CN110148103A - EO-1 hyperion and Multispectral Image Fusion Methods, computer readable storage medium, electronic equipment based on combined optimization - Google Patents
EO-1 hyperion and Multispectral Image Fusion Methods, computer readable storage medium, electronic equipment based on combined optimization Download PDFInfo
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Abstract
The present invention provides a kind of EO-1 hyperion based on combined optimization and Multispectral Image Fusion Methods, computer readable storage medium, electronic equipment, it solves existing EO-1 hyperion and Multispectral Image Fusion Methods relies on space degenerate matrix, the problem for causing high spectrum image spatial resolution lower.Method includes the following steps: step 1, input true value；Step 2 pre-processes true value, obtains observable low spatial resolution high spectrum image and observable high spatial resolution multi-spectral image；Step 3, according to line spectrum aliasing model, using Non-negative Matrix Factorization, to image IhWith image ImIt is mixed to carry out spectrum solution, end member matrix and abundance matrix needed for combined optimization obtain image IhEnd member matrix E, abundance matrix AhWith image ImAbundance matrix A；Step 4 estimates high spatial resolution high spectrum image, is denoted as image Z.
Description
Technical field
The present invention relates to technical field of image processing, and in particular to a kind of EO-1 hyperion and multispectral figure based on combined optimization
As fusion method, computer readable storage medium, electronic equipment, can be applied to environmental monitoring, target detection, target classification and
The fields such as military surveillance.
Background technique
Hyperspectral imager samples electromagnetic spectrum in many continuous and very narrow spectral band, acquisition
High spectrum image has high spectral resolution.In order to obtain more wave bands, sensor has a light splitting before receiving luminous energy
Light is divided into many parts by process, i.e. spectroscope, then each wave band only has small portion after light splitting under light projectile energy certain condition
Energy is divided to reach sensor.Sensor obtains certain luminous energy and could respond, in order to ensure enough signal-to-noise ratio, it is necessary to increase chip picture
Plain size.Pixel Dimensions refer to the area of each pixel, and when Pixel Dimensions increase, pixel quantity reduces in unit area, this meeting
The image spatial resolution obtained is caused to reduce.Based on the above reasons, had by the high spectrum image that Hyperspectral imager obtains
There is low spatial resolution.Due to various hardware limitations, existing technical staff proposes to improve high spectrum image using software approach
Spatial resolution, i.e. EO-1 hyperion and Multispectral Image Fusion Methods, by with the multispectral image with high spatial resolution
Fusion is to improve high spectrum image spatial resolution.
In recent years, researchers propose many EO-1 hyperions and Multispectral Image Fusion Methods.The purpose of these methods
It is, by observable low spatial resolution-high spectral resolution high spectrum image and the low spectrum of observable high spatial resolution-
Resolution multi-spectral image generates unobservable high spatial resolution-high spectral resolution high spectrum image.Such method is big
More dependences and space degenerate matrix, space degenerate matrix contains high spectral resolution high spectrum image and low spatial resolution is more
Space degeneration relationship between spectrum picture.Z.H.Nezhad et al. is in document " Z.H.Nezhad, A.Karami, R.Heylen
and P. Scheunders,“Fusion of Hyperspectral and Multispectral Images Using
Spectral Unmixing and Sparse Coding,”IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing,vol.9,no.6,pp.2377-2389,2016.”
It is middle to propose a kind of EO-1 hyperion and Multispectral Image Fusion Methods that sparse coding is mixed based on spectrum solution, the fusion process of this method
An ill posed inverse problem, first with based on the regularization term that sparse coding constructs be converted into it is suitable determine inverse problem, then use
Some high spatial resolution multi-spectral images or full-colour image and then one suitable dictionary of building, are based on the word in uncorrelated scene
Allusion quotation and sparse coding is estimated by the initial high spatial resolution high spectrum image that line spectrum solution mixes model estimation, reused dilute
It dredges coding and is used as regularization term, fixed inverse problem is fitted to calculate abundance by solution, is finally obtained from the abundance and end member obtained
Required high spatial resolution high spectrum image.However, in practical applications, it is very tired accurately to estimate space degenerate matrix
Difficulty, such method need estimation space degenerate matrix, and the evaluated error of generation can be propagated in fusion process, influence fusion method
Performance.
In conclusion existing EO-1 hyperion and Multispectral Image Fusion Methods excessively rely on space degenerate matrix, lead to height
Spectrum picture spatial resolution is lower, it is made to there is centainly restricted.
