CN113269688A - Method for removing sea flare of total-variation optical remote sensing image - Google Patents
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Abstract
The invention discloses a method for removing sea flare of a full-variational optical remote sensing image, which comprises the following steps: optical remote sensing degraded image to be contaminated by flare spotsXInputting a preset high-order texture fully-variable flare removing model, converting the solving problem of the high-order texture fully-variable flare removing model into a numerical optimization problem containing a plurality of sub-problems, solving the numerical optimization problem containing the plurality of sub-problems, and obtaining a pure remote sensing imageY(ii) a The high-order texture fully-variable flare removing model is an optical remote sensing degraded imageXAs a clean remote sensing imageYAnd a flare imageZIn the superimposed functional relationship ofX=Y+ZEstablishing a clean remote sensing image that facilitates distinguishing flare from background informationYAnd introducing high-order total variation terms of the texture to obtain the function expression. The invention realizes optical remote controlThe sea surface flare of the image is removed, any auxiliary data and wave band information are not needed, the flare can be effectively removed, and the ground object information is reserved.
Description
Technical Field
The invention relates to a remote sensing image processing technology, in particular to a method for removing sea surface flare of a full-variational optical remote sensing image.
Background
The remote sensing image provides abundant spectral information, and ground features can be better identified. However, remote sensing imaging usually requires specific conditions, such as sufficient sunlight or halogen light, and when data are collected outdoors, due to specular reflection formed by sunlight on seawater, image contrast is reduced, local scene information is lost, i.e., a flare phenomenon occurs, and therefore subsequent identification and analysis are affected, such as underwater ecological research and monitoring of water pollution conditions. Therefore, solving the problem of water surface flare interference becomes an important link.
The conventional method mainly removes flare by considering the relationship between wind speed, sea wave slope and flare intensity. This method requires the introduction of additional data to establish the probability density function of flare intensity, such as wind speed and sea wave slope, which is difficult to measure accurately in practice. In recent years, researchers have mainly removed flare based on an assumption that the intensity of the radiation in the near infrared band from water is zero and flare is not zero. And then, establishing a relation between the visible light and the near infrared band by designing various methods to achieve the purpose of removing flare spots.
For example, Multi spectral baseband using a simple physical based algorithm by D.R. Lyzenga et al (IEEE Trans. Geosci. Remote Sens.Vol.44, No. 8, pp. 2251-2259, aug.2006.) the relationship between visible light and near infrared band is established by means of covariance. J.A. Goodman et al, "infection of and sea-surface corrections on recovery of bottom depth and recovery using a semi-analytical model: a case study in Kaneohe bay, HawaiiAppl. Opt.Vol. 47, No. 28, pp. F1-F11, oct. 2008.) introduces an offset to correct each pel, which is obtained by using information of both bands 640nm and 750 nm. T. Kutser et al, "A sun glaze correction method for hyperspectral image containing areas with non-negligible water leaving NIR signalRemote Sens. Environ.Vol. 113, No. 10, pp. 2267 and 2274, 2009) oxygen absorption characteristics in the near infrared band to assess flare intensity. These methods can achieve better results in clean waters, but in complex scenarios, they often do not go wellAnd (5) removing flare spots.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problems in the prior art, the invention provides a method for removing the sea flare of the full-variation optical remote sensing image, which can remove the sea flare of the optical remote sensing image without using any auxiliary data and waveband information, converts the flare removal problem into an optimization solving problem, models the degraded image into a pure remote sensing image and a flare image, can effectively remove the flare and reserve the ground object information.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for removing sea flare of a full-variation optical remote sensing image comprises the following steps:
1) optical remote sensing degraded image to be contaminated by flare spotsXInputting a high-order texture fully-variant flare removal model shown in the following formula;
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,μandηin order to be the weight, the weight is,Z=X-Yin order to be a flare image,Xin order to optically remotely sense the degraded image,Z m as flare imagesZTo (1) amThe number of the wave bands is one,
for introducing higher-order fully-variant terms of texture, in whichZ m1First auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,Z 1 =DY-TandZ 2=GT，Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,D,Grespectively a first order and a second order difference operator,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band;
2) converting the solving problem of the high-order texture fully-variant flare removal model into a numerical optimization problem comprising a plurality of sub-problems;
optionally, before the step 1), a step of establishing a high-order texture fully-variant flare removal model is further included:
s1) introduction satisfies-S,T∈R 2|DY=S+TAuxiliary variables ofSAndTwhereinR 2Which represents a real number of the digital signal,Dfor the first order difference operator, the higher order fully variant term for introducing texture is determined as follows:
in the above formula, the first and second carbon atoms are,
to introduce the higher order fully variant terms of the texture,DYa first order difference representing a clean remote sensing image,S,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,S=DY-T，α 1andα 2in order to be a free parameter,Yin order to obtain a clean remote sensing image,Gin the form of a second-order difference operator,Nas flare imagesZThe size of the image of a single band of wavelengths,
as an auxiliary variableSThe mixed norm of (a) of (b),
second order difference as auxiliary variableGTA mixed norm of (d);
s2) degraded image of optical remote sensingXAs a clean remote sensing imageYAnd a flare imageZTo establish an optical remote sensing degraded imageXPure remote sensing imageYAnd flare imagesZFunctional relationship ofX=Y +ZCreating a convenient distinguishing blazeClean remote sensing image of spot and background informationYAnd introducing a high-order fully-variable partial term of the texture to obtain a high-order texture fully-variable flare removing model.
