CN100410684C - Remote sensing image fusion method based on Bayes linear estimation - Google Patents

Abstract

The present invention belongs to the technical field of the remote sensing image processing operation. More specifically, the present invention relates to a remote sensing image merging method based on the statistic parameter estimate. The method introduces an observation module between a high resolution ratio multi-spectral image and a low resolution ratio multi-spectral image, and the observation module between the high resolution ratio multi-spectral image and a full-color image. Then, the two observation modules are established into a Bayesian linear model. The estimate of the high resolution ratio multi-spectral image is obtained on the meaning of the linear minimum mean square error by using the Bayesian Garss-Markov theorem. The present invention not only can enhance the special details, but also can well keep the spectrum characteristics. The performance of the present invention is better than a traditional HIS method, a PCA method, a minimum wave conversion method, existing Nishii and Hardie methods which are based on the estimate of the statistic parameters. The new method provides the novel effective technical support for improving the visual judgment precision of the remote sensing image and enhancing the information definition and reliability.

Description

Remote sensing image fusion method based on Bayes's Linear Estimation

Technical field

The invention belongs to technical field of remote sensing image processing, be specifically related to a kind of remote sensing image fusion method based on Bayes's Linear Estimation.

Background technology

Because the restriction of remote sensor design, remote sensing images are generally traded off between spatial resolution and spectral resolution, and the image with high spectral resolution does not generally possess high spatial resolution, and vice versa.What for example, Landsat ETM+ sensor provided is exactly the multi light spectrum hands image of 6 width of cloth 30m spatial resolutions and the panchromatic wave-band image of a width of cloth 15m spatial resolution.In actual applications, those images that have high spatial resolution and high spectral resolution simultaneously can improve the precision of decipher and classification effectively, therefore the fusion of the remote sensing images of different resolution becomes the multispectral image of focus, especially low resolution of research and the fusion of high-resolution panchromatic wave-band image.In general, the image after the fusion had both required to keep the spectral characteristic of multispectral image, required to incorporate the spatial information of full-colour image again.

Common fusion method has the HIS method at present ^[1], the PCA method ^[2,3]And small wave converting method ^[4,5]HIS method and PCA method usually change the spectral characteristic of former multispectral image significantly, and small wave converting method is relatively more responsive for the selection of decomposing level and wavelet basis, and can be because of operating personnel's difference, and different syncretizing effects is arranged.

In recent years, based on the statistical parameter estimation approach ^[6-9]Begin to be applied in the fusion of remote sensing images.Nishii etc. ^[8]Suppose the probability distribution obedience associating Gaussian distribution of high-resolution multi-spectral image and full-colour image, use conditional mean as estimation.Hardie etc. ^[9]On the basis of above-mentioned associating Gaussian distribution hypothesis, introduce the observation model between high-resolution multi-spectral image and the low resolution multispectral image, obtained the estimation on maximum a posteriori probability (MAP) meaning.Nishii method and Hardie method are difficult to incorporate the spatial information of full-colour image when low resolution multispectral image and full-colour image correlativity are not strong.

At above problem, in the research of remote sensing image fusion, when strengthening spatial detail, keep spectral characteristic well, and guarantee that the robustness of algorithm becomes the focus of present research.

Summary of the invention

The objective of the invention is to propose a kind of remote sensing image fusion method, traditional depend on the problem of multispectral image and full-colour image related coefficient, strengthen spatial detail, keep spectral characteristic based on the statistical parameter method of estimation to solve based on Bayes's Linear Estimation.

The remote sensing image fusion method that the present invention proposes based on Bayes's Linear Estimation, concrete steps are as follows:

Introduce the observation model between high-resolution multi-spectral image and the low resolution multispectral image, and the observation model between high-resolution multi-spectral image and the full-colour image, and above-mentioned two observation model simultaneous are become Bayes's linear model; Use Bayes Gauss-Markov theorem, calculate the estimation of the high-resolution multi-spectral image on linear minimum mean-squared error (LMMSE) meaning.