Summary of the invention
Space degenerate matrix is relied in order to solve existing EO-1 hyperion and Multispectral Image Fusion Methods, leads to high spectrum image
The lower problem of spatial resolution, the invention proposes a kind of based on the EO-1 hyperion of combined optimization and Multispectral Image Fusion side
Method, computer readable storage medium, electronic equipment are mainly used for improving the low problem of high spectrum image spatial resolution, to mention
The spatial resolution of high high spectrum image.
The technical solution of the invention is as follows:
A kind of EO-1 hyperion and Multispectral Image Fusion Methods based on combined optimization, comprising the following steps:
Step 1, input true value, the true value refer to true high spatial resolution high spectrum image；
Step 2 pre-processes true value, obtains observable low spatial resolution high spectrum image and observable
They are denoted as image I by high spatial resolution multi-spectral image respectivelyhWith image Im；
Step 3, according to line spectrum aliasing model, using Non-negative Matrix Factorization, to image IhWith image ImCarry out spectrum solution
It is mixed, end member matrix and abundance matrix needed for combined optimization；After reaching maximum number of iterations, image I is obtainedhEnd member matrix
E, abundance matrix AhWith image ImAbundance matrix A；
Step 4, solution are mixed to rebuild, according to image IhEnd member matrix E and image ImAbundance matrix A, calculate high spatial point
Resolution high spectrum image, i.e., final fusion results are denoted as image Z.
Further, the step 2 specifically:
Step 2.1) carries out space degeneration processing to true value, i.e., first carries out fuzzy operation to true value, then to gained knot
Fruit carries out down-sampling operation, obtains observable low spatial resolution high spectrum image, is denoted as image Ih；
Step 2.2) carries out Spectrum curve degradation processing to true value, that is, allows true value and spectral response matrix multiple, and obtaining can
The high spatial resolution multi-spectral image of observation, is denoted as image Im。
Further, the step 3 specifically:
Step 3.1) input picture Ih, image ImWith spectral response matrix Gm, image IhEnd member matrix and abundance matrix
It is denoted as E and A respectivelyh, image ImAbundance matrix be denoted as A, according to line spectrum aliasing model Ih≈EAhAnd Im≈GmEA is right
Image IhSpectrum solution, which is carried out, using Non-negative Matrix Factorization mixes available E and Ah, to image ImLight is carried out using Non-negative Matrix Factorization
Spectrum solution mixes available A；
Step 3.2) initializes end member matrix E, abundance matrix AhWith abundance matrix A, the number of iterations k=0；
Step 3.3) updates end member matrix E, abundance matrix A simultaneouslyhWith abundance matrix A:
Wherein, α(k)It is the step-length of kth time iteration, L is loss function；
Wherein, parameter lambda=8, ‖ ‖FIndicate F norm,WithL is respectively indicated to E, AhPartial derivative is sought with A；
Wherein, ()TRepresenting matrix transposition；
Step 3.4) judges whether to reach maximum number of iterations, the end member matrix E that exports if reaching, abundance matrix
AhWith with abundance matrix A, otherwise continue step 3.3).
Further, the step 4 specifically:
Step 4.1) is according to image IhEnd member matrix E and image ImAbundance matrix A, calculate high spatial resolution height
Spectrum picture, i.e., final fusion results are denoted as image Z:
Z=EA.
Further, the step 4 further includes step 4.2):
Step 4.2) movement images Z and true value, Calculation Estimation index.
Meanwhile the present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, the calculating
The step of above method is realized when machine program is executed by processor.
In addition, the present invention also provides a kind of electronic equipment, the electronic equipment includes:
Processor；
Computer readable storage medium, is stored thereon with computer program, and the computer program is transported by the processor
The step of above method is executed when row.
Compared with the conventional method, the invention has the following advantages:
1. image interfusion method of the present invention does not need the priori knowledge of Spectrum curve degradation matrix, using the side based on combined optimization
Method merges low spatial resolution high spectrum image and high spatial resolution multi-spectral image, according to line spectrum aliasing mould
Type carries out spectrum to low spatial resolution high spectrum image and high spatial resolution multi-spectral image using Non-negative Matrix Factorization
Solution is mixed, and end member matrix and abundance matrix needed for combined optimization finally carry out them to solve mixed reconstruction, and available quality is higher
High spatial resolution high spectrum image, this method do not need the participation of Spectrum curve degradation matrix, overcomes conventional method to spectrum
The dependence of degenerate matrix improves the spatial resolution of high spectrum image, has the advantages that syncretizing effect is good, complexity is low.
