CN114331936A - Remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm - Google Patents
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Abstract
The invention provides a remote sensing image fusion method based on wavelet decomposition and an improved IHS algorithm, which comprises the following steps: extracting I components of the multispectral image and the high-resolution remote sensing image, and obtaining a fusion I through high-pass filtering and weighted average algorithm processing after fusionnew1Component, will fuse Inew1Histogram matching of component and panchromatic image to obtain matched fusion Inew2Component based on fusion Inew2Component, etc. are processed by HIS inverse transformation to obtain multispectral image MI _2, and then a brand new fusion is obtained by using wavelet decomposition methodAnd (5) imaging. The invention solves the problem of spectrum distortion of the fused image caused by IHS transformation, not only effectively inhibits the phenomenon of spectrum degradation, but also increases the definition of the fused image; meanwhile, the problem of image brightness reduction caused by high-pass filtering processing is effectively solved, and low-frequency information of the image, namely the overall brightness of the image, can be enhanced.
Description
Technical Field
The invention relates to the field of remote sensing information processing, in particular to a remote sensing image fusion method based on wavelet decomposition and an improved IHS algorithm.
Background
In recent years, explosive growth of remote sensing image data, production of high-performance computers and the like provide a basis for fusion of multivariate data, fusion of the remote sensing data provides possibility that multispectral and high resolution cannot be achieved simultaneously by a single satellite image, and good basis is provided for small-area ground feature identification, crop monitoring, fine agriculture monitoring and the like.
At present, the remote sensing image fusion mainly adopts methods such as IHS transformation, Brovery transformation, principal component transformation and the like to fuse multispectral images and panchromatic images. However, existing fusion multispectral methods exist: the spectrum distortion of the fused image, the low definition of the fused image and the like.
In addition, the historical remote sensing images of each age are used as the basis for monitoring the geographical national conditions, and play a key role in researching coastline change, urban and rural proportion change, land utilization change, vegetation change, water system change and the like; for the historical remote sensing image, besides the above problems, the existing fusion multispectral method also has the following problems: and the brightness of the fused image is low.
In order to solve the above problems, people are always seeking an ideal technical solution.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a remote sensing image fusion method based on wavelet decomposition and an improved IHS algorithm.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm, which is characterized by comprising the following steps:
acquiring an original multi-spectral image MI _0, an original high-resolution image HRI _0 and an original panchromatic image PI _0 in the same region;
resampling the original multispectral image MI _0 to obtain a target multispectral image MI _ 1; the target multispectral image MI _1 and the original high-resolution image HRI _0 have the same resolution;
IHS transformation is carried out on the target multispectral image MI _1 to obtain multispectral IhComponent, multispectral HhComponent and multispectral ShA component;
performing IHS transformation on the original high-resolution image HRI _0 to obtain high-resolution ImA component;
for the multispectral IhComponent and said high resolution ImThe components are processed by high-pass filtering to obtain a fusion IFA component;
for the fusion IFComponent and said high resolution ImThe components are weighted and averaged to obtain a fusion Inew1A component;
generating a new panchromatic image PI _1 according to the original panchromatic image PI _0 in a simulation mode, and performing fusion I on the fusion I based on the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2A component;
for the fusion Inew2Component, the multispectral HhComponent and said multispectral ShCarrying out IHS inverse transformation on the components to obtain a multispectral image MI _2 subjected to IHS inverse transformation;
performing wavelet transformation on the multispectral image MI _2 to obtain high-frequency components and low-frequency components of the multispectral image MI _ 2; performing wavelet transformation on the new panchromatic image PI _1 to obtain a high-frequency component and a low-frequency component of the new panchromatic image PI _ 1;
fusing the high-frequency component of the multispectral image MI _2 and the high-frequency component of the new panchromatic image PI _1 to obtain a fused high-frequency component; fusing the low-frequency component of the multispectral image MI _2 and the low-frequency component of the new panchromatic image PI _1 to obtain a fused low-frequency component;
and performing wavelet inverse transformation on the fused high-frequency component and the fused low-frequency component to obtain a fused remote sensing image.
