CN107578432A - Merge visible ray and the target identification method of infrared two band images target signature - Google Patents
Merge visible ray and the target identification method of infrared two band images target signature Download PDFInfo
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Abstract
The present invention disclose a kind of target identification method for merging visible ray and infrared two band images target signature, and methods described is included to visible ray with, the band image of far infrared two carries out the target registration stage and to visible ray with, the band image of the far infrared two progress target identification stage.This method is a kind of using target overall region as starting point, and visible ray and the target identification method of infrared two band images target signature are merged with being realized on the basis of the infrared same target area of two band images in registering visible ray.
Description
Technical field
Visible ray and the target identification method of infrared two band images target signature are merged the present invention relates to a kind of, belongs to mesh
Identify other technical field.
Background technology
Modernized war active demand round-the-clock can identify Research on Target from complex background.Aircraft, rocket
The heat wave length of the targets of military importance such as bullet, cruise missile has focused largely on 3~5 microns of middle infrared band and 8~10 micro-
In the far infrared band of rice；Under different atmospheric environments, the transmissivity that middle infrared band and far infrared band radiate is different.Can
See that light image has abundant colouring information, texture information, the details of Research on Target can be caught.Infrared image can it is round-the-clock into
Picture, remains the marginal information of target well, and structural information is complete.Middle infrared image is richer than the grain details of far infrared image
Information that is rich, including is more, the high temperature heat radiation concentrated area especially in target；Infrared image is integrally inclined in far infrared image ratio
It is bright.For higher-quality round-the-clock identification target, fusion visible ray and the target identification of infrared two band images target signature
As key technology.
At present, the research of multi-source image fusion is mostly the Pixel-level fusion of feature based Point matching, when different source images
When quantity increases and image background complicates, Feature Points Matching effect is poor, substantial amounts of error hiding characteristic point pair be present, ultimately results in
There is ghost phenomena in Pixel-level fused images, be unfavorable for the target identification in later stage.
The content of the invention
For above-mentioned technical problem, the present invention is intended to provide one kind is using target overall region as starting point, it is visible in registration
Light merges visible ray and infrared two band images target signature with being realized on the basis of the infrared same target area of two band images
Target identification method.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of target identification method for merging visible ray and infrared two band images target signature, methods described includes pair can
See light with, the band image of far infrared two carry out the target registration stage and to visible ray with, the band image of far infrared two carry out
The target identification stage.
It is described to visible ray with, the band image of far infrared two carry out the target registration stage, first to wave band carry out rough segmentation
Essence registration is carried out after class；
First it will be seen that light image carries out medium filtering, in infrared, far infrared image carry out histogram after first medium filtering
Equilibrium, target area imaging model is established, extract image target area, the spatial gradation histogram for then extracting target area is special
Sign, the target area is carried out to carry out rough sort under its affiliated light source；Secondly according to all kinds of destination numbers included and
Notable feature difference between class and class judges which visible images target class, middle infrared image target class and far infrared image
Target class belongs to same class target；When certain class number of targets be more than or equal to 2 when, extract such each target visible ray, in it is red
Spatial edge direction histogram feature in outer and far infrared image, calculate target visible light image and target far infrared image it
Between similarity, and the similarity in target between infrared image and target far infrared image, respective maximum similarity is corresponding
Two kinds of light source image targets be same target, realize similar target in 3 kinds of light by intermediate variable of target far infrared image
Source hypograph target registration.
It is described to visible ray with, the band image of far infrared two carry out the target identification stage, based on target registration result,
Target visible light and infrared two band images Sample Storehouse are established, fusion feature is calculated, is entered into softmax graders
In, realize fusion visible ray with, the target identification of the band image target signature of far infrared two.
There are the graders such as Logistic, SVM, K-Means, Random Forest, KNN in addition.Softmax graders
The classification of identification is not limited to two classes, and multi-class targets identification can be achieved.Softmax graders are using cross-entropy loss function as cost letter
Number, the probability that test sample belongs to each classification is calculated, classification corresponding to maximum probability is the classification of the test sample.
What Softmax graders were seen is the accuracy rate of classification, higher to the classification sensitivity of mistake, and its loss function value is deposited all the time
, be a The Insatiable grader.Therefore, what in deep learning field, we used is more Softmax graders.