2. image interfusion method application range of the present invention is wider, can be applied to environmental monitoring, target detection, target classification with
And the fields such as military surveillance.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the EO-1 hyperion of combined optimization and Multispectral Image Fusion Methods.
Specific embodiment
The contents of the present invention are described in further detail below in conjunction with the drawings and specific embodiments:
The method of the present invention is according to line spectrum aliasing model, using Non-negative Matrix Factorization, to low spatial resolution EO-1 hyperion
Image and high spatial resolution multi-spectral image carry out spectrum solution and mix, end member matrix and abundance matrix needed for combined optimization, most
They are carried out afterwards to solve mixed reconstruction, the available higher high spatial resolution high spectrum image of quality.The present invention and existing side
Method is compared, and the participation of Spectrum curve degradation matrix is not needed, and overcomes dependence of the conventional method to Spectrum curve degradation matrix, has fusion effect
The advantages of fruit is good, complexity is low and has wide range of applications.
As shown in Figure 1, the EO-1 hyperion and Multispectral Image Fusion Methods provided by the present invention based on combined optimization, including
Following steps:
Step 1, input true value, true value refer to true high spatial resolution high spectrum image；
Step 2 pre-processes true value, obtains observable low spatial resolution high spectrum image and observable
They are denoted as image I by high spatial resolution multi-spectral image respectivelyhWith image Im；
Step 2.1) carries out space degeneration processing to true value, i.e., first carries out fuzzy operation to true value, then to gained knot
Fruit carries out down-sampling operation, obtains observable low spatial resolution high spectrum image, is denoted as image Ih；
Step 2.2) carries out Spectrum curve degradation processing to true value, that is, allows true value and spectral response matrix multiple, and obtaining can
The high spatial resolution multi-spectral image of observation, is denoted as image Im；
Step 3, according to line spectrum aliasing model, using Non-negative Matrix Factorization, to image IhWith image ImCarry out spectrum solution
Mixed, end member matrix and abundance matrix needed for combined optimization obtain image I after reaching maximum number of iterationshEnd member matrix
E, abundance matrix AhWith image ImAbundance matrix A；
Step 3.1) input picture Ih, image ImWith spectral response matrix Gm, image IhEnd member matrix and abundance matrix
It is denoted as E and A respectivelyh, image ImAbundance matrix be denoted as A, according to line spectrum aliasing model Ih≈EAhAnd Im≈GmEA is right
Image IhSpectrum solution, which is carried out, using Non-negative Matrix Factorization mixes available E and Ah, to image ImLight is carried out using Non-negative Matrix Factorization
Spectrum solution mixes available A；
Step 3.2) initializes end member matrix E, abundance matrix AhWith abundance matrix A, the number of iterations k=0；
Step 3.3) updates end member matrix E, abundance matrix A simultaneouslyhWith abundance matrix A:
Wherein, α(k)It is the step-length of kth time iteration, its calculation method is in document " C.-J.Lin, " Projected
gradient methods for nonnegative matrix factorization,”Neural Computation,
Vol. 2779 19, no.10, pp.2756-, in 2007. " Algorithm 4, L is loss function,
Wherein, parameter lambda=8, ‖ ‖FIndicate F norm,WithL is respectively indicated to E, AhPartial derivative is sought with A,
Wherein, ()TRepresenting matrix transposition；
Step 3.4) judges whether to reach maximum number of iterations, the end member matrix E that exports if reaching, abundance matrix
AhWith with abundance matrix A, otherwise continue step 3.3；
The mixed reconstruction of step 4, solution and Calculation Estimation index
Step 4.1) is according to image IhEnd member matrix E and image ImAbundance matrix A, estimate high spatial resolution height
Spectrum picture, i.e., final fusion results are denoted as image Z:
Z=EA.
Step 4.2) movement images Z and true value, Calculation Estimation index.By comparing the similar of fusion results and true value
Size is spent, to evaluate the performance of fusion method, i.e. the similarity of the fusion results and true value matter that shows fusion results more greatly
Amount is better.
Effect of the invention is further explained below by way of specific emulation experiment.
1, simulated conditions
The method of the present invention be central processing unit be Intel (R) Core (TM) i7 5930k, memory 64GB, Ubuntu grasp
Make in system, the emulation carried out with MATLAB software；
2, emulation content
For the experimental data used for Pavia University database, which includes 1 true high spectrum image,
Picture material is Pavia University, and the spatial resolution of image is 1.3 meters.The size of image is 200 × 200 × 103,
Wherein 200 × 200 be image space size, 103 be spectral band number.It will be original in Pavia University database
High spectrum image as true value, low spatial resolution height of the image obtained again by fuzzy and down-sampling 4 as test
Spectrum picture.
