CN1588448A - Image optimum fusing method based on fuzzy integral - Google Patents

Abstract

The invention discloses an optimized fusion method based on fuzzy integral, in the HIS space, the intensity component of the spectrum image is decomposed with wavelet multi-layer and acquires the low frequency base band coefficients, acquires the low frequency base band coefficient by decomposing the high resolution image with multi-layer wavelet, integrates the two single factor indexes of spectrum information and space resolution are synthesized with fuzzy integral, carries on pixel fusion with iteration, and carries on character fusion to the high frequency detail of the high frequency sub-band coefficient after being decomposed with wavelet, then carries on correspondent wavelet reverse conversion to the sub-band coefficient and low frequency coefficient, and acquires the new intensity component I', then the fused image may be acquired through HIS reverse conversion. The invention combines the character of HIS fusion method and wavelet fusion method, it realizes high space resolution, and it reduces the deformation of color greatly, and it improves the spectrum information index of fused image.

Description

Image optimization fusion method based on fuzzy integral

Technical field

The present invention relates to a kind of image optimization fusion method based on fuzzy integral, decompose time-frequency characteristic and IHS (intensity I ntensity-colourity Hue-saturation degree Saturation) the conversion fusion method that has in conjunction with the wavelet multiresolution rate, utilize two single factor indexs of comprehensive spectral information of fuzzy integral and spatial resolution, carrying out remote sensing image optimization merges, effectively improve the spectral information index that merges image, in fields such as all kinds of military or civilian Remote Sensing Information Processing System, digital city space information system, all can be widely used.

Background technology

The multispectral remote sensing image of panchromatic remote sensing image of effective fusion high resolving power and low resolution, two characteristic indexs of space detailed information and spectral information in the balanced fusion results are one of multi-source remote sensing visual fusion Study on Technology focuses.

The IHS fusion method that people such as Haydn at first propose is one of classical practical algorithm.This method with multispectral image by the IHS conversion from RGB (red Red-green Green-blue Blue) spatial alternation to the IHS space, simultaneously high-resolution panchromatic image is carried out linear stretch, the feasible average of back image and the strength component I in variance and the IHS space of stretching ₀Consistent.Then, the high resolution image after stretching as new strength component, is arrived former rgb space with H and S component according to IHS inverse transformation fortran.Like this, make that the image after merging had both had higher spatial resolution, kept former low resolution multispectral image identical colourity and saturation degree simultaneously again.Yet, the IHS fusion method of this classics exists certain defective, because the data of different-waveband have different spectral patterns, the IHS fusion method has been twisted original spectral characteristic, produced spectrum degradation phenomena in various degree, thereby be unfavorable for the correct identification and the classification of image, particularly for the visual fusion of the multisensor remote sensing image of phase simultaneously not, the IHS fusion method can't make the tone of the former multispectral image of color harmony that merges image be consistent, it is this because the conversion of spectral information has caused image can not be used for atural object identification and inverting.People such as Te-Ming have carried out mathematical proof in the IHS space, have discussed the defective of IHS fusion method, and the conclusion that obtains is: although be used to replace strength component I ₀High-resolution panchromatic image I _NewBefore replacement, carried out the coupling of the statistical property of image, but matching error δ=I _New-I has caused colored distortion.

After utilizing the attention degree of fuzzy mearue sign to evaluation index, introducing fuzzy integral then can effectively comprehensive spectral information index and two single factor index of spatial resolution, on the basis of IHS fusion method and wavelet transformation fusion, fuzzy integral can conveniently be carried out image optimization and be merged.Have not yet to see the relevant method report that fuzzy integral is used for the image optimization fusion.

Summary of the invention

The objective of the invention is to deficiency at above-mentioned IHS conversion integration technology, provide a kind of remote sensing image to optimize fusion method, introduce fuzzy integral as comprehensive spectral information index and two single factor indexs of spatial resolution, can improve the spatial resolution that merges the back image, can reduce colored distortion again, effectively improve the spectral information index that merges image.

For realizing such purpose, the present invention is in the IHS space, the strength component of multispectral image is decomposed the low frequency base band coefficient that obtains through the small echo multilayer, carry out merging with the low frequency base band coefficient that high resolution image obtains through corresponding multilayer wavelet decomposition with the fuzzy optimization of spatial detail information and two characteristic indexs of spectral information, high-frequency sub-band coefficient after wavelet decomposition is carried out the high frequency minutia to be merged, then wavelet coefficient is carried out corresponding wavelet inverse transformation, obtain new strength component, the image after carrying out again obtaining merging after the IHS inverse transformation.