The invention provides a remote sensing image fusion device based on wavelet decomposition and improved IHS algorithm, which comprises a memory, a processor and a remote sensing image fusion program based on wavelet decomposition and improved IHS algorithm, wherein the remote sensing image fusion program is stored in the memory and can run on the processor, and when being executed by the processor, the remote sensing image fusion program based on wavelet decomposition and improved IHS algorithm realizes the steps of the remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm.
A third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm as described above.
The invention has the beneficial effects that:
1) the invention can quickly and effectively fuse the remote sensing image by the multispectral IhComponent and said high resolution ImThe high-pass filtering processing is carried out on the components, the problem of spectrum distortion of the fused image caused by IHS transformation is solved, the improved method not only effectively inhibits the spectrum degradation phenomenon, but also increases the definition of the fused image;
then, the invention solves the problem of image brightness reduction caused by high-pass filtering processing by using histogram matching and wavelet transformation methods, can effectively enhance the overall brightness of the image, and simultaneously inhibits noise interference in high-frequency information;
2) the method has the advantages of simplicity, good fusion effect, suitability for remote sensing image fusion of ground feature extraction and the like.
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FIG. 1 is a first flow chart of a remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm of the present invention;
FIG. 2 is a second flow chart of the remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the following embodiments.
Example 1
As shown in fig. 1 and fig. 2, a remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm includes the following steps:
acquiring an original multi-spectral image MI _0, an original high-resolution image HRI _0 and an original panchromatic image PI _0 in the same region;
resampling the original multispectral image MI _0 to obtain a target multispectral image MI _ 1; the target multispectral image MI _1 and the original high-resolution image HRI _0 have the same resolution;
IHS transformation is carried out on the target multispectral image MI _1 to obtain multispectral IhComponent, multispectral HhComponent and multispectral ShA component;
performing IHS transformation on the original high-resolution image HRI _0 to obtain high-resolution ImA component;
for the multispectral IhComponent and said high resolution ImThe components are processed by high-pass filtering to obtain a fusion IFA component;
for the fusion IFComponent and said high resolution ImThe components are weighted and averaged to obtain a fusion Inew1A component;
generating a new panchromatic image PI _1 according to the original panchromatic image PI _0 in a simulation mode, and performing fusion I on the fusion I based on the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2A component;
for the fusion Inew2Component, the multispectral HhComponent and said multispectral ShCarrying out IHS inverse transformation on the components to obtain a multispectral image MI _2 subjected to IHS inverse transformation;
performing wavelet transformation on the multispectral image MI _2 to obtain high-frequency components and low-frequency components of the multispectral image MI _ 2; performing wavelet transformation on the new panchromatic image PI _1 to obtain a high-frequency component and a low-frequency component of the new panchromatic image PI _ 1;
fusing the high-frequency component of the multispectral image MI _2 and the high-frequency component of the new panchromatic image PI _1 to obtain a fused high-frequency component; fusing the low-frequency component of the multispectral image MI _2 and the low-frequency component of the new panchromatic image PI _1 to obtain a fused low-frequency component;
and performing wavelet inverse transformation on the fused high-frequency component and the fused low-frequency component to obtain a fused remote sensing image.
It should be noted that, aiming at the characteristics of more spectra but lower resolution of the multispectral image and the characteristics of higher spatial resolution but single waveband range of the high-resolution remote sensing image, the invention respectively carries out IHS transformation on the multispectral image, extracts the I components of the two images, and obtains a new I component (fusing the I components) by high-pass filtering and weighted average algorithm processing after fusionnew1Component) of the fusion I based on the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2A component; reuse of said fusion Inew2And replacing approximate components in the multispectral remote sensing image by the components, and then performing IHS inverse transformation, thereby improving HIS.
Specifically, the original multispectral image MI _0, the original high-resolution image HRI _0 and the original panchromatic image PI _0 are remote sensing images of the same area, taking an MODIS image as an example, the multispectral remote sensing image of a certain area is downloaded, and the panchromatic image and the high-resolution image are obtained through a google map and the like.