It is described to be to visible ray and the detailed process in infrared two band image progress target registration stage：
Step 1.1, to image preprocessing, it is seen that light image carries out medium filtering, in infrared, far infrared image carry out intermediate value
Filtering and histogram equalization；
Step 1.2, according to visible ray, in infrared and far infrared image imaging characteristicses, determine image target area and background
The feature difference in region, image target area imaging model is established, extract the target area in multi-source image；
Step 1.3, for each single source images, the spatial gradation histogram feature of extraction each of which target area, calculate every
Similitude between individual target area, by each single source images target area rough sort；
Step 1.4, the number of targets that all kinds of targets include in visible images, middle infrared image and far infrared image is compared
Amount, contained number of targets identical visible images target class, middle infrared image target class and far infrared image object class is corresponding
Get up, referred to as the visible images target of same type, middle infrared image target and far infrared image object.If each single source images
The destination number that middle inhomogeneity target includes is consistent, then needs by the big target signature (such as area) of discrimination between all kinds of
To determine which class visible images target, middle infrared image target and far infrared image object are to belong to same type of.
Step 1.5, when in rough sort certain class destination number be more than or equal to 2 when, extract such each target visible ray,
In spatial edge direction histogram feature in infrared and far infrared image；First carry out target far infrared image with it is infrared in target
Image compares, then carries out target far infrared image compared with target visible light image；
Step 1.6, for two kinds of light source image targets of same type, the spatial edge direction histogram for calculating target is similar
Degree.Using far infrared image object as intermediate variable, middle infrared image target corresponding to maximum similarity and the far infrared image mesh
Mark is same target, realizes similar target mid and far infrared image object and middle infrared image target registration；Maximum similarity pair
The visible images target answered and the far infrared image object are same targets, realize similar target mid and far infrared image object
With visible images target registration.Finally realize visible images target in similar target, middle infrared image target and far infrared
Image object registration.
It is described to visible ray with, the band image of far infrared two carry out the target identification stage detailed process be：
Step 2.1, obtained in the step 1.6 registration cross target on the basis of, target establish target visible light with
In, the band image Sample Storehouse of far infrared two, half is as training set, and half is as test set；
Step 2.2, visible ray is automatically extracted with, effective spy of the band image target of far infrared two with convolutional neural networks
Sign, obtains fusion feature, final to realize fusion visible ray and infrared two wave bands figure as the input of softmax graders
As the target identification of target signature.
The present invention has following innovation：
1st, merged target visible ray, in infrared target identification, fully profit are carried out with the band image feature of far infrared three
With the complementary information between target difference source images.
2nd, in visible ray and infrared two band images target registration stage, propose first to carry out target rough segmentation on single source images
Class, then progress visible images target, middle infrared image target are smart registering with far infrared image object in similar target, can
Improve target registration speed.Meanwhile the target rough sort stage is carried out in single source images, different source images imagings can be reduced
Influence caused by characteristic difference.
3rd, the target identification stage has used convolutional neural networks, and it can be automatically extracted in target visible light image, target
The fusion feature of infrared image and target far infrared image, the error of artificial extraction characteristic strip can be reduced.
Brief description of the drawings
Fig. 1 is visible ray of the present invention and infrared two band images target registration method flow block diagram；
Fig. 2 is present invention fusion visible ray and the target identification method FB(flow block) of infrared two band images target signature.
Embodiment
This method is divided into visible ray and infrared two band images target registration and visible ray and infrared two band images target
Two parts are identified, are comprised the following steps that：
(1) visible ray and infrared two band images target registration
When destination number is very big, visible ray and infrared two band images target registration method based on blind search mechanism
Time complexity is also big, so the present invention is proposed based on the visible ray of essence registration and infrared two band images mesh after first rough sort
Mark method for registering：
1. image preprocessing, it is seen that light image carries out medium filtering, in infrared, far infrared image carry out medium filtering and straight
Side's figure is balanced；
2. study visible ray, in infrared and far infrared image imaging characteristicses, specify image target area and background area
Feature difference, establish image target area imaging model；
3. according to visible ray, in infrared and far infrared image target area imaging model, extract multi-source image in target
Region；
4. being directed to each single source images, the spatial gradation histogram feature of extraction each of which target area, each target is calculated
Similitude between region, it is believed that similarity is same class target more than 0.5, realizes the rough segmentation of each single source images target area
Class (target elder generation rough sort can reduce influence of the difference of different source images image-forming principles to registration accuracy in single source images)；
5. compare the destination number that all kinds of targets include in visible images, middle infrared image and far infrared image, will
Contained number of targets identical visible images target class, middle infrared image target class and far infrared image object class are mapped,
Referred to as the visible images target of same type, middle infrared image target and far infrared image object.If in each single source images not
The destination number that similar target includes is consistent, then needs by the big target signature (such as area) of discrimination between all kinds of come really
Fixed which class visible images target, middle infrared image target and far infrared image object are to belong to same type of.
