruction Practicing the coefficient vector atom of image, n is the number that the training image concentrates training image,For ykCorresponding i-th of training The heterogeneous feature of image；
By the heterogeneous character representation of target image are as follows:
y1=X1a'1
……
yk=Xka'k
yP=XPa'P
In formula, XkFor ykCorresponding heterogeneous feature vector, a'kFor ykCorresponding expression coefficient vector.
Preferably, in step S4:
Establish L2,1The multitask sparse representation model of norm regularization:
In formula, η is balance parameters, | | | |2,1Indicate L2,1Norm, A are coefficient vector collection, A=[a1…aP, | | | |F The F norm of representing matrix.
Preferably, in step S5:
Maximum preceding Q coefficient vector atom is extracted from the coefficient vector of every heterogeneous feature of one kind and duplicate removal obtains atom Collection,The k pattern feature vector of j-th of atom is concentrated for atom；
Based on formulaCalculate distance measure amount ej, take corresponding ejThe smallest preceding M atom forms M Neighbour part subset；
0 is set by the coefficient vector atom other than atom in the subset of M neighbour part, obtains new heterogeneous feature vector simultaneously Using new heterogeneous feature vector as new dictionary, X'kFor XkCorresponding new dictionary.
Preferably, in step S6:
New dictionary is formed based on M neighbour part subset to indicate target image:
In formula, ρ is balance parameters, | | | |FThe F norm of representing matrix, B are that collaboration indicates coefficient matrix, B=[b1… bP], bkFor ykCorresponding new expression coefficient vector.
In conclusion the invention discloses a kind of diameter radar image identification sides based on two-stage multi-task learning Method, comprising: S1, obtain target image and training image collection；S2, the multiclass for extracting the target image and the training image collection Heterogeneous feature；S3, concentrate all training images corresponding with the training image every heterogeneous feature of one kind of the target image Heterogeneous feature linear combination indicate；S4, the linear combination based on the heterogeneous feature indicate to extract every heterogeneous feature of one kind Coefficient vector；S5, the coefficient vector based on every heterogeneous feature of one kind obtain the M neighbour office of every heterogeneous feature of one kind Portion's subset and the new dictionary of composition, M neighbour part subset are the proper subclass of coefficient vector；S6, it is formed newly based on M neighbour part subset Dictionary indicates target image, and obtaining collaboration indicates coefficient matrix；S7, coefficient matrix is indicated based on collaboration, utilizes minimal reconstruction Error obtains the recognition result of target image into criterion.On the basis of existing technology, the present invention is based on two-stage multitask expressions Multiple features collaboration discriminating power is utilized in learning method, and rarefaction representation is mutually tied with the expression learning ability indicated is cooperateed with It closes, utilizes L in the first stage2,1It is best in training sample that the multitask rarefaction representation of norm regularization obtains target image Local subset, and the dictionary of each feature is accordingly updated, the interference of the training sample far from target image is greatly reduced, thus Interference caused by exceptional value is avoided, classification performance is substantially better than the study of multitask rarefaction representation and multitask coordinated expression study Algorithm, and there is robustness to extensive regularization parameter.
Detailed description of the invention
In order to keep the purposes, technical schemes and advantages of invention clearer, the present invention is made into one below in conjunction with attached drawing The detailed description of step, in which:
Fig. 1 is a kind of stream of the diameter radar image recognition methods based on two-stage multi-task learning disclosed by the invention Cheng Tu；
Fig. 2 is a certain radar target image；
Fig. 3 is that the second stage collaboration of PCA feature indicates coefficient；
Fig. 4 is that the second stage collaboration of wavelet character indicates coefficient；
Fig. 5 is that the second stage collaboration of 2DSZM feature indicates coefficient；
Fig. 6 is the reconstructed error that three category features in learning method are indicated based on two-stage multitask；
Fig. 7 is to indicate that learning method, direct multitask sparse representation method and direct multitask are assisted based on two-stage multitask The curve graph changed with the discrimination of three kinds of methods of representation method with intrinsic dimensionality；
Fig. 8 is to indicate that learning method, direct multitask sparse representation method and direct multitask are assisted based on two-stage multitask With three kinds of method discriminations of representation method with the curve graph for anywhere rule changing Parameters variation；
Fig. 9 is the identification indicated in learning method for two stage Different Rule Parameters variations based on two-stage multitask Effect picture.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawing.