In one embodiment, the original multispectral image MI _0 is resampled by a bilinear interpolation method, which is an extension of a linear interpolation on a two-dimensional rectangular grid and is used to interpolate a bivariate function. The core idea is to perform linear interpolation in two directions, which is not described herein again.
Further, MI is performed on the target multispectral image1 IHS transform to obtain multiple spectrum IhComponent, multispectral HhComponent and multispectral ShWhen the components are calculated, the following formula is adopted:
wherein R ish、Gh、BhImage gray level matrixes of red, green and blue spectral bands of the target multispectral image MI _1 are respectively; multispectral IhThe components reflect the spatial details of the multispectral image MI _1 of the object, multispectral HhComponent and multispectral ShThe components reflect the spectral information, V, of the multispectral image MI _1 of the targeth1And Vh2Is an intermediate variable.
It can be understood that IHS transformation is performed on the original high resolution image HRI _0 to obtain high resolution ImIn component, the adopted method is similar, and the description of this embodiment is omitted here.
Further, for said multispectral IhComponent and said high resolution ImThe components are processed by high-pass filtering to obtain a fusion IFWhen the component is calculated, executing:
reading the multispectral IhComponent of obtaining said multi-spectrum IhThe gray value G (I) of each pixel (I, j) in the componenth(i,j))；
Reading the high resolution ImComponent of obtaining said high resolution ImThe gray value G (I) of each pixel (I, j) in the componentm(i,j)) And the mean value M (I) of the gray values of the M × n regions centered on the pixel (I, j)m(i,j,m,n))；
For the high resolutionImEach pixel (I, j) of the component is represented by said grey value G (I)m(i,j)) Subtracting the mean value M (I) of the gray valuesm(i,j,m,n)) Obtaining high frequency information Pn(Im(i,j)) (ii) a Wherein, the Pn(Im(i,j)) Indicating extracted high resolution ImHigh frequency information in each pixel (i, j) in the component;
for said multiple spectra IhFor each pixel (I, j) of a component, for said grey value G (I)h(i,j)) And the high frequency information Pn(Im(i,j)) Performing superposition processing to obtain the gray value G (I) of each pixel (I, j) of the filtered image in the spatial domainF(i,j))；
Filtering the grey value G (I) of each pixel (I, j) of the image in the spatial domainF(i,j)) To obtain a fusion IFAnd (4) components.
It will be appreciated that after traversing each pixel, a grey value G (I) is obtained for each pixel (I, j) of the filtered image in the domainF(i,j)) Obtaining a whole fused image component (fusion I)FComponent).
Note that for the multispectral IhComponent and said high resolution ImWhen the components are processed by high-pass filtering, high-resolution I is extractedmHigh frequency information (detail information) of the component; then, the extracted high-frequency information (detail information) is superposed to the multispectral I by adopting a pixel addition methodhOn the component (low-resolution image), the detail information of the high-resolution panchromatic image is added on the basis of keeping the multispectral image with low resolution as much as possible, so that the data fusion between the multispectral low-resolution image and the high-resolution panchromatic image is realized, the problem of image spectrum distortion after the fusion caused by IHS transformation is solved, the spectrum degradation phenomenon is effectively inhibited, and the definition of the fused image is increased.
In particular, for said multispectral IhFor each pixel (I, j) of a component, for said grey value G (I)h(i,j)) And the high frequency information Pn(Im(i,j)) Performing superposition processing to obtain the gray value G (I) of each pixel (I, j) of the filtered image in the spatial domainF(i,j)) Then, the following formula is adopted:
G(IF(i,j))＝G(In(i,j))+Pn(Im(i,j))
Pn(Im(i,j))＝G(Im(i,j))-M(Im(i,j，m，n))
wherein, G (I)F(i,j)) Represents the fusion IFThe gray value of each pixel (I, j) in the component, G (I)h(i,j)) Representing said multiple spectra IhGray value, P, of each pixel (i, j) in the componentn(Im(i,j)) Represents the high resolution ImHigh frequency information for each pixel (i, j) in the component;
G(Im(i,j)) Represents the high resolution ImThe gray value of each pixel (I, j) in the component, M (I)m(i,j,m,n)) Is expressed in the high resolution ImThe average of the gradations of m × n regions centered on the pixel (i, j) among the components.