Optionally, the functional expression of the mixed norm is:
in the above formula, the first and second carbon atoms are,Sa calculation object representing a mixed norm,Mthe number of orders of the matrix is represented,Nas flare imagesZThe size of the image of a single band of wavelengths,s k+mN as objects of mixed normsSTo (1) ak+mStep oneNThe number of the elements is one,mis the number of matrix orders.
Alternatively, step S2) includes:
s2.1) degrading the image by optical remote sensingXAs a clean remote sensing imageYAnd a flare imageZTo establish an optical remote sensing degraded imageXPure remote sensing imageYAnd flare imagesZFunctional relationship ofX=Y +ZEstablishing a clean remote sensing image facilitating the distinction of flare and background informationYThe function expression of (a) is as follows:
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,μandηin order to be the weight, the weight is,Xin order to optically remotely sense the degraded image,Y m is a pure remote sensing imageYTo (1) amThe number of the wave bands is one,X m for remote sensing degraded imagesXTo (1) amThe number of the wave bands is one,
in order to perform the dot-product operation,
for introducing higher-order fully-variant terms of texture
To (1) amA plurality of wave bands;
s2.2) introducing a function expression of a high-order fully-variable component of the introduced texture into a pure remote sensing image convenient for distinguishing flare and background informationYA clean remote sensing image which will facilitate the distinction between flare and background informationYThe functional expression of (a) is rewritten as:
s2.3) introduction of auxiliary variablesZ=X-Y，Z 1 =DY-TAndZ 2=GTwhereinZ=X-YIn order to be a flare image,Xin order to optically remotely sense the degraded image,Yin order to obtain a clean remote sensing image,Z 1the first auxiliary variable of the high-order total variation term,Z 2a second auxiliary variable which is a high-order total variation term,D,Grespectively a first order difference operator and a second order difference operator to obtain a high-order texture fully-variant flare removing model shown in the formula (1).
Optionally, step 2) comprises:
2.1) determining the Lagrangian function of the higher-order texture fully variant flare removal model as follows:
in the above formula, the first and second carbon atoms are,L(Y,T,Z 1,Z 2,μ 1,μ 2,μ 3) In order to be a function of the lagrange,Yin order to obtain a clean remote sensing image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,μ 1,μ 2,μ 3in order to be a lagrange multiplier,μandηare two weights for the number of bits to be processed,Z=X-Yin order to be a flare image,Z m is the second of flare image ZmThe number of the wave bands is one,Z m1 first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZThe size of the image of a single band of wavelengths,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,D,Grespectively a first order and a second order difference operator,Tfor the introduction of a fillS,T∈R 2|DY=S+TAn auxiliary variable of };
2.2) converting the solving problem of the high-order texture fully-variant flare removal model into a solution including solving based on the Lagrangian function of the high-order texture fully-variant flare removal modelY、T、Z 1、Z 2AndZthe numerical optimization problem of the sub-problem of (1), whereinYIn order to obtain a clean remote sensing image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z 1to introduce the first auxiliary variable of the higher order fully variant terms of the texture,Z 2the second auxiliary variable of the high-order total variation item of the texture is introduced, and the parameters of other sub-problems are fixed and unchanged when a certain sub-problem is solved.
Optionally, the inclusion obtained in step 2.2) is solvedY、T、Z 1、Z 2AndZthe numerical optimization problem of the sub-problem of (1) includes: solving the solution shown in the formula (7)Y、TNumerical optimization of the sub-problem, solving the problem shown in equation (8)Z 1Numerical optimization of the sub-problem, solving the problem shown in equation (9)Z 2Numerical optimization of the sub-problem, solving the problem shown in equation (10)ZA numerical optimization problem of the sub-problem;
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,Z=X-Yin order to be a flare image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z 1the first auxiliary variable of the high-order total variation term,Z 2a second auxiliary variable which is a high-order total variation term,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,D,Grespectively a first order and a second order difference operator,μ 1,μ 2,μ 3in order to be a lagrange multiplier,μandηare two weights for the number of bits to be processed,Z m as flare imagesZTo (1) amThe number of the wave bands is one,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2 second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band.
Optionally, the method for solving the numerical optimization problem including the multiple sub-problems in step 3) is an alternating direction multiplier method.