As shown in Figure 1, the invention discloses a kind of diameter radar image identification sides based on two-stage multi-task learning Method, comprising:
S1, target image and training image collection are obtained；
In the present invention, the corresponding label of training image can be also obtained during obtaining training image collection, for subsequent Identification.
S2, the heterogeneous feature of multiclass for extracting the target image and the training image collection；
It is well known that each category feature is all to describe image from some angle, therefore it cannot be distinguished comprising all Other information.In addition, it is unpractiaca for extracting an optimal characteristics.Therefore, for image identification system, better method is to combine Multiclass feature is identified, rather than only uses single features.
S3, concentrate all training images corresponding with the training image every heterogeneous feature of one kind of the target image The linear combination of heterogeneous feature indicates；
S4, the linear combination based on the heterogeneous feature indicate to extract the coefficient vector of every heterogeneous feature of one kind；
S5, the coefficient vector based on every heterogeneous feature of one kind obtain the M neighbour part of every heterogeneous feature of one kind Subset and the new dictionary of composition, M neighbour part subset are the proper subclass of coefficient vector；
S6, new dictionary is formed to indicate target image based on M neighbour part subset, obtaining collaboration indicates coefficient matrix；
S7, coefficient matrix is indicated based on collaboration, obtains the recognition result of target image into criterion using minimal reconstruction error.
The recognition result of target image is obtained for the prior art, herein not into criterion using minimal reconstruction error in the present invention It repeats again.
First stage of the invention is exactly the individual features that every category feature of target image is expressed as to all training samples Linear combination, and determine using the multitask rarefaction representation ability of training sample the M neighbour part subset of target image. This is because in principle, current target image and its some neighbour's sample standard deviations should be from same class, that is to say, that These neighbour's samples are maximum to the contribution of recognition target image.Therefore, the first step of algorithm is exactly to detect far from target image Those of training sample, and assume that these samples do not influence in categorised decision.This precise classification for target image It is helpful.With local training sample, rather than all training samples carry out recognition target image, can substantially reduce far from mesh The interference of those of logo image training sample.The second stage of algorithm is exactly will be using multitask coordinated expression Learning Principle, benefit Target image is indicated with the new dictionary that M neighbour part subset forms, and carries out images steganalysis using expression result.The It is to be that second level, which uses the reason of multitask coordinated expression: it is multitask coordinated to indicate simple, and have closed solutions.The side proposed Method has following reason: its first stage has determined some with the most like training sample of current target image, and will be identified Candidate categories label of the class label of training sample as target image.Since the class label of the training sample identified is usually The a subset of all training samples determines finally so final classification becomes in a candidate class label from lesser amt The problem of class label.When the class label of real target image is one in those training samples for determining, this will be to the It is very helpful that two-stage carries out accurately classification.This algorithm proposed by the present invention has not only sufficiently excavated the table of multiclass feature Show learning ability；And the interference of those unrelated dictionary atoms is greatly reduced, therefore effectively improve recognition performance.
When it is implemented, the heterogeneous feature of multiclass includes appointing in PCA feature, Wavelet Transform Feature and 2DSZM feature It anticipates one or more.
In present invention, it is preferable to simultaneously using PCA feature, Wavelet Transform Feature and 2DSZM feature these three features, these three It is characterized in the SAR image feature from different feature extraction angle extractions, has preferable complementary.
When it is implemented, in step S3:
……
In formula, ykFor the heterogeneous feature of kth class of target image, P is heterogeneous feature class number,For ykCorresponding i-th of instruction Practicing the coefficient vector atom of image, n is the number that the training image concentrates training image,For ykCorresponding i-th of training The heterogeneous feature of image；
By the heterogeneous character representation of target image are as follows:
y1=X1a'1
……
yk=Xka'k
yP=XPa'P
In formula, XkFor ykCorresponding heterogeneous feature vector, a'kFor ykCorresponding expression coefficient vector.
When it is implemented, in step S4:
Establish L2,1The multitask sparse representation model of norm regularization:
In formula, η is balance parameters, | | | |2,1Indicate L2,1Norm, A are coefficient vector collection, A=[a1…aP], | | | |F The F norm of representing matrix.
According to rarefaction representation principle, the feature from identical training sample should be shared between every kind of feature of target image. And in view of brings errors such as noise factors, L can will be obtained2,1The multitask sparse representation model of norm regularization.More Regularization term has selected the L of A in business sparse representation model2,1Norm regularization term.This is because for coming from same target figure For the different characteristic of picture, the position of nonzero coefficient should be similar in all sparse vectors, i.e., dilute between shared class The mode of dredging.And due to the difference between different types of feature, the shared corresponding coefficient values of training sample of these in sparse vector It is different.Under this hypothesis, indicate that nonzero coefficient should be in Xiang Tonghang, a joint sparse regularization L in A2,1It can be set to In the non-zero row of expression for selecting smallest number on A.Multitask sparse representation model can be solved by APG algorithm.