Below each step is further described in detail.

1, introduces observation model

Suppose that areal is photographed by the multi light spectrum hands sensor of low resolution and high-resolution panchromatic wave-band sensor respectively, so-called image resolution ratio is meant the scope on each pixel covering ground in the image, the height of image resolution ratio is a relative notion, can specifically divide as required.Full-colour image is arranged in the column vector of one dimension in the following manner

x＝[x ₁，x ₂，…，x ₁，…，x _N] ^T(1)

X wherein _iThe pixel value of expression full-colour image on the i of locus, and N is the number of pixels of full-colour image.The multispectral image of low resolution also is arranged in the column vector of one dimension in a comparable manner

$y={[y_1^T,y_2^T,\·\·\·,y_j^T,\·\·\·,y_M^T]}^T---\left(2\right)$

Y wherein _jThe pixel value of multispectral image on the j of locus of expression low resolution (has K wave band, y _j=[y _{J, 1}, y _J2..., y _JK] ^T), and M is the number of pixels of low resolution multispectral image.

Suppose that high-resolution multispectral image exists, it should both comprise the spectral information of multispectral image so, had the spatial resolution the same with full-colour image again, represented with following one dimension column vector here

$z={[z_1^T,z_2^T,\·\·\·,z_i^T,\·\·\·,z_N^T]}^T---\left(3\right)$

Z wherein _iRepresent that the pixel value of high-resolution multispectral image on the i of locus (has K wave band, z _i=[z _I1, z _{I, 2}..., z _{I, K}] ^T), N is the number of pixels of high-resolution multi-spectral image.

Generally, the multispectral image of low resolution can be thought to be obtained by low-pass filtering and down-sampled process by high-resolution multispectral image (if exist) ^[10]The present invention introduces the observation model between high-resolution multispectral image and the low resolution multispectral image, specific as follows shown in

y＝Hz+u(4)

Wherein u is a random noise, and its average is zero, and covariance matrix is C _u, and with z be incoherent; H matrix representation low-pass filtering and down-sampled process.

In addition, we introduce observation model as follows between full-colour image and high-resolution multi-spectral image:

x＝G ^Tz+v(5)

Wherein v is a random noise, and its average is zero, and covariance matrix is C _v, and with z be incoherent; The G matrix representation is done weighted mean to K wave band of high-resolution multi-spectral image, and weight factor is as follows

$g_l={\mathrm{cc}}_l/\underset{l=1}{\overset{k}{\mathrm{\Σ}}}\left|{\mathrm{cc}}_l\right|---\left(6\right)$

Cc wherein _lThe related coefficient of the l wave band of expression full-colour image and low resolution multispectral image.

Because equation number M * K+N that formula (4) and (5) provide is less than unknown quantity N * K to be estimated (general M＜N), therefore can not directly solve z.The present invention will derive to above-mentioned two observation models from the angle of Bayes's linear model, thereby obtain the estimator of high-resolution multi-spectral image z.

When estimating high-resolution multispectral image, observation model (4) can become Bayes's linear model according to following form simultaneous with (5)

$\left[\begin{array}{c}y\\ x\end{array}\right]=\left[\begin{array}{c}H\\ G^T\end{array}\right]z+\left[\begin{array}{c}u\\ v\end{array}\right]---\left(11\right)$

2, use Bayes Gauss-Markov theorem, carry out Linear Estimation

Tentation data is described by Bayes's linear model

Wherein

Be L * 1 data vector, A is known L * p observing matrix, and θ is the random vector of p * 1.The reality of θ will estimate that its average is E (θ), and covariance matrix is C _θW is the random vector of L * 1, and its average is zero, and covariance matrix is C _w, and with θ be incoherent.