Optionally, step 3) comprises:
3.1) number of initialization iterationsk；
3.2) updating separately according toZ 1 k+1、Z 2 k+1AndZ k+1；
in the above formula, the first and second carbon atoms are, Z 1 k+1is as followsk+1 iteration of the first auxiliary variable of the high-order total variable termZ 1，Ψ γ1AndΨ γ2is the intermediate variable(s) of the variable,Din order to be a first order difference operator,Y k+1is as followsk+1 iteration of clean remote sensing imagesY，Z 2 k+1Is as followskSecond auxiliary variable of high-order total variable component of +1 iterationZ 2，Z k+1Is as followsk+1 iterative flare imagesZ，μIn order to be the weight, the weight is,S,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band, max is a maximum function, sgn is a sign function,Xin order to optically remotely sense the degraded image,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,μ 1 k ,μ 2 k ,μ 3 k is composed ofμ 1,μ 2,μ 3First, thekValues of the sub-iterations, wherein:
in the above formula, the first and second carbon atoms are,αsgn is a sign function, max is a maximum function,γ 1andγ 2the function of is expressed as:
in the above formula, the first and second carbon atoms are,α 1andα 2in order to be a free parameter,ηin order to be the weight, the weight is,β 1in order to be a weight parameter, the weight parameter,Z k is as followskSub-iterative flare imageZ；
3.3) determining the number of iterationskWhether the image is equal to a preset threshold value or not, and if the image is equal to the preset threshold value, obtaining a pure remote sensing image finallyYOutputting as a final result, ending and exiting; otherwise, skipping to execute the next step;
updating the Lagrangian multiplier according toμ 1,μ 2,μ 3(ii) a Then the number of iterationskAdding 1, and skipping to execute the step 3.2);
in the above formula, the first and second carbon atoms are,μ 1 k+1,μ 2 k+1,μ 3 k+1is composed ofμ 1,μ 2,μ 3First, thekThe value of +1 iterations is then used,μ 1 k ,μ 2 k ,μ 3 k is composed ofμ 1,μ 2,μ 3First, thekThe value of the sub-iteration is,X k+1is as followsk+1 iteration optical remote sensing degraded imageX，Y k+1Is as followsk+1 iteration of clean remote sensing imagesY，Z k+1Is as followsk+1 iterative flare imagesZ，D,GRespectively a first order and a second order difference operator,T k+1is as followskAuxiliary variable for +1 iterationsT，Z 1 k+1Is as followsk+1 iteration of the first auxiliary variable of the high-order total variable termZ 1，Z 2 k+1Is as followskSecond auxiliary variable of high-order total variable component of +1 iterationZ 2，T k+1 、Z 1 k+1AndZ 2 k+1is as followsk+1 iteration auxiliary variables.
In addition, the invention also provides a system for removing the sea flare of the fully-variable optical remote sensing image, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the method for removing the sea flare of the fully-variable optical remote sensing image.
Furthermore, the present invention also provides a computer-readable storage medium having stored therein a computer program programmed or configured to execute the method for removing sea flare of a fully-variational optical remote sensing image.
Compared with the prior art, the invention has the following main advantages:
1. the remote sensing image has a complex space spectrum structure, the traditional total variation can not well punish flare information in the image, a high-order texture total variation flare removing model is constructed in consideration of the high-order nonlinear characteristic of the remote sensing image, and compared with the previous total variation mode, the high-order texture total variation flare removing model has the advantages that the effect of the high-order texture total variation flare removing model on removing high-dimensional data flare is better through a high-order total variation item for introducing texture, and the detail information of ground objects is effectively reserved on the basis of removing flare.
2. The method for removing the sea surface flare of the fully-variational optical remote sensing image converts the flare removal problem into an optimization solving problem, the degraded image is modeled into a pure remote sensing image and a flare image, the solving is convenient and fast, and the existing multi-problem solving method, such as an Alternating Direction Multiplier Method (ADMM) and the like, can be adopted according to needs.
3. The method for removing the sea flare of the full-variational optical remote sensing image can remove the sea flare of the remote sensing image without using any auxiliary data and wave band information (such as wind speed, wavelength and the like).
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present invention.
FIG. 2 is a functional relationship established in an embodiment of the present inventionX=Y +ZSchematic diagram of (1).
Fig. 3 is flare contamination data in an embodiment of the present invention.
Fig. 4 shows the contrast of the anti-flare effect of the method of the embodiment of the present invention in the image of the oil spill accident in the plaza 19-3.
Fig. 5 is a graph comparing the anti-flare effect of the method of the embodiment of the present invention in the yellow river port image 1 with the prior art method.
Fig. 6 is a graph comparing the anti-flare effect of the method of the embodiment of the present invention in the yellow river port image 2 with the prior art method.
Fig. 7 is a graph comparing the anti-flare effect of the method of the embodiment of the present invention in the yellow river port image 3 with the prior art method.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described and explained in detail below with reference to flowcharts and embodiments, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following will take the oil spill accident site data on the platform of the Penglai 19-3 and the yellow river port data obtained by us as an example to further describe the method for removing the sea surface flare of the total variation optical remote sensing image in detail.