6. when in rough sort certain class destination number be more than or equal to 2 when, extract such each target visible ray, in it is infrared
With the spatial edge direction histogram feature in far infrared image；First carry out target far infrared image and infrared image ratio in target
Compared with, then target far infrared image is carried out compared with target visible light image；
7. for same type far infrared image object and middle infrared image target, calculate some far infrared image object with
The spatial edge direction histogram similarity of all middle infrared image targets, middle infrared image target corresponding to maximum similarity with
The far infrared image object is same target, realizes that similar target mid and far infrared image object and middle infrared image target are matched somebody with somebody
It is accurate.Similarly, similar target mid and far infrared image object and visible images target registration are realized, finally realizes quick visible ray
With infrared two band images target registration.
Far infrared image is first carried out compared with middle infrared image, then carries out far infrared image compared with visible images, will
Far infrared image is as intermediate quantity.Here visible images target class, middle infrared image target corresponding to referring to more than or equal to 2
The destination number that class and far infrared image object class include is more than or equal to 2, just needs to utilize target image in this case
Spatial edge direction histogram carries out further registration, if the target number that corresponding three source images target class includes is 1,
Visible images, middle infrared image and far infrared image are just registered upper so corresponding to this target, it is not necessary to enter one
The utilization space edge orientation histogram of step carries out registration.Each target include visible ray, in it is infrared with 3 kinds of light sources of far infrared
Image.Comparison is all based on the comparison that two kinds of light sources belong between same type target, is all two two and compares, pair of 3 kinds of light sources
Than being realized by regarding far infrared image as intermediate quantity.
(2) visible ray and infrared two band images target identification
Completed visible ray with infrared two band image same target it is registering, extract on this basis same
The visible ray of target is used for target identification with infrared two band images feature.Visible ray and infrared two band images target identification
Concretely comprise the following steps：
1. establishing target visible light and infrared two band images Sample Storehouse, half is as training set, and half is as test set；
2. automatically extracting the validity feature of visible ray and infrared two band images target with convolutional neural networks, merged
Feature, it is final to realize fusion visible ray and infrared two band images target signature as the input of softmax graders
Target identification.The present embodiment is using softmax graders, also Logistic, SVM, K-Means, Random in addition
The graders such as Forest, KNN.The classification of Softmax graders identification is not limited to two classes, and multi-class targets identification can be achieved.
Softmax graders calculate the probability that test sample belongs to each classification, maximum probability using cross-entropy loss function as cost function
Corresponding classification is the classification of the test sample.What Softmax graders were seen is the accuracy rate of classification, to wrong classification
Sensitivity is higher, and its loss function value exists all the time, is the grader of a The Insatiable.Therefore, led in deep learning
It is more Softmax graders that we, which use, in domain.
The thinking has feasibility, the inventive method is used to study among human body performance-based objective identification process, to data
Handled, being input to convolutional neural networks in convolutional neural networks as input data carries out Local Features Analysis, obtains spy
Levy output item, be directly inputted in Softmax graders, can recognize that walk, runnings, stair activity, 5 kinds of standings etc. act, knowledge
Not rate is up to 84.8%.Equally, the inventive method can also be applied to Finger print identification field, the ability of feature extraction from
A large amount of finger print data learnings are input to Softmax graders, fingerprint image classification are obtained, in International Publication to line type feature
The classification accuracy of line type four is measured on finger print data collection NISTDB4 up to 94.2%.In addition, apply the inventive method to clap on the spot
For the visible ray taken the photograph with, in the band image target identification of far infrared two, target includes car, motorcycle, boy student and schoolgirl, target
Discrimination is up to 96.8%
Described above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (8)
1. a kind of merge visible ray and the target identification method of infrared two band images target signature, it is characterised in that the side
Method include to visible ray with, the band image of far infrared two carry out the target registration stage and to visible ray with, the ripple of far infrared two
Section image carries out the target identification stage.
