CN114881892A

CN114881892A - Remote sensing image characteristic discretization method and device based on II-type fuzzy rough model

Info

Publication number: CN114881892A
Application number: CN202210776562.XA
Authority: CN
Inventors: 陈琼; 黄小猛
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2022-07-04
Filing date: 2022-07-04
Publication date: 2022-08-09
Anticipated expiration: 2042-07-04
Also published as: WO2022258077A3; LU503147B1; CN114881892B; WO2022258077A2

Abstract

The invention provides a remote sensing image characteristic discretization method and a device based on a II-type fuzzy rough model, wherein the method comprises the following steps: acquiring target remote sensing image data, and extracting mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types; determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; calculating the secondary membership degree of each mixed pixel belonging to each ground object type according to the primary membership degree; determining a II-type fuzzy rough set of each object type according to the primary membership degree and the secondary membership degree; and carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result. According to the embodiment of the invention, the fuzzy component in the remote sensing image characteristic discretization process is described by the primary membership degree and the secondary membership degree corresponding to the mixed pixel, the primary membership degree is used for carrying out the fuzzy discretization process, the primary membership degree is further fuzzified by the secondary membership degree, the uncertainty of the mixed pixel is accurately quantified, and the accurate discretization result is obtained.

Description

Remote sensing image characteristic discretization method and device based on II-type fuzzy rough model

Technical Field

The invention relates to the field of remote sensing image feature extraction, in particular to a remote sensing image feature discretization method and device based on a II-type fuzzy rough model.

Background

Remote sensing has been widely used in various fields of economic and social development as an advanced technical means. The spatial resolution, the time resolution, the spectral resolution and the radiation resolution are gradually improved, so that the remote sensing data have obvious big data characteristics. Due to the diversity, the interpenetration and the complexity of the spatial distribution of the ground feature elements, the spectrum signal of each pixel in the remote sensing image records different land cover types, and the pixels are called mixed pixels. Feature discretization, one of the most influential data preprocessing techniques, plays an important role in the fields of knowledge discovery and data mining, which are widely applied to industrial control. The method can convert continuous features into discrete features which are closer to the representation of a knowledge layer, so that the data is easier to understand, use and interpret, and therefore the efficiency of remote sensing data processing is improved and the method is suitable for learning algorithms which need discrete data as input. In the related art, in order to realize the remote sensing image feature discretization, a feature discretization algorithm of the remote sensing image is usually based on the assumption that one sample only belongs to a single category, and uncertainty caused by a mixed pixel cannot be described. Or in order to simplify the operation of the type II fuzzy set, the secondary membership degree of the mixed image element is defined as a constant. Although the fuzzy rough model quantifies uncertain information by introducing membership of the pixel to each category, the decomposition model of the mixed pixel has larger error, which causes that the distribution information of the data is not uniform, the uncertainty of the data cannot be accurately described, and the data precision is reduced.

Disclosure of Invention

Therefore, the technical problem to be solved by the invention is to overcome the defect that the uncertainty caused by the fact that the characteristic discretization algorithm in the prior art cannot accurately quantize and evaluate the mixed pixel, so that the remote sensing image characteristic discretization method and the remote sensing image characteristic discretization device based on the II-type fuzzy rough model are provided.

According to a first aspect, the embodiment of the invention provides a remote sensing image feature discretization method based on a fuzzy rough model type II, which includes the following steps: acquiring target remote sensing image data, and extracting mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types; determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; calculating the secondary membership degree of each mixed pixel belonging to each ground object type according to the primary membership degree; determining a II-type fuzzy rough set of each object type according to the primary membership degree and the secondary membership degree; and carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result.

Optionally, determining the main membership degree of each mixed pixel corresponding to each surface feature type according to the mixed pixels, including: iteratively calculating a fuzzy mean vector and a fuzzy covariance matrix of a preset fuzzy partition matrix, wherein the preset fuzzy partition matrix is composed of membership degrees of the mixed pixels corresponding to various ground object types; and determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the fuzzy partition matrix when the iterative computation meets the iterative termination condition.