At first suppose the estimator of θ

Can pass through data set

Try to achieve by following formula,

Select weighting coefficient a _jAnd b _iMake Bayes's square error (Bayesian mean square error, BMSE)

$\mathrm{BMSE}\left(\widehat{\mathrm{\θ}}\right)=E\left[{(\mathrm{\θ}-\widehat{\mathrm{\θ}})}^2\right]---\left(9\right)$

Minimum, the estimator that obtains are called linear minimum mean-squared error (Linear minimum mean square error, LMMSE) estimator (Bayes's Gauss-Markov theorem ^[11]), as follows

At this moment $\mathrm{BMSE}\left({\widehat{\mathrm{\θ}}}_i\right)={\left[{(C_{\mathrm{\θ}}+A^T{C}_w^{-1}A)}^{-1}\right]}_{\mathrm{ti}}.$

Because generally θ can not ideally be expressed as

Linear combination, so the LMMSE estimator is not best, but it is quite useful in practice, because it has separating of closed form, and only relevant with average and covariance.

For Bayes's linear model (11), the estimation that the LMMSE estimator in application (10) formula can obtain high-resolution multispectral image (makes n=[u here, ^T, v ^T] ^T)

$\hat{z}=E\left(z\right)+C_z{\left[\begin{array}{c}H\\ G^T\end{array}\right]}^T{\left(\begin{array}{c}\left[\begin{array}{c}H\\ G^T\end{array}\right]C_z{\left[\begin{array}{c}H\\ G^T\end{array}\right]}^T+C_n\end{array}\right)}^{-1}\left[\begin{array}{c}y-E\left(y\right)\\ x-E\left(x\right)\end{array}\right]---\left(12\right)$

Statistical parameter wherein is estimated as follows:

In formula (12), to obtain estimator

, need know the average E (z) and the covariance C of high-resolution multi-spectral image _zHere suppose that between the pixel of high-resolution multi-spectral image be separate, so the average of entire image and covariance can be made of the average and the covariance of each pixel ^[12], specific as follows shown in:

E(z)＝[E(z ₁) ^T，E(z ₂) ^T，…，E(z _i) ^T，…，E(z _N) ^T](13)

Wherein E (z) is to use the multispectral image after the bilinear interpolation (B), is specially:

E(z)＝B(y)(15)

In order to estimate C _z, we with Vector Quantization algorithm to above-mentioned vector E (z ₁) classify according to Euclidean distance, calculate the covariance matrix of each class vector set, and its covariance matrix as each vector correspondence in the class.

In addition, the estimation of the E (y) in the formula (12) is by obtaining interpolation image low-pass filtering and down-sampled (H),

E(y)＝H(E(z))＝H(B(y))(16)

And the estimation of E (x) is by the panchromatic wave-band image being carried out low-pass filtering and down-sampled, and then bilinear interpolation obtains,

E(x)＝B(H(x))(17)

In actual calculation, if the covariance matrix dimension is bigger, the inversion operation in (12) formula will produce difficulty, and therefore, in this case, the present invention becomes many image fritters to estimate image segmentation again.

Above-mentioned Bayes's Linear Estimation method for the wave band number of image before merging without limits, the wave band number of the multispectral image y of low resolution can be greater than 3, and high-resolution full-colour image both can be single band also can be multiwave.Therefore, Bayes's Linear Estimation method both had been applicable to the fusion of multiwave multispectral image and single-range full-colour image, also was applicable to the fusion of multiwave HYPERSPECTRAL IMAGERY and multiwave multispectral image.

Description of drawings

Fig. 1 is the geometric interpretation based on the remote sensing image fusion method of Bayes's Linear Estimation.

Fig. 2 is full-colour image and the Multispectral Image Fusion experimental result of Landsat ETM+.Wherein, Fig. 2 (a) is the multispectral image of 30m spatial resolution, Fig. 2 (b) is the full-colour image of 30m spatial resolution, Fig. 2 (c) is the multispectral image of 120m spatial resolution, and Fig. 2 (d) is the HIS method, and Fig. 2 (e) is the PCA method, Fig. 2 (f) is a small wave converting method, Fig. 2 (g) is the Nishii method, and Fig. 2 (h) is the Hardie method, and Fig. 2 (i) is a bayes method

Fig. 3 is the Multispectral Image Fusion experimental result of full-colour image and the TM of SPOT.Wherein, Fig. 3 (a) is the multispectral image of 30m spatial resolution, Fig. 3 (b) is the full-colour image of 30m spatial resolution, Fig. 3 (c) is the multispectral image of 150m spatial resolution, and Fig. 3 (d) is the HIS method, and Fig. 3 (e) is the PCA method, Fig. 3 (f) is a small wave converting method, Fig. 3 (g) is the Nishii method, and Fig. 3 (h) is the Hardie method, and Fig. 3 (i) is a bayes method

Embodiment

By the following examples, further each composition in the invention is described.