As shown in fig. 1, the method for removing sea flare of a full-variable-spectrum optical remote sensing image in the embodiment includes:
1) optical remote sensing degraded image to be contaminated by flare spotsXInputting a high-order texture fully-variant flare removal model shown in the following formula;
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,μandηin order to be the weight, the weight is,Z=X-Yin order to be a flare image,Xin order to optically remotely sense the degraded image,Z m as flare imagesZTo (1) amThe number of the wave bands is one,
for introducing higher-order fully-variant terms of texture, in whichZ m1First auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,Z 1 =DY-TandZ 2=GT，Tto introduce intoSatisfy-S,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,D,Grespectively a first order and a second order difference operator,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band;
2) converting the solving problem of the high-order texture fully-variant flare removal model into a numerical optimization problem comprising a plurality of sub-problems;
3) solving the numerical optimization problem containing a plurality of sub-problems to obtain a pure remote sensing imageY。
In this embodiment, before step 1), the method further includes the step of establishing a high-order texture fully-variant flare removal model:
s1) introduction satisfies-S,T∈R 2|DY=S+TAuxiliary variables ofSAndTwhereinR 2Which represents a real number of the digital signal,Dfor the first order difference operator, the higher order fully variant term for introducing texture is determined as follows:
in the above formula, the first and second carbon atoms are,
to introduce the higher order fully variant terms of the texture,DYa first order difference representing a clean remote sensing image,S,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,S=DY-T，α 1andα 2in order to be a free parameter,Yin order to obtain a clean remote sensing image,Gin the form of a second-order difference operator,Nas flare imagesZThe size of the image of a single band of wavelengths,
as an auxiliary variableSThe mixed norm of (a) of (b),
second order difference as auxiliary variableGTA mixed norm of (d);
s2) degraded image of optical remote sensingXAs a clean remote sensing imageYAnd a flare imageZTo establish an optical remote sensing degraded imageXPure remote sensing imageYAnd flare imagesZFunctional relationship ofX=Y +ZEstablishing a clean remote sensing image facilitating the distinction of flare and background informationYAnd introducing a high-order fully-variable partial term of the texture to obtain a high-order texture fully-variable flare removing model.
In the embodiment, the high-order total variation is introduced, and then the texture constraint regular term is combined to construct the high-order total variation introduced into the texture, wherein the high-order total variation introduced into the texture is mainly used for giving a larger weight to punish on a flare pollution part, and the weight of an area without flare pollution in the ocean is smaller, so that feature information is reserved while flare is removed.
Wherein, the function expression of the mixed norm is:
in the above formula, the first and second carbon atoms are,Sa calculation object representing a mixed norm,Mthe number of orders of the matrix is represented,Nas flare imagesZThe size of the image of a single band of wavelengths,s k+mN as objects of mixed normsSTo (1) ak+mStep oneNThe number of the elements is one,mis the number of matrix orders.
In this embodiment, step S2) includes:
s2.1) as shown in FIG. 2, degrading the image by optical remote sensingXAs a clean remote sensing imageYAnd a flare imageZTo establish an optical remote sensing degraded imageXPure remote sensing imageYAnd flare imagesZFunctional relationship ofX=Y +ZEstablishing a clean remote sensing image facilitating the distinction of flare and background informationYThe function expression of (a) is as follows:
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,μandηin order to be the weight, the weight is,Xin order to optically remotely sense the degraded image,Y m is a pure remote sensing imageYTo (1) amThe number of the wave bands is one,X m for remote sensing degraded imagesXTo (1) amThe number of the wave bands is one,
in order to perform the dot-product operation,
for introducing higher-order fully-variant terms of texture
To (1) amA plurality of wave bands;
s2.2) introducing a function expression of a high-order fully-variable component of the introduced texture into a pure remote sensing image convenient for distinguishing flare and background informationYA clean remote sensing image which will facilitate the distinction between flare and background informationYThe functional expression of (a) is rewritten as:
s2.3) introduction of auxiliary variablesZ=X-Y，Z 1 =DY-TAndZ 2 =GTwhereinZ=X-YIn order to be a flare image,Xin order to optically remotely sense the degraded image,Yin order to obtain a clean remote sensing image,Z 1the first auxiliary variable of the high-order total variation term,Z 2a second auxiliary variable which is a high-order total variation term,D,Grespectively a first order difference operator and a second order difference operator to obtain a high-order texture fully-variant flare removing model shown in the formula (1).
Wherein the first and second order difference operatorsD,GThe functional expression of (a) is:
wherein the content of the first and second substances,D h andD v representing the difference matrix in the horizontal and vertical directions, respectively.
In the embodiment, step 1) inputs the remote sensing degraded image polluted by flare spotsXFor adopting Nano-Hyperspec&The M600 Pro unmanned aerial vehicle remote sensing system collects water surface flare data in the east Yinghuang estuary wetland. The remote sensing camera has a spectral range of 400-1000 nm and 270 spectral bands. In fig. 3, subgraphs (a) to (c) show flare data of 3 different areas collected at different times.