Optionally, determining the main membership degree of each mixed pixel corresponding to each ground feature type according to a fuzzy partition matrix when iteration calculation meets an iteration termination condition, including: and determining the abundance corresponding to each mixed pixel according to the fuzzy partition matrix, and taking the abundance as the main membership degree of each ground object type corresponding to each mixed pixel.

Optionally, calculating a secondary membership degree of each mixed pixel belonging to each surface feature type according to the primary membership degree, including: determining a hard segmentation matrix according to a fuzzy segmentation matrix when iteration calculation meets an iteration termination condition; determining a set formed by pixels belonging to various surface feature types according to the hard segmentation matrix; calculating upper approximation, lower approximation, a positive domain, a negative domain and a boundary domain of the set in an approximation space; and determining the secondary membership degree of each mixed pixel belonging to each ground object type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain.

Optionally, performing feature discretization processing on the target remote sensing image data to obtain an optimal discretization result, including: acquiring an initial breakpoint set of a mixed pixel from remote sensing image data; initializing a target remote sensing image data population based on the number of breakpoints of the initial breakpoint set; iteratively executing a genetic algorithm on individuals of the target remote sensing image data population to determine an optimal discretization result; the discretization scheme corresponding to the initialized target remote sensing image data population is an initial discretization scheme, and each population individual corresponds to one discretization result.

Optionally, iteratively executing a genetic algorithm on the individual of the target remote sensing image data population to determine an optimal discretization result, including: determining a fuzzy relation between the mixed pixels based on Euclidean distances between the mixed pixels; calculating the average approximate precision of the type II fuzzy rough set according to the fuzzy relation; determining the reduction amplitude of the number of the breakpoints corresponding to the individual target remote sensing image data population according to the number of the breakpoints of the initial breakpoint set; determining a fitness function of the II-type fuzzy rough set according to the amplitude of the reduction of the number of the fault points and the average approximate precision; and determining the fitness value of the II-type fuzzy rough set according to the fitness function of the II-type fuzzy rough set, and taking the individual of each target remote sensing image data population corresponding to the optimal fitness value as an optimal discretization result.

Optionally, the fitness function of the type II fuzzy rough set is expressed by the following formula:

，

wherein,αandβis a weight coefficient ofDL is the magnitude of the reduction in the number of breakpoints,

is the average approximation accuracy.

According to a second aspect, the embodiment of the invention provides a remote sensing image feature discretization device based on a fuzzy rough model type II, which includes: the mixed pixel extraction unit is configured to acquire target remote sensing image data and extract mixed pixels from the target remote sensing image data, and each mixed pixel comprises spectral response characteristics of multiple surface feature types; the main membership degree determining unit is configured to determine the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; the secondary membership degree determining unit is configured to calculate the secondary membership degree of each mixed pixel belonging to each ground feature type according to the primary membership degree; the fuzzy rough set determining unit is configured to determine a II-type fuzzy rough set of each ground object type according to the primary membership degree and the secondary membership degree; and the optimal discretization result determining unit is configured to perform characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result.

According to a third aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by a processor, the method for discretizing a feature of a remote sensing image based on a fuzzy rough model of type II according to any one of the embodiments of the first aspect is implemented.

According to a fourth aspect, embodiments of the present invention provide a computer device comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executable by the at least one processor to perform the method for discretizing a feature of a remote sensing image based on a blurred rough model of type II according to any of the embodiments of the first aspect.