1 is provided with observation model

The multispectral image of low resolution can be thought to be obtained by low-pass filtering and down-sampled process by high-resolution multispectral image (if exist) ^[10], this observation model is as follows:

y＝Hz+u(4)

Wherein u is a random noise, and its average is zero, and covariance matrix is C _u, and with z be incoherent; H matrix representation low-pass filtering and down-sampled process.

Between full-colour image and high-resolution multi-spectral image, exist observation model as follows:

x＝G ^Tz+v(5)

Wherein v is a random noise, and its average is zero, and covariance matrix is C _v, and with z be incoherent; The G matrix representation is done weighted mean to K wave band of high-resolution multi-spectral image, and weight factor is as follows:

$g_l={\mathrm{cc}}_l/\underset{l=1}{\overset{K}{\mathrm{\Σ}}}\left|{\mathrm{cc}}_l\right|---\left(6\right)$

Cc wherein _lThe related coefficient of the l wave band of expression full-colour image and low resolution multispectral image.

2 get in touch into Bayes's linear model with observation model

When estimating high-resolution multispectral image, observation model (4) can become Bayes's linear model according to following form simultaneous with (5)

$\left[\begin{array}{c}y\\ x\end{array}\right]=\left[\begin{array}{c}H\\ G^T\end{array}\right]z+\left[\begin{array}{c}u\\ v\end{array}\right]---\left(11\right)$

3 use Bayes's Linear Estimation

For Bayes's linear model (11), the estimation that the LMMSE estimator in application (10) formula can obtain high-resolution multispectral image (makes n=[u here, ^T, v ^T] ^T)

$\hat{z}=E\left(z\right)+C_z{\left[\begin{array}{c}H\\ G^T\end{array}\right]}^T{\left(\begin{array}{c}\left[\begin{array}{c}H\\ G^T\end{array}\right]C_z{\left[\begin{array}{c}H\\ G^T\end{array}\right]}^T+C_n\end{array}\right)}^{-1}\left[\begin{array}{c}y-E\left(y\right)\\ x-E\left(x\right)\end{array}\right]---\left(12\right)$

4 estimate statistical parameter

Suppose that between the pixel of high-resolution multi-spectral image be separate, so the average of entire image and covariance can be made of the average and the covariance of each pixel ^[12], as follows

E(z)＝[E(z ₁) ^T，E(z ₂) ^T，…，E(z _i) ^T，…，E(z _N) ^T](13)

Wherein E (z) is to use the multispectral image after the bilinear interpolation (B),

E(z)＝B(y)(15)

In order to estimate C _z, we with Vector Quantization algorithm to above-mentioned vector E (z _i) classify according to Euclidean distance, calculate the covariance matrix of each class vector set, and its covariance matrix as each vector correspondence in the class.

In addition, the estimation of the E (y) in the formula (12) is by obtaining interpolation image low-pass filtering and down-sampled (H),

E(y)＝H(E(z))＝H(B(y))(16)

And the estimation of E (x) is by the panchromatic wave-band image being carried out low-pass filtering and down-sampled, and then bilinear interpolation obtains, and is as follows

E(x)＝B(H(x))(17)

To said method of the present invention, carried out simulation calculation.Concrete simulated conditions is as follows:

(1) full-colour image of Landsat 7ETM+ and multispectral image;

(2) multispectral image of the full-colour image of SPOT and TM.