Further, to the fusion IFComponent and said high resolution ImThe components are weighted and averaged to obtain a fusion Inew1When the component is calculated, executing:
using the following formula, fusion I is obtainednew1Component (b):
Inew1＝w1×Im+w2×IF
w2＝1-w1
wherein, Inew1Representing the multispectral IFComponent and said high resolution ImCarrying out weighted average processing on the components to obtain I components; i ismRepresenting an I component obtained by IHS transformation of the original high-resolution image HRI _ 0; i isFRepresenting the multispectral IhComponent and said high resolution ImCarrying out high-pass filtering on the component to obtain an I component; w is a1Represents a first weight, w2Representing a second weight.
Note that, for the fusion IFComponent and said high resolution ImThe components are weighted-averaged so as not to blend I with the componentsFThe components (source images) are subjected to any transformation, directly in fusion IFOn the basis of the component (source image), after the pixels are selected, averaged, weighted and averaged, a new image is obtained by fusion (fusion I)new1Component).
Specifically, the first weight w1Is said high resolution ImWeight of a component, the second weight w2Is the fusion IFThe weight of the component; the two images for which a typical weighted fusion is aimed are more equally well, so the sum of the weighting coefficients is 1. The invention considers that the spatial information carried by the high-resolution image has extremely high proportion, and through experiments, the w of the invention1＝1，w2＝0.2。
Specifically, when a new full-color image PI _1 is generated by simulation according to the original full-color image PI _0, the following formula is adopted:
wherein the PANnewImage gray matrix, R, representing the panchromatic band of the new panchromatic image PI _1p、Gp、BpThe image gray level matrix respectively represents the red, green and blue spectral bands of the original panchromatic image PI _0, the IR represents the image gray level matrix of the near-infrared spectral band of the original panchromatic image PI _0, and the PAN represents the image gray level matrix of the panchromatic spectral band of the original panchromatic image PI _ 0.
Further, the fusion I is carried out based on the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2When the component is calculated, executing:
based on the fusion Inew1Component, obtaining a fusion Inew1Image histogram of the components, generating said fusion Inew1Continuous probability density function p corresponding to image histogram of componentr(r); it is composed ofWherein r represents the fusion Inew1The gray scale of the image histogram of the component;
reading the image histogram of the new panchromatic image PI _1, and generating a continuous probability density function p of the new panchromatic image PI _1z(z), z represents a gray level of the histogram of the new full-color picture PI _ 1;
based on the continuous probability density function pr(r) generating an intermediate image gray level s; according to the probability density function pz(z) generating a transformation function g (z); obtaining the gray level z of the histogram of the new panchromatic image PI _1 and the fusion I according to the set relation of the transformation function G (z) ═ snew1Mapping relation between gray levels r of image histograms of the components;
fusing I based on the mapping relation pairnew1Replacing the gray value in the image histogram of the component to obtain a matched fusion Inew2And (4) components.
It should be noted that the fusion I is performed based on the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2A component; matched fusion Inew2Component elimination of the fusion Inew1The difference in illumination intensity between the component and the new panchromatic image PI _1 effectively solves the problem of image brightness reduction caused by high-pass filtering processing.
The calculation formula of the intermediate image gray level s is as follows:
wherein s represents the intermediate image gray level, and w is a false variable;
the transformation function g (z) is calculated as:
wherein t is a false variable;
the new panchromaticThe gray level z of the histogram of the image PI _1 and the fusion Inew1The mapping relationship between the gray levels r of the image histograms of the components is expressed as:
z＝G-1[T(r)]＝G-1(s)
obtaining the transformation relation from r to z based on the above formula, fusing I according to the inputnew1The image histograms of the components are histogram matched to obtain a fusion Inew2Component (new I component).
It will be appreciated that the fusion Inew2The components are images having gray values corresponding to the requirements of the particular probability density function.