Because wavelet transformation has good frequency division characteristic at transform domain, the statistical property of wavelet coefficient has reflected the notable features such as edge, line and zone of remote sensing image, and the present invention introduces multiresolution analysis (Multi-resolution Analysis) method of wavelet transformation in the fusion of multispectral remote sensing image of panchromatic remote sensing image of high resolving power and low resolution.

Method of the present invention comprises following concrete steps:

1. the multispectral image B that treats fusion carries out the IHS conversion, obtains colourity H, saturation degree S and strength component I in the IHS color space respectively, then I component is carried out wavelet decomposition, obtains low frequency base band coefficient and high-frequency sub-band coefficient.

2. treat fusion high resolution image A and carry out linear stretch and histogram coupling, carry out wavelet decomposition then, obtain low frequency base band coefficient and high-frequency sub-band coefficient, the decomposition number of plies is identical with the wavelet decomposition number of plies of multispectral image I component.

3. determine one 3 * 3 spatial domain window, try to achieve the average μ (2 of the high-frequency sub-band coefficient of the high-frequency sub-band coefficient of I component of image B and image A respectively ^j) and variance D (2 ^j).

4. on corresponding layers of resolution, the high-frequency sub-band coefficient carries out the high frequency minutia according to (1) formula and merges:

(1) in the formula, 2 ^jBe the wavelet decomposition number of plies, W ^k(2 ^j, x is 2 y) ^jThe high-frequency sub-band coefficient fusion results that obtains under the resolution; W _A ^K(2j, x, y) and W _B ^K(2 ^j, x y) is respectively the pairing high-frequency sub-band coefficient of I component among image A and the image B, D _A ^K, D _B ^KBe respectively so that (x y) is the variance of 3 * 3 spatial domain window of center pixel.

5. to the I component of image B and the low frequency base band coefficient of image A, the Pixel-level that is optimized according to (2) formula merges the k of weight coefficient _OptAsk excellent according to spectral information evaluation index and spatial resolution evaluation index, ask the comprehensive spectral information index of excellent employing fuzzy integral and two single factor indexs of spatial resolution:

A(2 ^j，x，y)＝k ₁A _A(2 ^j，x，y)+k ₂A _B(2 ^j，x，y)??????????????(2)

(2) in the formula, A _A(2 ^j, x, y), A _B(2 ^j, x y) is respectively 2 of I component correspondence among image A and the image B ^jThe low frequency base band data of resolution, k ₁, k ₂For needs are asked excellent weight coefficient.According to normalization requirement, k ₁, k ₂Satisfy k ₁+ k ₂=1, promptly ask excellent Determination of Weight Coefficient can be summed up as the k that satisfies objective function _Opt=k ₁=1-k ₂Optimize the weight coefficient that merges in the iterative process and satisfy 0≤k _Opt≤ 1;

According to formula S=sup{min[e (u ₁), g (E ₁)], min[e (u ₂), g (E ₂)] the definite fuzzy integral value E of calculating optimizing evaluation index _i, try to achieve S _Kopt=max (E _i), S _KoptCorresponding iterative value k _OptBe optimum weight coefficient, in the formula, g (E ₁), g (E ₂) be attention degree to spectral information index and two indexs of spatial resolution, e (u ₁) be the spectral information index, e (u ₂) be the spatial resolution index, according to e (u ₁), e (u ₂) size, u ₁And u ₂Be to spectral information and spatial resolution sorting position from small to large.

6. the low frequency base band coefficient of the wavelet coefficient that the Pixel-level that obtains is merged, and each high frequency wavelet coefficient carries out corresponding wavelet inverse transformation, obtains new strength component I ';

7. I ', H, S are carried out the IHS inverse transformation, the image C after obtaining merging.

When the present invention introduces fuzzy integral spectral information index and two single factor indexs of spatial resolution is carried out pixel level optimization and merge:

Definition: establishing X is domain, and e is the measurable function from X to [0,1], A ∈ P (X), and then e defines as (3) formula about the fuzzy integral S of fuzzy mearue g on collection A:

$S=\underset{A}{\∫}e\left(x\right)\·g\left(x\right)=\underset{\mathrm{\α}\∈\left[\mathrm{0,1}\right]}{\sup \min }[\mathrm{\α},g(A\∩E_{\mathrm{\α}})]$

$=\underset{\mathrm{\α}\∈\left[\mathrm{0,1}\right]}{\max }\left[\min (\mathrm{\α},g(A\∩E_{\mathrm{\α}}))\right]--\left(3\right)$

Wherein, E _α={ x|e (x) 〉=α }, P (x) is the power set of X.G () is a fuzzy mearue.Utilize fuzzy integral to be optimized when merging, fuzzy mearue can characterize attention degree.