What Fig. 2 showed is multispectral image and the full-colour image of Landsat 7ETM+ sensor in the area, Shanghai of shooting on June 14th, 2000.Wherein, full-colour image has the spatial resolution of 15m, and multispectral image has the spatial resolution of 30m, here with the 3rd, 2 and 1 wave band respectively as the RGB passage.

Because Landsat 7 ETM+ do not provide the true multispectral image of 15m resolution to be used for comparison, so we are difficult to estimate the syncretizing effect that the whole bag of tricks obtains.In order to address this problem, we degenerate to 30m and 120m respectively with the spatial resolution of full-colour image and multispectral image.By above-mentioned degraded image is merged, and the multispectral image of fusion results and former 30m resolution is compared.

Fig. 3 has shown at the full-colour image of the SPOT satellite in the Hanoi area of shooting on October 26 nineteen ninety-five and the multispectral image (http://earth.esa.int/mcities/images/cases) of TM.The present invention with the full-colour image of SPOT from the resolution degradation of 10m to 30m, the multispectral image of TM from the resolution degradation of 30m to 150m.Here with the 3rd, 2 and 1 wave band of TM respectively as the RGB passage.

Method proposed by the invention will compare with following method:

(1) traditional HIS method, PCA method;

(2) small wave converting method is selected 4 rank Daubechies wavelet basiss for use, and the level of decomposition is 3 layers.

(3) Nishii method and Hardie method.

Experimental result is as follows:

1, the full-colour image of Landsat 7ETM+ and multispectral image

Analyze from visual effect, changed the spectral characteristic of true multispectral image among Fig. 2 significantly based on the fused images of HIS method and PCA method, its syncretizing effect obviously is inferior to other image interfusion method, therefore in following quantitative comparison, no longer consider HIS method and PCA method based on statistical parameter.

Some spots have appearred in the water body on the right of small wave converting method causes.The fused images of Nishii method and Hardie method is very fuzzy.Here it is to be noted that the related coefficient of low resolution multispectral image and full-colour image is respectively 0.57,0.18 and-0.20.The fused images of bayes method relatively approaches real multispectral image, has not only strengthened spatial detail, and has kept the spectral characteristic of former multispectral image well.

We compare small wave converting method, Nishii method and Hardie method and bayes method quantitatively in the statistical parameter situation of change that strengthens on spatial detail and the maintenance spectral characteristic with the lower part.

In order to weigh the ability that said method strengthens spatial detail, we calculate the standard deviation (SDD) of error image on each wave band that multispectral image that fused images deducts 120m resolution obtains, and are as shown in table 1.Wherein first row have shown the SDD parameter of real multispectral image (30m resolution).In table 1, Nishii method and the Hardie method SDD parameter on each passage all is far smaller than the value of true picture, illustrates that Nishii method and Hardie method can not strengthen spatial detail in this experiment effectively.The SDD parameter of small wave converting method on each passage is identical, and this explanation small wave converting method depends primarily on the decomposition level, and the concrete condition on each passage is not handled targetedly.The SDD parameter of bayes method relatively approaches the value of true picture on each passage, this explanation estimates that by the covariance of high-resolution multi-spectral image it is rational deciding the way of the amplitude of fused images on each passage.

Table 1 the whole bag of tricks strengthens the statistical parameter (Landsat ETM+) of spatial detail

In order to weigh the ability that said method keeps spectral characteristic, we adopt following statistical parameter:

(1) Y-PSNR ^[9](PSNR) be used for weighing ratio between the error of the peak value of gradation of image and two width of cloth images, be defined as follows:

PSNR＝20×log ₁₀(b/rms)(20)

The peak value of b presentation video gray scale wherein, rms is the error mean square root of two width of cloth images, the unit of Y-PSNR is db.General Y-PSNR is big more, and the difference between two width of cloth images is more little.