On Pavia University database, complete inventive algorithm (a kind of EO-1 hyperion based on combined optimization and
Multispectral Image Fusion Methods) experiment.In order to prove the validity of algorithm, comprehensively considers the popular, newness of algorithm, select
4 kinds of control methods have been taken to be compared: Bicubic, FUSE, PALM and CO-CNMF.Wherein, Bicubic is classical benchmark side
Method, by Zeyde et al. in document " R.Zeyde, M.Elad, and M.Protter, " On single image scale-up
Usingsparse-representations, " the middle proposition of Curves and Surfaces, 2012, pages 711-730. "；
FUSE is in document " Q.Wei, N.Dobigeon, and J.-Y.Tourneret, " Fast fusion of multi-
bandimages based on solving a sylvester equation,”IEEE Transactions onImage
It is proposed in Processing, vol.24, no.11, pp.4109-4121,2015. "；PALM document " C.Lanaras,
E.Baltsavias,and K.Schindler,“Hyperspectralsuperresolution by coupled
spectral unmixing,”in IEEE International Conference on Computer Vision,2015,
Pp.3586-3594. it is proposed in "；CO-CNMF is in document " C.-H.Lin, F.Ma, C.-Y. Chi, and C.-H.Hsieh, " A
convex optimization-based coupled nonnegative matrix factorization algorithm
for hyperspectral and multispectral data fusion,”IEEE Transactions on
It is proposed in Geoscienceand Remote Sensing, vol.56, no.3, pp.1652-1667,2018. ".
Fusion side is measured using PSNR, UIQI, RMSE, ERGAS and SAM of true value and super-resolution high spectrum image
The performance of method.On Pavia University database, it is compared with 4 kinds of control methods, the results are shown in Table 1.
As seen from Table 1, fusion results of the invention are more preferable than existing fusion method.This is because existing method needs to estimate
Space degenerate matrix, the error generated in estimation procedure can be transmitted in fusion process, this will affect the performance of fusion method, this
Invention does not need the participation of space degenerate matrix, and alleviating the error generated during estimation space degenerate matrix can merge
The problem of being transmitted in journey.Therefore than other methods, more robust more effectively further demonstrates of the invention advanced this method
Property.
The comparing result of the different fusion methods of table 1
| Bicubic | FUSE | PALM | CO-CNMF | The present invention | |
| PSNR | 25.885 | 32.032 | 33.708 | 32.105 | 36.329 |
| UIQI | 0.768 | 0.949 | 0.963 | 0.950 | 0.975 |
| RMSE | 13.261 | 6.906 | 5.367 | 6.629 | 4.260 |
| ERGAS | 8.086 | 4.256 | 3.473 | 3.955 | 3.049 |
| SAM | 6.271 | 4.840 | 4.038 | 4.280 | 3.650 |
The embodiment of the present invention also provides a kind of computer readable storage medium, and for storing program, program is performed reality
Now the step of EO-1 hyperion and Multispectral Image Fusion Methods based on combined optimization.In some possible embodiments, this hair
Bright various aspects are also implemented as a kind of form of program product comprising program code, when described program product is at end
When running in end equipment, said program code is for executing the terminal device described in this specification above method part
The step of various illustrative embodiments according to the present invention.
For realizing the program product of the above method, portable compact disc read only memory (CD-ROM) can be used
And including program code, and can be run on terminal device, such as PC.However, program product of the invention is unlimited
In this, in this document, readable storage medium storing program for executing can be any tangible medium for including or store program, which can be referred to
Enable execution system, device or device use or in connection.
Program product can be using any combination of one or more readable mediums.Readable medium can be readable signal Jie
Matter or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or partly lead
System, device or the device of body, or any above combination.More specific example (the non exhaustive column of readable storage medium storing program for executing
Table) it include: the electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only storage
Device (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Claims (7)
1. a kind of EO-1 hyperion and Multispectral Image Fusion Methods based on combined optimization, which comprises the following steps:
Step 1, input true value, the true value refer to true high spatial resolution high spectrum image；
Step 2 pre-processes true value, obtains observable low spatial resolution high spectrum image and observable high-altitude
Between resolution multi-spectral image, they are denoted as to image I respectivelyhWith image Im；
Step 3, according to line spectrum aliasing model, using Non-negative Matrix Factorization, to image IhWith image ImIt is mixed to carry out spectrum solution,
End member matrix and abundance matrix needed for combined optimization；After reaching maximum number of iterations, image I is obtainedhEnd member matrix E,
Abundance matrix AhWith image ImAbundance matrix A；
Step 4, solution are mixed to rebuild, according to image IhEnd member matrix E and image ImAbundance matrix A, calculate high spatial resolution
High spectrum image, i.e., final fusion results are denoted as image Z.