A kind of 2. target identification side for merging visible ray and infrared two band images target signature according to claim 1
Method, it is characterised in that it is described to visible ray with, the band image of far infrared two carry out the target registration stage, first wave band is entered
Essence registration is carried out after row rough sort；
First it will be seen that light image carries out medium filtering, in infrared, far infrared image carry out histogram equalization after first medium filtering,
Target area imaging model is established, extracts image target area, then extracts the spatial gradation histogram feature of target area,
The target area is carried out to carry out rough sort under its affiliated light source；Secondly according to all kinds of destination numbers included and class with
Notable feature difference between class judges which visible images target class, middle infrared image target class and far infrared image object
Class belongs to same class target；Extract certain a kind of each target visible ray, in spatial edge side in infrared and far infrared image
To histogram feature, the similarity between target visible light image and target far infrared image, and infrared figure in target are calculated
Picture and the similarity between target far infrared image, two kinds of light source image targets corresponding to respective maximum similarity are same mesh
Mark, realizes similar target in 3 kinds of light source hypograph target registrations by intermediate variable of target far infrared image.
A kind of 3. target identification side for merging visible ray and infrared two band images target signature according to claim 1
Method, it is characterised in that it is described to visible ray with, the band image of far infrared two carry out the target identification stage, matched somebody with somebody based on target
Quasi- result, target visible light and infrared two band images Sample Storehouse are established, fusion feature is calculated, is entered into grader
In, realize fusion visible ray with, the target identification of the band image target signature of far infrared two.
A kind of 4. target identification side for merging visible ray and infrared two band images target signature according to claim 2
Method, it is characterised in that described to be to visible ray and the detailed process in infrared two band image progress target registration stage：
Step 1.1, to image preprocessing, it is seen that light image carries out medium filtering, in infrared, far infrared image carry out medium filtering
And histogram equalization；
Step 1.2, according to visible ray, in infrared and far infrared image imaging characteristicses, determine image target area and background area
Feature difference, establish image target area imaging model, extract the target area in multi-source image；
Step 1.3, for each single source images, the spatial gradation histogram feature of extraction each of which target area, each mesh is calculated
The similitude between region is marked, by each single source images target area rough sort；
Step 1.4, the destination number that all kinds of targets include in visible images, middle infrared image and far infrared image is compared,
Contained number of targets identical visible images target class, middle infrared image target class and far infrared image object class are corresponded to
Come, referred to as the visible images target of same type, middle infrared image target and far infrared image object；
Step 1.5, when in rough sort certain class destination number be more than or equal to 2 when, extract such each target visible ray, in it is red
Spatial edge direction histogram feature in outer and far infrared image, first carry out target far infrared image and infrared image in target
Compare, then carry out target far infrared image compared with target visible light image；
Step 1.6, for two kinds of light source image targets of same type, the spatial edge direction histogram similarity of target is calculated；
Using far infrared image object as intermediate variable, middle infrared image target corresponding to maximum similarity is with the far infrared image object
Same target, realize similar target mid and far infrared image object and middle infrared image target registration；Corresponding to maximum similarity
Visible images target and the far infrared image object are same targets, realize similar target mid and far infrared image object and can
See light pattern objects registration, finally realize visible images target in similar target, middle infrared image target and far infrared image
Target registration.
A kind of 5. target identification side for merging visible ray and infrared two band images target signature according to claim 3
Method, it is characterised in that it is described to visible ray with, the band image of far infrared two carry out the target identification stage detailed process
For：
Step 2.1, on the basis of the target that the registration obtained in the step 1.6 is crossed, target establish target visible light with, it is remote
Infrared two band images Sample Storehouse, half is as training set, and half is as test set；
Step 2.2, visible ray is automatically extracted with, the validity feature of the band image target of far infrared two with convolutional neural networks,
Fusion feature is obtained, it is final to realize fusion visible ray and infrared two band images target signature as the input of grader
Target identification.
6. a kind of fusion visible ray and the target identification of infrared two band images target signature according to claim 4 or 5
Method, it is characterised in that
It is special by the big target of discrimination between all kinds of if the destination number that inhomogeneity target includes in each single source images is consistent
Levy to determine which class visible images target, middle infrared image target and far infrared image object are to belong to same type of.
7. a kind of fusion visible ray and the mesh of infrared two band images target signature according to any one of claim 2 to 5
Mark recognition methods, it is characterised in that
The target signature includes what is automatically extracted by convolutional neural networks, and target signature automatically extracts process：Sample will be trained
Originally classification logotype is carried out, with the deep learning method training sample based on convolutional neural networks, obtains disaggregated model, when there is test
When sample inputs, the disaggregated model that convolutional neural networks can train according to this automatically extracts feature.
8. a kind of fusion visible ray and the mesh of infrared two band images target signature according to any one of claim 2 to 5
Mark recognition methods, it is characterised in that the grader includes softmax, Logistic, SVM, K-Means, Random
Forest, KNN grader.
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