In this embodiment, step 2) includes:
2.1) determining the Lagrangian function of the higher-order texture fully variant flare removal model as follows:
in the above formula, the first and second carbon atoms are,L(Y,T,Z 1,Z 2,μ 1,μ 2,μ 3) In order to be a function of the lagrange,Yin order to obtain a clean remote sensing image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,μ 1,μ 2,μ 3in order to be a lagrange multiplier,μandηare two weights for the number of bits to be processed,Z=X-Yin order to be a flare image,Z m is the second of flare image ZmThe number of the wave bands is one,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2 second auxiliary variable being higher-order total variation termZ 2 To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nis a flareImage of a personZThe size of the image of a single band of wavelengths,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,D,Grespectively a first order and a second order difference operator,Tfor the introduction of a fillS,T∈R 2|DY=S+TAn auxiliary variable of };
2.2) converting the solving problem of the high-order texture fully-variant flare removal model into a solution including solving based on the Lagrangian function of the high-order texture fully-variant flare removal modelY、T、Z 1 、Z 2 AndZthe numerical optimization problem of the sub-problem of (1), whereinYIn order to obtain a clean remote sensing image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z 1to introduce the first auxiliary variable of the higher order fully variant terms of the texture,Z 2the second auxiliary variable of the high-order total variation item of the texture is introduced, and the parameters of other sub-problems are fixed and unchanged when a certain sub-problem is solved.
In this example, the inclusion obtained in step 2.2) was solvedY、T、Z 1、Z 2AndZthe numerical optimization problem of the sub-problem of (1) includes: solving the solution shown in the formula (7)Y、TNumerical optimization of the sub-problem, solving the problem shown in equation (8)Z 1Numerical optimization of the sub-problem, solving the problem shown in equation (9)Z 2Numerical optimization of the sub-problem, solving the problem shown in equation (10)ZA numerical optimization problem of the sub-problem;
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,Z=X-Yin order to be a flare image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z 1the first auxiliary variable of the high-order total variation term,Z 2a second auxiliary variable which is a high-order total variation term,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,D,Grespectively a first order and a second order difference operator,μ 1,μ 2,μ 3in order to be a lagrange multiplier,μandηare two weights for the number of bits to be processed,Z m as flare imagesZTo (1) amThe number of the wave bands is one,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band.
In this embodiment, the method adopted for solving the numerical optimization problem including the plurality of sub-problems in step 3) is an alternating direction multiplier method.
In this embodiment, step 3) includes:
3.1) number of initialization iterationsk；
3.2) updating separately according toZ 1 k+1、Z 2 k+1AndZ k+1；
in the above formula, the first and second carbon atoms are, Z 1 k+1 is as followsk+1 iteration of the first auxiliary variable of the high-order total variable termZ 1，Ψ γ1AndΨ γ2is the intermediate variable(s) of the variable,Din order to be a first order difference operator,Y k+1is as followsk+1 iteration of clean remote sensing imagesY，Z 2 k+1Is as followskSecond auxiliary variable of high-order total variable component of +1 iterationZ 2，Z k+1Is as followsk+1 iterative flare imagesZ，μIn order to be the weight, the weight is,S,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band, max is a maximum function, sgn is a sign function,Xin order to optically remotely sense the degraded image,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,μ 1 k ,μ 2 k ,μ 3 k is composed ofμ 1,μ 2,μ 3First, thekValues of the sub-iterations, wherein:
in the above formula, the first and second carbon atoms are,αsgn is a sign function, max is a maximum function,γ 1andγ 2the function of is expressed as:
in the above formula, the first and second carbon atoms are,α 1andα 2in order to be a free parameter,ηin order to be the weight, the weight is,β 1in order to be a weight parameter, the weight parameter,Z k is as followskSub-iterative flare imageZ；
3.3) determining the number of iterationskWhether the image is equal to a preset threshold value or not, and if the image is equal to the preset threshold value, obtaining a pure remote sensing image finallyYOutputting as a final result, ending and exiting; otherwise, skipping to execute the next step;
updating the Lagrangian multiplier according toμ 1,μ 2,μ 3(ii) a Then the number of iterationskAdding 1, and skipping to execute the step 3.2);
in the above formula, the first and second carbon atoms are,μ 1 k+1,μ 2 k+1,μ 3 k+1is composed ofμ 1,μ 2,μ 3First, thekThe value of +1 iterations is then used,μ 1 k ,μ 2 k ,μ 3 k is composed ofμ 1,μ 2,μ 3First, thekThe value of the sub-iteration is,X k+1is as followsk+1 iteration optical remote sensing degraded imageX，Y k+1Is as followsk+1 iteration of clean remote sensing imagesY，Z k+1Is as followsk+1 iterative flare imagesZ，D,GRespectively a first order and a second order difference operator,T k+1is as followskAuxiliary variable for +1 iterationsT，Z 1 k+1 Is as followsk+1 iteration of the first auxiliary variable of the high-order total variable termZ 1，Z 2 k+1Is as followskSecond auxiliary variable of high-order total variable component of +1 iterationZ 2，T k+1 、Z 1 k+1AndZ 2 k+1is as followsk+1 iteration auxiliary variables.