(2) related coefficient (CC) is defined as follows:

$C(f,g)=\frac{\mathrm{\Σ}[(f(i,j)-\stackrel{\‾}{f})\×(g(i,j)-\stackrel{\‾}{g})]}{\sqrt{\mathrm{\Σ}\left[{(f(i,j)-\stackrel{\‾}{f})}^2\right]\×\mathrm{\Σ}\left[{(g(i,j)-\stackrel{\‾}{g})}^2\right]}}---\left(21\right)$

Wherein f (i, j) and g (f and g are the averages of image for i, the j) gray scale of presentation video.General related coefficient is high more, and two width of cloth images are similar more.Y-PSNR and related coefficient are calculated respectively on each wave band of fused images and real multispectral image.

(3) relative global error ^[5](ERGAS) as follows, the RASE parameter is low more, and the degreeof tortuosity of spectrum is more little.

$\mathrm{ERGAS}=100\frac{h}{l}\sqrt{\frac{1}{K}\underset{k=l}{\overset{K}{\mathrm{\Σ}}}\sqrt{\mathrm{rms}\left(K\right)/\mathrm{mz}\left(K\right)}}---\left(22\right)$

Wherein, l and h are before merging and merge the resolution (getting 120m and 30m here respectively) of back multispectral image, rms (K) expression fused images and the real error mean square root of multispectral image on each wave band, mz (K) is the average of real multispectral image on each wave band.General relative global error is more little, and it is good more to illustrate that the spectral characteristic of fused images keeps.

Above-mentioned statistical parameter is as shown in table 2.Bayes method all has the highest Y-PSNR on each passage, related coefficient with maximum, and global error is minimum relatively, the fused images of bayes method and the difference minimum between the real multispectral image are described, so bayes method is better than small wave converting method, Nishii method and Hardie method in the maintenance of spectral characteristic.

Table 2 the whole bag of tricks keeps the statistical parameter (Landsat ETM+) of spectral characteristic

2, the multispectral image of the full-colour image of SPOT and TM

Analyze from visual effect, HIS method among Fig. 3 and PCA method have changed the spectral characteristic of multispectral image significantly, and especially the PCA method is with the color conversion in river.The fused images of Nishii method and Hardie method is fuzzyyer.Here, the related coefficient of low resolution multispectral image and full-colour image is respectively 0.52,0.35 and 0.29.The syncretizing effect of small wave converting method and bayes method compares better.

In table 3, the SDD parameter of Nishii method and Hardie method is all very little, illustrates that they are very limited really for the raising of spatial resolution.Small wave converting method has identical SDD parameter on each passage, still the concrete condition on the different passages is not done to handle targetedly.The value of the SDD parameter of bayes method on each passage and true multispectral image much at one.

In addition, in table 4, bayes method all has the highest Y-PSNR and maximum related coefficient on each passage, and global error is minimum relatively, fused images and the difference between the real multispectral image that bayes method is described are less, therefore, performance that we can say bayes method is better than small wave converting method, Nishii method and Hardie method.

Table 3 the whole bag of tricks strengthens the statistical parameter (SPOT and TM) of spatial detail

Table 4 the whole bag of tricks keeps the statistical parameter (SPOT and TM) of spectral characteristic

In sum, bayes method has solved the problem that Nishii method and Hardie method depend on the related coefficient of multispectral image and full-colour image, and experimental result has proved that the fusion performance of bayes method obviously is better than HIS method, PCA method and small wave converting method simultaneously.

At last, it is worthy of note certain methods such as small wave converting method especially, because parameter setting and operating personnel's is different, can cause the fusion results difference, and method proposed by the invention parameter will be provided with automatically, and under the situation that does not need human intervention, still syncretizing effect preferably can be obtained.

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Claims (3)

1. remote sensing image fusion method based on Bayes's Linear Estimation, it is characterized in that introducing the observation model between high-resolution multi-spectral image and the low resolution multispectral image, and the observation model between high-resolution multi-spectral image and the full-colour image, and above-mentioned two observation model simultaneous are become Bayes's linear model; Use Bayes Gauss-Markov theorem, calculate the estimation of the high-resolution multi-spectral image on the linear minimum mean-squared error LMMSE meaning.