Further, to the fusion Inew2Component, the multispectral HhComponent and said multispectral ShWhen the components are subjected to IHS inverse transformation, the following formula is adopted:
wherein R isMI_2、GMI_2、BMI_2The image gray level matrixes of the red, green and blue spectral bands of the multispectral image MI _2 are based on RMI_2、GMI_2、BMI_2And obtaining the multispectral image MI _2 after IHS inverse transformation.
Further, when the high-frequency component of the multispectral image MI _2 and the high-frequency component of the new panchromatic image PI _1 are fused to obtain a fused high-frequency component, the following steps are executed:
obtaining the high-frequency component C of the multispectral image MI _2 after N layers of wavelet decompositionN,HMI_2And the high-frequency component C after the wavelet decomposition of the N layers of the new panchromatic image PI _1N,HPI_1；
For the high frequency component CN,HMI_2And the high frequency component CN,HPI_1After weighted average, a fused high-frequency component C is obtainedN,HF。
Further, when the low-frequency component of the multispectral image MI _2 and the low-frequency component of the new panchromatic image PI _1 are fused to obtain a fused low-frequency component, the following steps are performed:
obtaining the low-frequency component C of the multispectral image MI _2 after N layers of wavelet decompositionN,LMI_2And a low-frequency component C of the new panchromatic image PI _1 after N layers of wavelet decompositionN,LPI_1；
For the low frequency component CN,LMI_2And said low frequency component CN,LPI_1After weighted average, obtaining a fused low-frequency component CN,LF。
In a specific embodiment, the multi-spectral image MI _2 and the new panchromatic image PI _1 are respectively subjected to wavelet decomposition to obtain high and low frequency components, and then subjected to image reconstruction to obtain a fused remote sensing image, which mainly includes:
(1) and selecting a proper wavelet basis and the number of decomposition layers, and performing multi-layer wavelet division on the multispectral image MI _2 and the new panchromatic image PI _1 (original image). Wavelet decomposition is carried out on an image by adopting a wavedec2 function in an MATLAB wavelet toolbox, and the calling format is [ c, s ] ═ wavedec2(X, N, 'Haar'), wherein X represents an original image signal, N represents N-layer decomposition of the signal, Haar is a mother function for carrying out wavelet transformation, c is decomposition coefficients of each layer, and s is the length of each decomposition coefficient;
(2) selecting fusion rules for wavelet coefficients
(a) Wavelet high-frequency coefficient fusion rule:
the fused high-frequency component is the weighted average of the high-frequency components after wavelet decomposition of the multispectral image MI _2 and the new panchromatic image PI _1, namely:
wherein, CN,HFRepresenting a high-frequency part of the multispectral image MI _2 and the new panchromatic image PI _1 after wavelet decomposition and weighted averaging of N layers, namely a fusion high-frequency component; cN,HMI_2Representing the high frequency component, C, of the multi-spectral image MI _2 after the wavelet decomposition of N layersN,HPI_1Representing the high-frequency components of the new panchromatic image PI _1 after N layers of wavelet decomposition;
(b) wavelet low frequency coefficient fusion rule
The fused low-frequency component is the weighted average of the low-frequency components after the multi-spectral image MI _2 and the new panchromatic image PI _1 are subjected to wavelet decomposition, namely:
wherein, CN,LFRepresenting the low-frequency part of the multispectral image MI _2 and the new panchromatic image PI _1 after N layers of wavelet decomposition and weighted average respectively, namely fusion low-frequency components; cN,LMI_2Represents the low-frequency component, C, of the multi-spectral image MI _2 after the wavelet decomposition of N layersN,LPI_1The low-frequency component after the N-layer wavelet decomposition of the new full-color image PI _1 is represented.
It should be noted that, on the highest decomposition level, wavelet coefficients of 3 directional high-frequency components of the multispectral image MI _2 and the new panchromatic image PI _1 are compared, and the wavelet coefficient with the largest absolute value is taken as the wavelet coefficient of the fused high-frequency component; on the intermediate decomposition layer, taking the wavelet coefficient (of the multispectral image MI _2 or the new panchromatic image PI _ 1) with the largest mean variance of the pixel central local area (taking 3 × 3 here) as the wavelet coefficient corresponding to the fused high-frequency component; thereby concentrating all information of the multispectral image MI _2 and the new panchromatic image PI _1 into a portion of wavelet coefficients having large amplitudes.