The technical scheme of the invention has the following advantages:

the invention provides a remote sensing image characteristic discretization method and a device based on a II-type fuzzy rough model, wherein the method comprises the following steps: acquiring target remote sensing image data, and extracting mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types; determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; calculating the secondary membership degree of each mixed pixel belonging to each ground object type according to the primary membership degree; determining a II-type fuzzy rough set of each object type according to the primary membership degree and the secondary membership degree; and carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result. According to the embodiment of the invention, the rough set and the fuzzy set are combined, the fuzzy component in the remote sensing image characteristic discretization process is described by the primary membership and the secondary membership corresponding to the mixed pixel, the discretization process is fuzzified by the primary membership, and the primary membership is further fuzzified by the secondary membership, so that the uncertainty of the mixed pixel is accurately quantified and evaluated, and a more accurate discretization result is obtained.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a specific example of a remote sensing image feature discretization method based on a type II fuzzy rough model in embodiment 1 of the present invention;

fig. 2 is a specific exemplary analysis diagram of a discretization method of remote sensing image features based on a fuzzy rough model type II in embodiment 1 of the present invention;

fig. 3 is a diagram illustrating a structure of a remote sensing image feature discretization apparatus based on a type II fuzzy rough model in embodiment 2 of the present invention;

fig. 4 is a diagram showing an example of the structure of a computer device in embodiment 4 of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood 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.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the present invention, the fuzzy rough model is a more powerful uncertainty data analysis model than the fuzzy set and the rough set. Fuzzy sets are introduced on the basis of rough sets, and the correlation among samples is described by adopting similar relations instead of equivalent relations of the rough sets. As the popularization of the fuzzy rough model, the II type fuzzy rough model can provide more accurate uncertainty analysis capability. The fuzzy rough model of II type fuzzifies the membership function values of the fuzzy set again, so that the fuzzy phenomenon can be described more deeply.

In the description of the present invention with respect to a formula,expmeans natural constants in higher mathematicseAn exponential function of the base.infDenotes the infimum bound, which is the largest lower bound of a set.supRepresenting the supremum bound, is the smallest upper bound of a collection.

In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

Example 1

The embodiment provides a remote sensing image feature discretization method based on a II-type fuzzy rough model, as shown in fig. 1, comprising the following steps:

s11: acquiring target remote sensing image data, and extracting mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types.

Specifically, the components of the mixed pixel spectral signal are called end members, and each end member corresponds to the spectral response characteristic of one of the terrain types.

S12: and determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels.

Specifically, determining the main membership degree of each corresponding ground object type according to the mixed pixels is to calculate a fuzzy partition matrix through iteration; and under the condition that the iterative computation meets the iterative termination condition, determining the abundance corresponding to each mixed pixel, and taking the abundance as the main membership degree of each ground object type corresponding to each mixed pixel. The abundance corresponding to each mixed pixel refers to the abundance of the end members of the mixed pixels.

In practical application, the fuzzy partition matrix is composed of the membership degree of each mixed pixel to the class number of the classification scheme. The fuzzy mean vector and the fuzzy covariance matrix in the fuzzy partition matrix can be represented by the above membership.

S13: and calculating the secondary membership degree of each mixed pixel belonging to each ground object type according to the primary membership degree.

Specifically, calculating the secondary membership degree of each mixed pixel belonging to each surface feature type means determining the secondary membership degree according to the distribution of the mixed pixels in the boundary area of the rough set. The distribution condition of the mixed pixels in the boundary area of the rough set comprises determining a set formed by pixels belonging to various surface feature types; and calculating the distribution area of the set in the approximate space to determine the secondary membership degree of the mixed pixel belonging to each surface feature type. Wherein, the distribution region of the collection in the approximate space comprises: upper approximation, lower approximation, positive domain, negative domain, boundary domain in the approximation space are aggregated.

In practical applications, the approximation space refers to a coarse approximation space (A)U,T) WhereinUA set of mixed picture elements is represented,Tthe number of the wave bands of the remote sensing image is represented.

S14: and determining a II-type fuzzy rough set of each object type according to the primary membership degree and the secondary membership degree.

Specifically, in the process of determining the primary membership degree of each mixed pixel corresponding to each surface feature type and the secondary membership degree of each mixed pixel belonging to each surface feature type, the fuzzy components in the remote sensing image characteristic discretization process are described according to the primary membership degree and the secondary membership degree, the fuzzy discretization process is performed according to the primary membership degree, the primary membership degree is further fuzzified according to the secondary membership degree, and the type II fuzzy rough set of each surface feature type is determined according to the determined primary membership degree and the secondary membership degree.