The key of utilizing fuzzy integral to be optimized fusion is the definition of fuzzy mearue g (x), can adopt g λ to estimate.At domain X={x ₁, x ₂, x ₃... x _n(set of factors) be under the condition of limited, in λ=0 o'clock, as long as determined single-point collection (single set of factors) { x _iG λ fuzzy mearue g λ (x _i), then can obtain estimating of any A X.Merge problem, domain X={x for multispectral, full resolution pricture ₁, x ₂, factor of evaluation has two, x ₁=spectral information, x ₂=spatial resolution.The attention degree is g λ (x ₁), g λ (x ₂), simple table is shown g ₁, g ₂, g ({ x then ₁)=g ₁, g ({ x ₂)=g ₂, g ({ x ₁, x ₂)=g ({ x ₁)+g ({ x ₂)=1.E (x) expression spectral information evaluation index and resolution evaluation index.Domain X relevant evaluating index is e (x ₁)=E _SP, e (x ₂)=E _HFSimple table is shown e ₁, e ₂According to the definition of fuzzy integral, can obtain (4) formula:

$\underset{X}{\∫}e\left(x\right)\·g\left(x\right)=\underset{\mathrm{\α}\∈\left[\mathrm{0,1}\right]}{\sup \min }[\mathrm{\α},g(X\∩E_{\mathrm{\α}})]--\left(4\right)$

$=\underset{\mathrm{\α}\∈\left[\mathrm{0,1}\right]}{\max }\left[\min (\mathrm{\α},g\left(E_{\mathrm{\α}}\right))\right]$

According to e ₁, e ₂Size, to x ₁And x ₂Ordering is designated as u by from small to large sorting position ₁And u ₂This moment, the value of α had two kinds of situations: as α=e (u ₁) time, E _α=E ₁={ u ₁, u ₂, g (E then ₁)=1; As α=e (u ₂) time, E _ε=E ₂={ u ₂, g (E then ₂)=g ({ u ₂).

According to the definition of fuzzy integral, can be expressed as (5) formula:

S＝sup{min[e(u ₁)，g(E ₁)]，min[e(u ₂)，g(E ₂)]}?????????????(5)

Work as e ₁＞e ₂The time, x ₁And x ₂Sorting position from small to large is u ₁=x ₂, u ₂=x ₁, e (u correspondingly ₁)=e ₂, g (E ₁)=1, e (u ₂)=e ₁, g (E ₂)=g ({ u ₂)=g ({ x ₁)=g ₁, fuzzy integral S=max (e then ₁G ₁, e ₂).

Work as e ₁＜e ₂The time, x ₁And x ₂Sorting position from small to large is u ₁=x ₁, u ₂=x ₂, e (u correspondingly ₁)=e ₁, g (E ₁)=1, e (u ₂)=e ₂, g (E ₂)=g ({ u ₂)=g ({ x ₂)=g ₂, fuzzy integral S=max (e then ₁, e ₂G ₂).

Work as e ₁=e ₂The time, any one in desirable top two values of S.

Merge iteration in optimization and ask for weight coefficient k _OptProcess in, utilize (5) formula to try to achieve fuzzy integral value about spectral information index and two single factor indexs of spatial resolution, try to achieve S again _Kopt=max (E _i), S _KoptCorresponding iterative value k _OptBe optimum weight coefficient, can conveniently determine optimum weights k like this _Opt

The present invention combines the characteristics of IHS fusion method and small echo fusion method, ask the high frequency minutia of excellent fusion and high-frequency sub-band coefficient to merge by respectively the weight coefficient of small echo base band coefficient having been carried out Pixel-level, its beneficial effect is presented as: make the image after merging both reach the highest spatial resolution, reduced colored distortion simultaneously again to greatest extent.Balanced space detailed information and two characteristic indexs of spectral information in the fusion results have effectively been improved the spectral information index that merges image.Simultaneously, introduced the fuzzy integral method, effectively comprehensive spectral information and two single factor indexs of spatial resolution, conveniently definite Pixel-level is asked the optimum weights in the excellent fusion process, and its result meets the people more to merging the subjective feeling of image.