When it is implemented, in step S5:
Maximum preceding Q coefficient vector atom is extracted from the coefficient vector of every heterogeneous feature of one kind and duplicate removal obtains atom Collection,The k pattern feature vector of j-th of atom is concentrated for atom；
Based on formulaCalculate distance measure amount ej, take corresponding ejThe smallest preceding M atom forms M Neighbour part subset；
0 is set by the coefficient vector atom other than atom in the subset of M neighbour part, obtains new heterogeneous feature vector simultaneously Using new heterogeneous feature vector as new dictionary, X'kFor XkCorresponding new dictionary.
By multitask sparse representation model, available A, it is each column be corresponding different characteristic expression coefficient to Amount.When indicating the feature of target image, the individual features of each training sample can make the contribution of oneself.Substantially, A Each column be rarefaction representation coefficient vector of the target image under character pair.The contribution of i-th of training sample in dictionary It can be assessed by the size of corresponding rarefaction representation coefficient value.One big expression coefficient means that i-th of training sample There is very big contribution to expression target image.Due to there is the multiple rarefaction representation coefficient vectors for corresponding to multiple features；Therefore we M neighbour part subset is obtained using above-mentioned method.In this way, the dictionary X that the feature of every one kind is constitutedkIt is all accordingly updated to new Dictionary X'k.In this way, can greatly reduce uncorrelated atom pair indicates study bring interference, to reduce erroneous judgement.
When it is implemented, in step S6:
New dictionary is formed based on M neighbour part subset to indicate target image:
In formula, ρ is balance parameters, | | | |FThe F norm of representing matrix, B are that collaboration indicates coefficient matrix, B=[b1… bP], bkFor ykCorresponding new expression coefficient vector.
It is multitask coordinated to indicate that there are analytic solutions.That is:
It is to be calculated simply because it has, it is possible to reduce the advantage of computation burden using multitask coordinated expression.
After the multitask coordinated expression in the second level, target type is determined according to multitask reconstructed error minimum, it is as follows Formula:
In order to prove technical effect of the invention, we are using radar target image as shown in Figure 2 as target image. For given radar target image, the heterogeneous feature of multiclass of all training samples and target image, including PCA spy are extracted first Sign, Wavelet Transform Feature, 2DSZM feature.Multitask in the first stage indicates to utilize L in study2,1Norm regularization it is more Task sparse representation model acquires the coefficient vector of every category feature.It is maximum to select Q coefficient value before finding in each coefficient vector Atom, remove duplicate atom, by these atoms form a new set, further according to the Europe of characteristic vector and target image M atom forms final M neighbour part subset before formula distance is selected.It will be corresponding in the training sample dictionary of each feature M training sample retains, and other dictionary atoms are set to 0 update dictionary.Second stage utilizes multitask coordinated expression, obtains The collaboration of the feature of tertiary target image indicates the reconstructed error of coefficient (as shown in figure 3, figure 4 and figure 5) and three category features (such as Fig. 6).Fig. 7 is that study and direct multitask sparse representation method and direct multitask coordinated method are indicated based on two-stage multitask With the recognition effect that intrinsic dimensionality changes, which illustrates that two-stage multi-task learning method still can be under dimension situation of change Keep more superior recognition performance.Fig. 8 is that (method of the invention is TSMRL to three kinds of methods, and in addition two methods are respectively MSRC and MCRC) anywhere rule change the recognition performance curve of Parameters variation, which has absolutely proved this method for regularisation parameter Value has preferable robustness, and good recognition effect (this hair can be obtained in the very big variation range of regularisation parameter value Two kinds of regularisation parameters, λ and ρ can be used in bright).Fig. 9 is two-stage multi-task learning method for two stage Different Rules The recognition effect figure of Parameters variation.These experimental results support the superiority of method proposed by the present invention.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although passing through ginseng According to the preferred embodiment of the present invention, invention has been described, it should be appreciated by those of ordinary skill in the art that can To make various changes to it in the form and details, without departing from the present invention defined by the appended claims Spirit and scope.