2. the remote sensing image fusion method based on Bayes's Linear Estimation according to claim 1 is characterized in that: the observation model between described high-resolution multi-spectral image and the low resolution multispectral image is as follows:

y＝Hz+u (4)

Wherein u is a random noise, and its average is zero, and covariance matrix is C _u, and with z be incoherent; H matrix representation low-pass filtering and down-sampled process; Y is the column vector that the multispectral image of low resolution is lined up one dimension:

$y=[y_1^T,y_2^T,...,y_j^T,...,y_M^T{]}^T---\left(2\right)$

Y wherein _jThe pixel value of multispectral image on the j of locus of expression low resolution (has K wave band, y _j=[y _{J, 1}, y _J2..., y _JK] ^T), and M is the number of pixels of low resolution multispectral image; Z is the column vector that high-resolution multispectral image is lined up one dimension:

$z=[z_1^T,z_2^T,...,z_i^T...,z_N^T{]}^T---\left(3\right)$

Z wherein _iRepresent that the pixel value of high-resolution multispectral image on the i of locus (has K wave band, z _i=[z _{I, 1}, z _{I, 2}..., z _{I, K}] ^T), N is the number of pixels of high-resolution multi-spectral image;

Observation model between described high-resolution multi-spectral image and the full-colour image is as follows:

x＝G ^Tz+v (5)

Wherein v is a random noise, and its average is zero, and covariance matrix is C _v, and with z be incoherent; The G matrix representation is done weighted mean to K wave band of high-resolution multi-spectral image, and weight factor is as follows

$g_l={\mathrm{cc}}_l/\underset{l=1}{\overset{K}{\mathrm{\Σ}}}\left|{\mathrm{cc}}_l\right|---\left(6\right)$

Cc wherein _lThe related coefficient of the l wave band of expression full-colour image and low resolution multispectral image; X is the n dimensional vector n that full-colour image is lined up:

x＝[x ₁，x ₂，…，x _i，…，x _N] ^T (1)

X wherein _iThe pixel value of expression full-colour image on the i of locus, and N is the number of pixels of full-colour image; Observation model (4) becomes Bayes's linear model with (5) according to following form simultaneous

$\left[\begin{array}{c}y\\ x\end{array}\right]=\left[\begin{array}{c}H\\ G^T\end{array}\right]z+\left[\begin{array}{c}u\\ v\end{array}\right]---\left(11\right).$

3. the remote sensing image fusion method based on Bayes's Linear Estimation according to claim 2 is characterized in that the estimator of high-resolution multi-spectral image is as follows:

${\hat{z}{=E\left(z\right)+C}_z\left[\begin{array}{c}H\\ G^T\end{array}\right]}^T{(\left[\begin{array}{c}H\\ G^T\end{array}\right]C_z{\left[\begin{array}{c}H\\ G^T\end{array}\right]}^T+C_n)}^{-1}\left[\begin{array}{c}y-E\left(y\right)\\ x-E\left(x\right)\end{array}\right]---\left(12\right)$

Wherein, E (z) and C _zBe respectively the covariance of the average of high-resolution multi-spectral image, they are made up of the average and the covariance of each pixel, specific as follows shown in:

E(z)＝[E(z ₁) ^T，E(z ₂) ^T，…，E(z _i) ^T，…，E(z _N) ^T] (13)

Wherein E (z) is to use the multispectral image after the bilinear interpolation (B), is specially:

E(z)＝B(y) (15)

In order to estimate C _z, with Vector Quantization algorithm to above-mentioned vector E (z _i) classify according to Euclidean distance, calculate the covariance matrix of each class vector set, and its covariance matrix as each vector correspondence in the class;

The estimation of E (y) in the formula (12) is by obtaining interpolation image low-pass filtering and down-sampled (H):

E(y)＝H(E(z))＝H(B(y)) (16)

And the estimation of E (x) is by full-colour image being carried out low-pass filtering and down-sampled, and then bilinear interpolation obtains:

E(x)＝B(H(x)) (17)。

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