Description of drawings

Fig. 1 is the present invention-based on the image optimization fusion method process flow diagram of fuzzy integral.

Fig. 2 is the contrast of remote sensing image fusion results of the present invention and IHS, WT (small echo) method.Wherein, Fig. 2 (a) is multispectral remote sensing image (256 * 256); Fig. 2 (b) is the panchromatic remote sensing image of high spatial resolution (256 * 256); Fig. 2 (c) is the fusion results of IHS method, and Fig. 2 (d) is the fusion results of WT method, Fig. 2 (e) optimization fusion results of the present invention.

Fig. 3 is a performance evaluation index curve of the present invention.

Embodiment

In order to understand technical scheme of the present invention better, be further described below in conjunction with accompanying drawing.

The detailed process of the inventive method as shown in Figure 1.The present invention chooses a multispectral remote sensing image B (256 * 256) as Fig. 2 (a), and the panchromatic remote sensing image A of high resolving power (256 * 256) behind the strict registration of A, B, implements following steps as Fig. 2 (b):

1, multispectral image B is carried out the IHS conversion, obtain colourity H, saturation degree S and strength component I respectively, then I component is carried out 3 layers of wavelet decomposition, obtain low frequency base band coefficient and high-frequency sub-band coefficient in the IHS color space.

2, high resolution image A is carried out linear stretch and histogram coupling, carry out wavelet decomposition then, decomposing the number of plies is 3 layers also, obtains low frequency base band coefficient and high-frequency sub-band coefficient.

3, determine one 3 * 3 spatial domain window, try to achieve the average μ (2 of the high frequency wavelet coefficient of the high-frequency sub-band coefficient of I component among the image B and image A respectively ^j) and variance D (2 ^j).

4, determine the high-frequency sub-band coefficient of corresponding layers of resolution according to following formula, carry out the high frequency minutia and merge, can obtain the high frequency minutia fusion results of corresponding layers of resolution.

5, introducing fuzzy integral, that spectral information index and two single factor indexs of spatial resolution are carried out comprehensive method is as follows: according to following formula

S＝sup{min[e(u ₁)，g(E ₁)]，min[e(u ₂)，g(E ₂)]}

Calculate the fuzzy integral value E that the optimizing evaluation index is determined _i, try to achieve S _Kopt=max (E _i), S _KoptCorresponding iterative value k _OptBe optimum weight coefficient.

Wherein, fused images optimizing evaluation index is defined as follows respectively:

(i) spectral information evaluation index

Utilize the degree of correlation of fused images and multispectral image to define the evaluation index of spectral information.

Make that f is a fused image, f ₀Be multispectral image.The definition spectral information is estimated E _SPIndex is as (6) formula.

$E_{\mathrm{SP}}=\mathrm{Corr}(f,f_0)=-\frac{{\mathrm{\Σ}}_{j=1}^{\mathrm{npix}}(f_j-\stackrel{\‾}{f})(f_{0j}-{\stackrel{\‾}{f}}_0)}{\sqrt{{\mathrm{\Σ}}_{j=1}^{\mathrm{npix}}{(f_j-\stackrel{\‾}{f})}^2{\mathrm{\Σ}}_{j=0}^{\mathrm{npix}}{(f_{0j}-{\stackrel{\‾}{f}}_0)}^2}}---\left(6\right)$

Wherein, npix is the number of pixel in the image, f and f ₀The gray average of presentation video, degree of correlation Corr (f, f ₀) reflected image f and f ₀Similarity degree.

(ii) spatial resolution evaluation index

Utilize the high fdrequency component and the degree of correlation between the high-definition picture high fdrequency component of the gray scale of fused images correspondence to come definition space resolution index.Make f _HBe the high resolving power panchromatic image.At first the image that merges is converted into gray level image, carries out wavelet decomposition then, obtain four component (f of fused images ^a, f ^h, f ^v, f ^d), represent the low frequency component of fused images, the high fdrequency component of horizontal direction, the high fdrequency component of vertical direction and the high fdrequency component of diagonal respectively.Equally also can obtain four component (f of high-definition picture wavelet decomposition _H ^a, f _H ^h, f _H ^v, f _H ^d).Definition space resolution evaluation index is shown in (7) formula:

$E_{\mathrm{HF}}=\frac{\mathrm{Corr}(f^h,f_H^h)+\mathrm{Corr}(f^v,f_H^v)+\mathrm{Corr}(f^d,f_H^d)}{3}--\left(7\right)$

In order to verify the validity of the optimum weights that calculate based on fuzzy integral, can carry out Pixel-level according to (8) formula and merge weight coefficient k _OptAsk excellent according to the objective function shown in (8) formula:

Weight coefficient k according to the fusion of the base band data in the optimizing evaluation index correction fusion criterion of fused images ₁, k ₂Fusion weights k along with high resolution image low frequency base band coefficient ₁Increase, spatial resolution evaluation index E _HFIncrease spectral information evaluation index E thereupon _SPReduce.Therefore, the k in (6) formula _OptBe to make objective function reach maximum weight coefficient, promptly make E _SP, E _HFReach maximum simultaneously weight coefficient.k _OptSpan is [0,1].

According to weight coefficient k _OptAsk excellent objective function, in interval [0,1], along with weight coefficient merges weights k ₁Increase, obtain spatial resolution evaluation index E by (9) formula _HFWith spectral information evaluation index E _SPCurve, intersections of complex curve is the k of optimum weight coefficient _Opt, as shown in Figure 3.

$E\left(i\right)=\frac{E\left(i\right)-\mathrm{MinE}\left(i\right)}{\mathrm{Max}[E\left(i\right)-\mathrm{Min}(E\left(i\right)]}$ $\left(9\right)$

(9) in the formula, E (i) is an evaluation index, and i is weight coefficient k ₁The number of times of optimizing.Figure 3 shows that the E that obtains with optimizing step-length 0.001 in [0,1] _SP, E _HFCurve.Can see the E after the normalization _SP, E _HFAll be non-linear.Satisfy the weight coefficient k that asks excellent objective function _OptBe E _SPAnd E _HFIntersection point k _Opt=0.424 (convergence precision δ≤0.001).

As shown in Figure 3, obviously, introduce fuzzy integral spectral information index and two single factor indexs of spatial resolution are carried out comprehensive evaluation, calculate and try to achieve S _Kopt(S _Kopt=max (E _i)) pairing iterative value k _OptBe the E that tries to achieve by (9) formula _HF, E _SPIntersections of complex curve, be the k of optimum weight coefficient _Opt

6, the low frequency base band coefficient of the wavelet coefficient that the Pixel-level optimization that obtains is merged, and each high frequency wavelet coefficient that the feature level of carrying out merges carries out corresponding wavelet inverse transformation, obtains new strength component I '.

7, I ', H, S are carried out the IHS inverse transformation, the image C after obtaining merging.

Fusion results compares: Fig. 2 (c) is the fusion results (256 * 256) of IHS method, and Fig. 2 (d) is the fusion results (256 * 256) of WT method, Fig. 2 (e) optimization fusion results of the present invention (256 * 256).

In searching process, according to two characteristic evaluating indexs of (4)～(5) formula definition, substitution (5) formula, according to the value of per 100 step size computation fuzzy integral S, result (attention degree g as shown in table 1 ₁=0.7, g ₂=0.3).Simultaneously fuzzy integral value S is drawn as shown in Figure 3.Obviously, fuzzy integral value S presents a nonlinear change procedure, has a peak point, and this peak point promptly is the intersection point-optimum weight coefficient k of the index curve that will obtain after two characteristic index normalization _Opt, it makes the image after merging both reach the highest spatial resolution, has reduced colored distortion simultaneously again to greatest extent.

The evaluation index of table 1 fusion results relatively

Ask excellent step-length	Spectral information E SP	Spatial resolution E HF	Fuzzy integral S
				????K 1	??0.99323	??0.8869	??0.70000
????K 2	??0.99159	??0.90191	??0.69077
				????K 3	??0.98812	??0.91298	??0.67116
????K 4	??0.98237	??0.92122	??0.6998
				????K 5	??0.97384	??0.92718	??0.82135
????K 6	??0.96197	??0.93127	??0.74776
				????K 7	??0.94615	??0.93389	??0.62011
????K 8	??0.92579	??0.93535	??0.45575
				????K 9	??0.90032	??0.93594	??0.30000
????K 10	??0.86932	??0.93589	??0.29965

The variation of the spectral information shown in the table 1 and these two kinds of factor evaluation values of spatial resolution information meets people's subjective assessment impression.In other words, adopt the Fig. 2 (e) that obtains based on fuzzy integral image optimization fusion method to meet people's subjective assessment custom and subjective feeling.