Claims (6)

1. a kind of diameter radar image recognition methods based on two-stage multi-task learning characterized by comprising
S1, target image and training image collection are obtained；
S2, the heterogeneous feature of multiclass for extracting the target image and the training image collection；
S3, concentrate all training images corresponding heterogeneous with the training image every heterogeneous feature of one kind of the target image The linear combination of feature indicates；
S4, the linear combination based on the heterogeneous feature indicate to extract the coefficient vector of every heterogeneous feature of one kind；
S5, the coefficient vector based on every heterogeneous feature of one kind obtain the M neighbour part subset of every heterogeneous feature of one kind And new dictionary is formed, M neighbour part subset is the proper subclass of coefficient vector；
S6, new dictionary is formed to indicate target image based on M neighbour part subset, obtaining collaboration indicates coefficient matrix；
S7, coefficient matrix is indicated based on collaboration, obtains the recognition result of target image into criterion using minimal reconstruction error.
2. as described in claim 1 based on the diameter radar image recognition methods of two-stage multi-task learning, feature exists In the heterogeneous feature of multiclass includes any one or more in PCA feature, Wavelet Transform Feature and 2DSZM feature.
3. as described in claim 1 based on the diameter radar image recognition methods of two-stage multi-task learning, feature exists In in step S3:
……
In formula, ykFor the heterogeneous feature of kth class of target image, P is heterogeneous feature class number,For ykCorresponding i-th of training image Coefficient vector atom, n be the training image concentrate training image number,For ykCorresponding i-th of training image Heterogeneous feature；
By the heterogeneous character representation of target image are as follows:
y1=X1a'1
……
yk=Xka'k
yP=XPa'P
In formula, XkFor ykCorresponding heterogeneous feature vector, a'kFor ykCorresponding expression coefficient vector.
4. as claimed in claim 3 based on the diameter radar image recognition methods of two-stage multi-task learning, feature exists In in step S4:
Establish L2,1The multitask sparse representation model of norm regularization:
In formula, η is balance parameters, | | | |2,1Indicate L2,1Norm, A are coefficient vector collection, A=[a1...aP], | | | |FTable Show the F norm of matrix.
5. as claimed in claim 4 based on the diameter radar image recognition methods of two-stage multi-task learning, feature exists In in step S5:
Maximum preceding Q coefficient vector atom is extracted from every heterogeneous characteristic quantity of one kind and duplicate removal obtains atom collection,For atom Concentrate the k pattern feature vector of j-th of atom；
Based on formulaCalculate distance measure amount ej, take corresponding ejThe smallest preceding M atom forms M neighbour Local subset；
0 is set by the coefficient vector atom other than atom in the subset of M neighbour part, obtains new heterogeneous feature vector and will be new Heterogeneous feature vector as new dictionary, X'kFor XkCorresponding new dictionary.
6. as claimed in claim 5 based on the diameter radar image recognition methods of two-stage multi-task learning, feature exists In in step S6:
New dictionary is formed based on M neighbour part subset to indicate target image:
In formula, ρ is balance parameters, | | | |FThe F norm of representing matrix, B are that collaboration indicates coefficient matrix, B=[b1...bP], bk For ykCorresponding new expression coefficient vector.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3115378A1 (en) * 2020-10-20 2022-04-22 Biomerieux Method for classifying an input image representing a particle in a sample
CN115470873A (en) * 2022-11-14 2022-12-13 中国人民解放军国防科技大学 Radar radiation source identification method and system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104899549A (en) * 2015-04-17 2015-09-09 重庆大学 SAR target recognition method based on range profile time-frequency image identification dictionary learning
CN106096506A (en) * 2016-05-28 2016-11-09 重庆大学 Based on the SAR target identification method differentiating doubledictionary between subclass class

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104899549A (en) * 2015-04-17 2015-09-09 重庆大学 SAR target recognition method based on range profile time-frequency image identification dictionary learning
CN106096506A (en) * 2016-05-28 2016-11-09 重庆大学 Based on the SAR target identification method differentiating doubledictionary between subclass class

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
XINZHENG ZHANG 等: "Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification", 《SENSORS》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3115378A1 (en) * 2020-10-20 2022-04-22 Biomerieux Method for classifying an input image representing a particle in a sample
WO2022084617A1 (en) * 2020-10-20 2022-04-28 Biomerieux Method for classifying an input image containing a particle in a sample
CN115470873A (en) * 2022-11-14 2022-12-13 中国人民解放军国防科技大学 Radar radiation source identification method and system

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