Solving for equation (7)Y、TThe numerical optimization problem derivation for the sub-problem can be found as:
in the above formula, the first and second carbon atoms are,I N to representNA matrix of the order of the unit,I N2is shown in (2)NA matrix of the order of the unit,D,Gfirst and second order difference operators, respectively, Δ =D T D= D T h D h + D T v D v ，D h AndD v respectively representing the difference matrices in the horizontal and vertical directions,G T Gthe functional expression of (a) is:
for convenience of solution, other variables are also transformed correspondingly:
in the above formula, the subscript in the decomposed variableh,d,vRepresenting components in the horizontal, diagonal and vertical directions, respectively.
Thus, it is possible to obtain:
variables are obtained using fast Fourier transformYAndT。
to verify the method of this embodiment, the free parameters are taken in this embodimentα 1=0.06, free parameterα 2=0.04, weightμ=120, weightηNumber of iterations =0.3k= 40. Fig. 4 shows the anti-flare effect of the other method and the method of this embodiment in the image of the oil spill accident of plaza 19-3, wherein sub-image (a) is a pseudo-color image (R:140, G:180, B:220), sub-image (B) is the anti-flare effect of the Lyzenga method, sub-image (c) is the anti-flare effect of the Goodman method, sub-image (d) is the anti-flare effect of the Kutser method, and sub-image (e) is the anti-flare effect of the method of this embodiment. Fig. 5 shows a graph of the anti-flare effect of the yellow river port image 1 in the other method and the method of the present embodiment, wherein sub-graph (a) is a pseudo-color image (R:124, G:65, B:37), sub-graph (B) is the anti-flare effect of the Lyzenga method, sub-graph (c) is the anti-flare effect of the Goodman method, sub-graph (d) is the anti-flare effect of the Kutser method, and sub-graph (e) is the anti-flare effect of the method of the present embodiment. FIG. 6 shows the anti-flare effect of the yellow river port image 2 of the other method and the method of the present embodiment, wherein sub-image (a) is a pseudo-color image (R:124, G:65, B:37), sub-image (B) is the anti-flare effect of Lyzenga method, sub-image (c) is the anti-flare effect of Goodman method, sub-image (d) is the anti-flare effect of Kutser method, and sub-image (e) is the method of the present embodimentThe anti-flare effect of (1). Fig. 7 shows a graph of the anti-flare effect of the yellow river port image 3 in the other method and the method of the present embodiment, in which sub-graph (a) is a pseudo-color image (R:124, G:65, B:37), sub-graph (B) is the anti-flare effect of the Lyzenga method, sub-graph (c) is the anti-flare effect of the Goodman method, sub-graph (d) is the anti-flare effect of the Kutser method, and sub-graph (e) is the anti-flare effect of the method of the present embodiment. It should be noted that the color images described above have been converted into gray images due to the limitation of the published format of the patent literature. As can be seen from fig. 4 to 7, the method of the present embodiment can realize the sea flare removal of the optical remote sensing image, without using any auxiliary data and waveband information, convert the flare removal problem into an optimization solution problem, and model the degraded image into a pure remote sensing image and flare image, which can effectively remove flare and retain ground object information. The method can effectively remove flare pollution with different intensities, recover a pure remote sensing image, convert the flare removal problem into an optimization solving problem without using any auxiliary data and waveband information, model a degraded image into the pure remote sensing image and the flare image, effectively remove flare and reserve ground object information.
In addition, the invention also provides a system for removing the sea surface flare of the fully-variable optical remote sensing image, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the method for removing the sea surface flare of the fully-variable optical remote sensing image.