It can be understood that the multispectral image MI _2 and the new panchromatic image PI _1 in the present invention have rich high frequency components and high brightness and contrast, and the wavelet high frequency coefficient fusion rule is suitable for obtaining accurate fusion high frequency components.
And after determining the fused high-frequency component and the fused low-frequency component, performing inverse wavelet transform, and reconstructing an image based on the high-frequency part and the low-frequency part of the fused remote sensing image to obtain a final output remote sensing image.
Example 2
The embodiment provides a remote sensing image fusion device based on wavelet decomposition and improved IHS algorithm, which comprises a memory, a processor and a remote sensing image fusion program based on wavelet decomposition and improved IHS algorithm, stored in the memory and capable of running on the processor, wherein when the processor executes the remote sensing image fusion program based on wavelet decomposition and improved IHS algorithm, the steps of the remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm as in embodiment 1 are realized.
The present embodiment also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of wavelet decomposition and remote sensing image fusion based on the modified IHS algorithm as in embodiment 1.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the above-described modules is only one logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow in the method of the embodiments described above may be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention and not to limit it; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications to the specific embodiments of the invention or equivalent substitutions for parts of the technical features may be made; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.
Claims (8)
1. A remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm is characterized by comprising the following steps:
acquiring an original multi-spectral image MI _0, an original high-resolution image HRI _0 and an original panchromatic image PI _0 in the same region;
resampling the original multispectral image MI _0 to obtain a target multispectral image MI _ 1; the target multispectral image MI _1 and the original high-resolution image HRI _0 have the same resolution;
IHS transformation is carried out on the target multispectral image MI _1 to obtain multispectral IhComponent, multispectral HhComponent and multispectral ShComponent(s) of；
Performing IHS transformation on the original high-resolution image HRI _0 to obtain high-resolution ImA component;
for the multispectral IhComponent and said high resolution ImThe components are processed by high-pass filtering to obtain a fusion IFA component;
for the fusion IFComponent and said high resolution ImThe components are weighted and averaged to obtain a fusion Inew1A component;
generating a new panchromatic image PI _1 according to the original panchromatic image PI _0 in a simulation mode, and performing fusion I on the fusion I based on the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2A component;
for the fusion Inew2Component, the multispectral HhComponent and said multispectral ShCarrying out IHS inverse transformation on the components to obtain a multispectral image MI _2 subjected to IHS inverse transformation;
performing wavelet transformation on the multispectral image MI _2 to obtain high-frequency components and low-frequency components of the multispectral image MI _ 2; performing wavelet transformation on the new panchromatic image PI _1 to obtain a high-frequency component and a low-frequency component of the new panchromatic image PI _ 1;
fusing the high-frequency component of the multispectral image MI _2 and the high-frequency component of the new panchromatic image PI _1 to obtain a fused high-frequency component; fusing the low-frequency component of the multispectral image MI _2 and the low-frequency component of the new panchromatic image PI _1 to obtain a fused low-frequency component;
and performing wavelet inverse transformation on the fused high-frequency component and the fused low-frequency component to obtain a fused remote sensing image.