2. the EO-1 hyperion and Multispectral Image Fusion Methods according to claim 1 based on combined optimization, which is characterized in that
The step 2 specifically:
Step 2.1) to true value carry out space degeneration processing, i.e., first to true value carry out fuzzy operation, then to acquired results into
The operation of row down-sampling, obtains observable low spatial resolution high spectrum image, is denoted as image Ih；
Step 2.2) carries out Spectrum curve degradation processing to true value, that is, allows true value and spectral response matrix multiple, obtains Observable
High spatial resolution multi-spectral image, be denoted as image Im。
3. the EO-1 hyperion and Multispectral Image Fusion Methods according to claim 1 based on combined optimization, which is characterized in that
The step 3 specifically:
Step 3.1) input picture Ih, image ImWith spectral response matrix Gm, image IhEnd member matrix and abundance matrix difference
It is denoted as E and Ah, image ImAbundance matrix be denoted as A, according to line spectrum aliasing model Ih≈EAhAnd Im≈GmEA, to image
IhSpectrum solution, which is carried out, using Non-negative Matrix Factorization mixes available E and Ah, to image ImSpectrum solution is carried out using Non-negative Matrix Factorization
Mix available A；
Step 3.2) initializes end member matrix E, abundance matrix AhWith abundance matrix A, the number of iterations k=0；
Step 3.3) updates end member matrix E, abundance matrix A simultaneouslyhWith abundance matrix A:
Wherein, α(k)It is the step-length of kth time iteration, L is loss function；
Wherein, parameter lambda=8, ‖ ‖FIndicate F norm,WithL is respectively indicated to E, AhPartial derivative is sought with A；
Wherein, ()TRepresenting matrix transposition；
Step 3.4) judges whether to reach maximum number of iterations, end member matrix E, the abundance matrix A exported if reachinghWith
With abundance matrix A, otherwise continue step 3.3).
4. the EO-1 hyperion and Multispectral Image Fusion Methods according to claim 1 or 2 or 3 based on combined optimization, special
Sign is, the step 4 specifically:
Step 4.1) is according to image IhEnd member matrix E and image ImAbundance matrix A, calculate high spatial resolution EO-1 hyperion
Image, i.e., final fusion results are denoted as image Z:
Z=EA.
5. the EO-1 hyperion and Multispectral Image Fusion Methods according to claim 4 based on combined optimization, which is characterized in that
The step 4 further includes step 4.2):
Step 4.2) movement images Z and true value, Calculation Estimation index.
6. a kind of computer readable storage medium, is stored thereon with computer program, it is characterised in that: the computer program quilt
The step of claim 1-4 any the method is realized when processor executes.
7. a kind of electronic equipment, which is characterized in that the electronic equipment includes:
Processor；
Computer readable storage medium is stored thereon with computer program, when the computer program is run by the processor
Perform claim requires the step of 1 to 4 any the method.
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| CN111369487A (en) * | 2020-05-26 | 2020-07-03 | 湖南大学 | Hyperspectral and multispectral image fusion method, system and medium |
| CN113205453A (en) * | 2021-04-06 | 2021-08-03 | 武汉大学 | Hyperspectral fusion method based on space-spectrum total variation regularization |
| CN113516646A (en) * | 2021-07-26 | 2021-10-19 | 云南电网有限责任公司电力科学研究院 | Method and device for enhancing high spectral resolution of multi-sensor image data |
| CN113822207A (en) * | 2021-09-27 | 2021-12-21 | 海南长光卫星信息技术有限公司 | Hyperspectral remote sensing image identification method and device, electronic equipment and storage medium |
| CN113888413A (en) * | 2021-12-03 | 2022-01-04 | 湖南大学 | Different-time different-source multispectral image blind spectrum super-resolution method and system |
| CN114092834A (en) * | 2022-01-24 | 2022-02-25 | 南京理工大学 | Unsupervised hyperspectral image blind fusion method and system based on space-spectrum combined residual correction network |
| CN116245779A (en) * | 2023-05-11 | 2023-06-09 | 四川工程职业技术学院 | Image fusion method and device, storage medium and electronic equipment |
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