Furthermore, the present invention also provides a computer-readable storage medium having stored therein a computer program programmed or configured to execute the foregoing method for removing sea flare of a fully-variational optical remote sensing image.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (10)
1. A method for removing sea flare of a full-variation optical remote sensing image is characterized by comprising the following steps:
1) optical remote sensing degraded image to be contaminated by flare spotsXInputting a high-order texture fully-variant flare removal model shown in the following formula;
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,μandηin order to be the weight, the weight is,Z=X-Yin order to be a flare image,Xin order to optically remotely sense the degraded image,Z m as flare imagesZTo (1) amThe number of the wave bands is one,
for introducing higher-order fully-variant terms of texture, in whichZ m1First auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,Z 1 =DY-TandZ 2=GT，Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,D,Grespectively a first order and a second order difference operator,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band;
2) converting the solving problem of the high-order texture fully-variant flare removal model into a numerical optimization problem comprising a plurality of sub-problems;
3) solving the numerical optimization problem containing a plurality of sub-problems to obtain a pure remote sensing imageY。
2. The method for removing sea flare of the fully-variational optical remote sensing image according to claim 1, wherein the step 1) is preceded by the step of establishing a high-order texture fully-variational flare removal model:
s1) introduction satisfies-S,T∈R 2|DY=S+TAuxiliary variables ofSAndTwhereinR 2Which represents a real number of the digital signal,Dfor the first order difference operator, the higher order fully variant term for introducing texture is determined as follows:
in the above formula, the first and second carbon atoms are,
to introduce the higher order fully variant terms of the texture,DYa first order difference representing a clean remote sensing image,S,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,S=DY-T，α 1andα 2in order to be a free parameter,Yin order to obtain a clean remote sensing image,Gin the form of a second-order difference operator,Nas flare imagesZThe size of the image of a single band of wavelengths,
as an auxiliary variableSThe mixed norm of (a) of (b),
second order difference as auxiliary variableGTA mixed norm of (d);
s2) degraded image of optical remote sensingXAs a clean remote sensing imageYAnd a flare imageZTo establish an optical remote sensing degraded imageXPure remote sensing imageYAnd flare imagesZFunctional relationship ofX=Y +ZEstablishing a clean remote sensing image facilitating the distinction of flare and background informationYAnd introducing a high-order fully-variable partial term of the texture to obtain a high-order texture fully-variable flare removing model.
3. The method for removing sea flare of a fully-variational optical remote sensing image according to claim 2, wherein the function expression of the mixed norm is:
in the above formula, the first and second carbon atoms are,Sa calculation object representing a mixed norm,Mthe number of orders of the matrix is represented,Nas flare imagesZThe size of the image of a single band of wavelengths,s k+mN as objects of mixed normsSTo (1) ak+mStep oneNThe number of the elements is one,mis the number of matrix orders.
4. The method for removing sea flare of a fully-variational optical remote sensing image according to claim 2, wherein the step S2) comprises:
s2.1) degrading the image by optical remote sensingXAs a clean remote sensing imageYAnd a flare imageZTo establish an optical remote sensing degraded imageXPure remote sensing imageYAnd flare imagesZFunctional relationship ofX=Y +ZEstablishing a clean remote sensing image facilitating the distinction of flare and background informationYThe function expression of (a) is as follows:
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,μandηin order to be the weight, the weight is,Xin order to optically remotely sense the degraded image,Y m is a pure remote sensing imageYTo (1) amThe number of the wave bands is one,X m for remote sensing degraded imagesXTo (1) amThe number of the wave bands is one,
in order to perform the dot-product operation,
for introducing higher-order fully-variant terms of texture
To (1) amA plurality of wave bands;
s2.2) introducing a function expression of a high-order fully-variable component of the introduced texture into a pure remote sensing image convenient for distinguishing flare and background informationYA clean remote sensing image which will facilitate the distinction between flare and background informationYThe functional expression of (a) is rewritten as:
s2.3) introduction of auxiliary variablesZ=X-Y，Z 1 =DY-TAndZ 2=GTwhereinZ=X-YIn order to be a flare image,Xin order to optically remotely sense the degraded image,Yin order to obtain a clean remote sensing image,Z 1the first auxiliary variable of the high-order total variation term,Z 2a second auxiliary variable which is a high-order total variation term,D,Grespectively a first order difference operator and a second order difference operator to obtain a high-order texture fully-variant flare removing model shown in the formula (1).
5. The method for removing sea flare of the fully-variational optical remote sensing image according to any one of claims 1 to 4, wherein the step 2) comprises:
2.1) determining the Lagrangian function of the higher-order texture fully variant flare removal model as follows:
in the above formula, the first and second carbon atoms are,L(Y,T,Z 1,Z 2,μ 1,μ 2,μ 3) In order to be a function of the lagrange,Yin order to obtain a clean remote sensing image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,μ 1,μ 2,μ 3in order to be a lagrange multiplier,μandηare two weights for the number of bits to be processed,Z=X-Yin order to be a flare image,Z m is the second of flare image ZmThe number of the wave bands is one,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZThe size of the image of a single band of wavelengths,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,D,Grespectively a first order and a second order difference operator,Tfor the introduction of a fillS,T∈R 2|DY=S+TAn auxiliary variable of };
2.2) converting the solving problem of the high-order texture fully-variant flare removal model into a solution including solving based on the Lagrangian function of the high-order texture fully-variant flare removal modelY、T、Z 1、Z 2AndZthe numerical optimization problem of the sub-problem of (1), whereinYIn order to obtain a clean remote sensing image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z 1to introduce the first auxiliary variable of the higher order fully variant terms of the texture,Z 2the second auxiliary variable of the high-order total variation item of the texture is introduced, and the parameters of other sub-problems are fixed and unchanged when a certain sub-problem is solved.