2. The wavelet decomposition and IHS algorithm-based remote sensing image fusion method according to claim 1, wherein the multispectral I is subjected tohComponent and said high resolution ImThe components are processed by high-pass filtering to obtain a fusion IFWhen the component is calculated, executing:
reading the multispectral IhComponent(s) ofObtaining said plurality of spectra IhThe gray value G (I) of each pixel (I, j) in the componenth(i,j))；
Reading the high resolution ImComponent of obtaining said high resolution ImThe gray value G (I) of each pixel (I, j) in the componentm(i,j)) And the mean value M (I) of the gray values of the M × n regions centered on the pixel (I, j)m(i,j,m,n)）；
For the high resolution ImEach pixel (I, j) of the component is represented by said grey value G (I)m(i,j)) Subtracting the mean value M (I) of the gray valuesm(i,j,m,n)) Obtaining high frequency information Pn(Im(i,j)) (ii) a Wherein, the Pn(Im(i,j)) Indicating extracted high resolution ImHigh frequency information in each pixel (i, j) in the component;
for said multiple spectra IhFor each pixel (I, j) of a component, for said grey value G (I)h(i,j)) And the high frequency information Pn(Im(i,j)) Performing superposition processing to obtain the gray value G (I) of each pixel (I, j) of the filtered image in the spatial domainF(i,j))；
Filtering the grey value G (I) of each pixel (I, j) of the image in the spatial domainF(i,j)) To obtain a fusion IFAnd (4) components.
3. The remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm according to claim 1, characterized in that for the fusion IFComponent and said high resolution ImThe components are weighted and averaged to obtain a fusion Inew1When the component is calculated, executing:
using the following formula, fusion I is obtainednew1Component (b):
wherein, Inew1Representing the multispectral IFComponent and said high resolution ImI component obtained after weighted average processing of components；ImRepresenting an I component obtained by IHS transformation of the original high-resolution image HRI _ 0; i isFRepresenting the multispectral IhComponent and said high resolution ImCarrying out high-pass filtering on the component to obtain an I component;w 1 a first weight is represented that is a function of,w 2 representing a second weight.
4. The remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm according to claim 1, wherein the fusion I is performed on the basis of the new panchromatic image PI _1new1Histogram matching is carried out on the image histograms of the components to obtain a matched fusion Inew2When the component is calculated, executing:
based on the fusion Inew1Component, obtaining a fusion Inew1Image histogram of the components, generating said fusion Inew1Continuous probability density function p corresponding to image histogram of componentr(r)；
Reading the new panchromatic image PI _1 and generating a continuous probability density function p of the new panchromatic image PI _1z(z)；
Based on the continuous probability density function pr(r) generating an intermediate image gray level s; according to the probability density function pz(z) generating a transformation function g (z); obtaining the gray level z of the histogram of the new panchromatic image PI _1 and the fusion I according to the set relation of the transformation function G (z) = snew1Mapping relation between gray levels r of image histograms of the components;
fusing I based on the mapping relation pairnew1Replacing the gray value in the image histogram of the component to obtain a matched fusion Inew2And (4) components.
5. The remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm according to claim 1, wherein when the high frequency component of the multispectral image MI _2 and the high frequency component of the new panchromatic image PI _1 are fused to obtain a fused high frequency component, the following steps are performed:
obtaining the multiple spectraHigh frequency component C of image MI _2 after N-layer wavelet decompositionN, HMI_2And the high-frequency component C after the wavelet decomposition of the N layers of the new panchromatic image PI _1N, HPI_1；
For the high frequency component CN, HMI_2And the high frequency component CN, HPI_1After weighted average, a fused high-frequency component C is obtainedN, HF。
6. The remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm according to claim 1, wherein when the low frequency component of the multispectral image MI _2 and the low frequency component of the new panchromatic image PI _1 are fused to obtain a fused low frequency component, the steps of:
obtaining the low-frequency component C of the multispectral image MI _2 after N layers of wavelet decompositionN, LMI_2And a low-frequency component C of the new panchromatic image PI _1 after N layers of wavelet decompositionN, LPI_1；
For the low frequency component CN, LMI_2And said low frequency component CN, LPI_1After weighted average, obtaining a fused low-frequency component CN, LF。
7. A remote sensing image fusion equipment based on wavelet decomposition and improved IHS algorithm is characterized in that: the remote sensing image fusion program based on wavelet decomposition and improved IHS algorithm stored on the memory and capable of running on the processor comprises a memory, a processor and a program, wherein the program is executed by the processor and realizes the steps of the remote sensing image fusion method based on wavelet decomposition and improved IHS algorithm according to any one of claims 1-6.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method for remote sensing image fusion based on wavelet decomposition and modified IHS algorithm as claimed in any one of claims 1-6.
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