S15: and carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result.

Specifically, the discretization is to adopt a certain specific method to divide the continuous features into a plurality of subintervals and associate the subintervals with the candidate breakpoints. Therefore, the feature discretization processing of the target remote sensing image can be regarded as the selection of the candidate break points. The process of carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result refers to iterative selection of candidate breakpoints through a genetic algorithm; determining the fitness function of individuals in the population according to the reduction range of the number of the candidate fault points in each iteration process and the average approximate precision of the type II fuzzy rough set; and evaluating the discretization result according to the determined fitness function of the individuals in the population, and obtaining the optimal discretization result.

In practical application, the initial discretization scheme is determined by acquiring an initial breakpoint set of a mixed pixel in remote sensing image data.

The invention provides a remote sensing image characteristic discretization method based on a II-type fuzzy rough model, which comprises the following steps: acquiring target remote sensing image data, and extracting mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types; determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; calculating the secondary membership degree of each mixed pixel belonging to each ground object type according to the primary membership degree; determining a II-type fuzzy rough set of each object type according to the primary membership degree and the secondary membership degree; and carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result. The embodiment of the invention combines the rough set and the fuzzy set, describes the fuzzy component in the discretization process of the remote sensing image characteristic by the primary membership degree and the secondary membership degree corresponding to the mixed pixel, fuzzifies the discretization process by the primary membership degree, and further fuzzifies the primary membership degree by the secondary membership degree, thereby accurately quantifying and evaluating the uncertainty of the mixed pixel and obtaining a more accurate discretization result.

In an optional embodiment of the present invention, in the step S12, determining, according to the mixed pixels, a main membership degree of each mixed pixel corresponding to each surface feature type includes:

(1) iteratively calculating a fuzzy mean vector and a fuzzy covariance matrix of a preset fuzzy partition matrix, wherein the preset fuzzy partition matrix is composed of membership degrees of mixed pixels corresponding to various surface feature types;

specifically, the preset fuzzy partition matrix can be expressed according to the following formula:

，

wherein,F _s (X _k ) To representUTo middlekPixel elementX _k To pairsThe degree of membership of a class,s∈{1,2,…,g}，gthe number of the categories is indicated and,k∈{1,2,…,n}，nto representUThe number of pixels in.

In the practical application of the method, the air conditioner,F _s (X _k ) Satisfies the following conditions: 0 is less than or equal toF _s (X _k )≤1，

，q∈{1,2,…,g}。

Specifically, the fuzzy mean vector of the preset fuzzy partition matrix can be expressed by the following formula:

，

wherein,

which represents the fuzzy average of the mean,i∈{1,2,…,m}，mthe number of the wave bands is shown,

，wthe weight is represented by a weight that is,wis greater than or equal to 1, and the content of the active carbon,x _ik is shown askThe picture element is atiPixel values over a band.

Specifically, the fuzzy covariance matrix of the preset fuzzy partition matrix can be expressed by the following formula:

wherein,σ _mms the mean of the fuzzy covariance,j∈{1,2,…,m}，

。

specifically, iteratively calculating the fuzzy mean vector and the fuzzy covariance matrix of the preset fuzzy partition matrix includes determining the preset fuzzy partition matrix from the fuzzy mean vector and the fuzzy covariance matrix.

In practical application, the preset fuzzy partition matrix is composed of the membership degree of pixel pairs and categories, the membership degree of the pixel pairs and the categories can be determined by a fuzzy mean vector and a fuzzy covariance matrix, and the membership degree of the pixel pairs and the categories can be expressed according to the following formula:

，

wherein,P`(s) Is as followssThe prior probability of the occurrence of a class,

。

in practical application, forsThe determination of the prior probability of the occurrence of the category belongs to the mature prior art, and is not repeated in this application.