6. The method for removing sea flare of full-variation optical remote sensing image according to claim 5, wherein the inclusion obtained in step 2.2) is solvedY、T、Z 1 、Z 2 AndZthe numerical optimization problem of the sub-problem of (1) includes: solving the solution shown in the formula (7)Y、TNumerical advantage of subproblemsSolving the problem, the solution shown in equation (8)Z 1Numerical optimization of the sub-problem, solving the problem shown in equation (9)Z 2Numerical optimization of the sub-problem, solving the problem shown in equation (10)ZA numerical optimization problem of the sub-problem;
in the above formula, the first and second carbon atoms are,Yin order to obtain a clean remote sensing image,Z=X-Yin order to be a flare image,Tfor the introduction of a fillS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z 1the first auxiliary variable of the high-order total variation term,Z 2a second auxiliary variable which is a high-order total variation term,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,D,Grespectively a first order and a second order difference operator,μ 1,μ 2,μ 3in order to be a lagrange multiplier,μandηare two weights for the number of bits to be processed,Z m as flare imagesZTo (1) amThe number of the wave bands is one,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2 second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band.
7. The method for removing sea flare of the fully-variational optical remote sensing image according to claim 6, wherein the method for solving the numerical optimization problem comprising the plurality of sub-problems in the step 3) is an alternating direction multiplier method.
8. The method for removing sea flare of the fully-variational optical remote sensing image according to claim 7, wherein the step 3) comprises:
3.1) number of initialization iterationsk；
3.2) updating separately according toZ 1 k+1、Z 2 k+1AndZ k+1；
in the above formula, the first and second carbon atoms are, Z 1 k+1is as followsk+1 iteration of the first auxiliary variable of the high-order total variable termZ 1，Ψ γ1AndΨ γ2is the intermediate variable(s) of the variable,Din order to be a first order difference operator,Y k+1is as followsk+1 iteration of clean remote sensing imagesY，Z 2 k+1Is as followskSecond auxiliary variable of high-order total variable component of +1 iterationZ 2，Z k+1Is as followsk+1 iterative flare imagesZ，μIn order to be the weight, the weight is,S,Tto introduceSatisfyS,T∈R 2|DY=S+TThe auxiliary variable of (c) is set to zero,Z m1first auxiliary variable of high-order total variation itemZ 1To (1) amThe number of the wave bands is one,Z m2second auxiliary variable being higher-order total variation termZ 2To (1) amThe number of the wave bands is one,α 1andα 2in order to be a free parameter,Nas flare imagesZImage size of a single band, max is a maximum function, sgn is a sign function,Xin order to optically remotely sense the degraded image,β 1,β 2,β 3in order to be a weight parameter, the weight parameter,μ 1 k ,μ 2 k ,μ 3 k is composed ofμ 1,μ 2,μ 3First, thekValues of the sub-iterations, wherein:
in the above formula, the first and second carbon atoms are,αsgn is a sign function, max is a maximum function,γ 1andγ 2the function of is expressed as:
in the above formula, the first and second carbon atoms are,α 1andα 2in order to be a free parameter,ηin order to be the weight, the weight is,β 1in order to be a weight parameter, the weight parameter,Z k is as followskSub-iterative flare imageZ；
3.3) determining the number of iterationskWhether the image is equal to a preset threshold value or not, and if the image is equal to the preset threshold value, obtaining a pure remote sensing image finallyYOutputting as a final result, ending and exiting; otherwise, skipping to execute the next step;
updating the Lagrangian multiplier according toμ 1,μ 2,μ 3(ii) a Then the number of iterationskAdding 1, and skipping to execute the step 3.2);
in the above formula, the first and second carbon atoms are,μ 1 k+1,μ 2 k+1,μ 3 k+1is composed ofμ 1,μ 2,μ 3First, thekThe value of +1 iterations is then used,μ 1 k ,μ 2 k ,μ 3 k is composed ofμ 1,μ 2,μ 3First, thekThe value of the sub-iteration is,X k+1is as followsk+1 iteration optical remote sensing degraded imageX，Y k+1Is as followsk+1 iteration of clean remote sensing imagesY，Z k+1Is as followsk+1 iterative flare imagesZ，D,GRespectively a first order and a second order difference operator,T k+1is as followskAuxiliary variable for +1 iterationsT，Z 1 k+1Is as followsk+1 iteration of the first auxiliary variable of the high-order total variable termZ 1，Z 2 k+1Is as followskSecond auxiliary variable of high-order total variable component of +1 iterationZ 2，T k+1 、Z 1 k+1AndZ 2 k+1is as followsk+1 iteration auxiliary variables.
9. A system for removing sea flare from a fully-variable optical remote sensing image, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute a computer program of the method for removing sea flare from a fully-variable optical remote sensing image according to any one of claims 1 to 8.
10. A computer readable storage medium having stored thereon a computer program programmed or configured to perform the method of removing sea flare from a full-variable-spectrum optical remote sensing image according to any one of claims 1 to 8.
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