The different fusion method results' of table 2 evaluation index relatively

	Wave band	Related coefficient	Average gradient
				??IHS	????R	????0.5624	????10.6146
????G	????0.4644	????11.3663
			????B		????0.5060	????9.5875
??WT	????R	????0.6457					????11.0157
			????G	????0.5512	????13.5479
	????B	????0.5714				????10.5447
			This paper method	????R	????0.8488		????10.5142
????G	????0.7691	????11.1475
				????B	????0.8247	????9.2163

The result that table 2 is estimated for the fusion image that adopts two index quantifications of related coefficient and average gradient that different fusion methods are obtained.As known from Table 2, the image optimization fusion method that employing the present invention is based on fuzzy integral can be greatly improved at the spectral information retention property of image so that merge the back image, but slightly be worse than wavelet transformation (WT) method aspect the raising of spatial resolution, and the spatial resolution index of the fusion image that obtains with the IHS method is basic identical.Therefore, the inventive method is when reaching the highest spatial resolution, has reduced colored distortion again to greatest extent.

Claims (1)

1, a kind of image optimization fusion method based on fuzzy integral is characterized in that comprising following concrete steps:

(1) the multispectral image B that treats fusion carries out the IHS conversion, obtains colourity H, saturation degree S and strength component I in the IHS color space respectively, then I component is carried out wavelet decomposition, obtains low frequency base band coefficient and high-frequency sub-band coefficient;

(2) treat fusion high resolution image A and carry out linear stretch and histogram coupling, carry out wavelet decomposition then, obtain low frequency base band coefficient and high-frequency sub-band coefficient, the decomposition number of plies is identical with the wavelet decomposition number of plies of multispectral image I component;

(3) determine one 3 * 3 spatial domain window, try to achieve the average μ (2 of the high-frequency sub-band coefficient of the high-frequency sub-band coefficient of I component of image B and image A respectively ^j) and variance D (2 ^j);

(4) the high-frequency sub-band coefficient of corresponding layers of resolution is according to following formula

Carry out the high frequency minutia and merge, in the formula, 2 ^jBe the wavelet decomposition number of plies, W ^k(2 ^j, x is 2 y) ^jThe high-frequency sub-band coefficient fusion results that obtains under the resolution, W _A ^k(2 ^j, x, y) and W _B ^k(2 ^j, x y) is respectively the corresponding high-frequency sub-band coefficient of I component among image A and the image B, D _A ^k, D _B ^kBe respectively so that (x y) is the variance of 3 * 3 spatial domain window of center pixel;

(5) to the I component of image B and the low frequency base band coefficient of image A, according to following formula

A (2 ^j, x, y)=k ₁A _A(2 ^j, x, y)+k ₂A _B(2 ^j, x, the Pixel-level that y) is optimized merges, in the formula, A _A(2 ^j, x, y), A _B(2 ^j, x y) is respectively 2 of I component correspondence among image A and the image B ^jThe low frequency base band data of resolution, k ₁, k ₂For needs are asked excellent weight coefficient, k ₁+ k ₂=1; Introduce fuzzy integral spectral information index and two single factor indexs of spatial resolution are carried out comprehensively, optimize the weight coefficient k that merges in the iterative process _Opt=k ₁=1-k ₂, satisfy 0≤k _Opt≤ 1; According to formula S=sup{min[e (u ₁), g (E ₁)], min[e (u ₂), g (E ₂)] the definite fuzzy integral value E of calculating optimizing evaluation index _i, try to achieve S _Kopt=max (E _i), S _KoptCorresponding iterative value k _OptBe optimum weight coefficient, in the formula, g (E ₁), g (E ₂) be attention degree to spectral information index and two indexs of spatial resolution, e (u ₁) be the spectral information index, e (u ₂) be the spatial resolution index, according to e (u ₁), e (u ₂) size, u ₁And u ₂Be to spectral information and spatial resolution sorting position from small to large;

(6) the low frequency base band coefficient of the wavelet coefficient that the Pixel-level optimization that obtains is merged, and each high frequency wavelet coefficient that the feature level of carrying out merges carries out corresponding wavelet inverse transformation, obtains new strength component I ';

(7) I ', H, S are carried out the IHS inverse transformation, the image C after obtaining merging.

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