(2) And determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the fuzzy partition matrix when the iterative computation meets the iterative termination condition.

Specifically, the iteration termination condition can be expressed by the following formula:

，

wherein,θfor the number of iteration steps of the algorithm,εis an error threshold.

In an optional embodiment of the present invention, determining the main membership degree of each mixed pixel corresponding to each surface feature type according to the fuzzy partition matrix when the iterative computation satisfies the iterative termination condition includes: and determining the abundance corresponding to each mixed pixel according to the fuzzy partition matrix, and taking the abundance as the main membership degree of each ground object type corresponding to each mixed pixel.

Specifically, the determination of the main membership degree of each mixed pixel corresponding to each surface feature type is related to the weight and the abundance of each end member in the mixed pixel.

WhereineIs a natural number. The main membership degree of each mixed pixel corresponding to each surface feature type can be expressed according to the following formula:

，

，

wherein,P _s (x) Representing mixed pixelsxThe corresponding abundance of the protein is shown in the figure,

the secondary membership degree of each mixed pixel belonging to each surface feature type is represented,J _x the value range of the main membership degree is represented,uwhich represents a degree of primary membership,

the minimum value of the main membership degree of each mixed pixel corresponding to each ground feature type is represented,

and expressing the maximum value of the main membership degree of each mixed pixel corresponding to each ground feature type.

In the practical application of the method, the material is,edetermine (a)wThe number of the middle weights is the same as the weight,ethe greater the value of (a) is,P _s (x) The more elements are contained, that is, the larger the value range of the main membership degree is, and meanwhile, a large amount of calculation is also brought. Weight ofwNot only the convexity and concavity of the discretization result, but also the sharing degree of the mixed pixels among various types is controlled. The research result shows that when the weight is 2, the clustering effect can be obtainedBest with a greater probability. If it is noteIf the value of (1) is greater than 1, different weights are introduced, the fuzzy clustering result can be considered more comprehensively, but the clustering quality caused by the introduction of the weights cannot be ensured, so that the discretization result has errors and is unstable. Thus, to ensure the stability of the discretization result and reduce the complexity, in an alternative embodiment, the choice is madeeHas a value of 1 andwthe value is 2. At this time, the process of the present invention,

. It should be understood in relation towAndeincluding but not limited to the manner described in the examples,wandethe value of (a) is only required to be used for ensuring the stability of the discretization result and reducing the complexity.

In practical application, for

The main membership degree of each mixed pixel corresponding to each surface feature type can be expressed according to the following formula:

，

。

in an alternative embodiment of the invention, the fuzzy partition matrix is determined by iteratively computing a fuzzy mean vector and a fuzzy partition matrix. And determining the abundance corresponding to the mixed pixel through the fuzzy partition matrix, and taking the abundance as the main membership degree of each surface feature type corresponding to the mixed pixel. Thereby realizing the fuzzification discretization process with the main membership degree.

In an alternative embodiment of the present invention, in step S13, calculating a secondary membership degree of each mixed image element to each surface feature type according to the primary membership degree includes:

(1) and determining a hard segmentation matrix according to the fuzzy segmentation matrix when the iteration calculation meets the iteration termination condition.

Specifically, the hard partition matrix is determined by modifying the maximum value of each column in the fuzzy partition matrix when the iteration termination condition is satisfied to 1 and modifying the other values in the corresponding columns to 0, thereby completing the determination of the hard partition matrix.

(2) And determining a set formed by the image elements belonging to the types of the ground objects according to the hard segmentation matrix.

Specifically, the set of image elements belonging to each surface feature type can be expressed by the following formula:

，

wherein Xs represents a set formed by pixels belonging to the s-th terrain type in the mixed pixel set, and Cs (x) is the membership degree of the pixel x in the hard partition matrix C to the s-th category.

In practical application, the hard partition matrix determines the corresponding position with the membership value of 1 in each column, and judges the mixed pixel set according to the determined corresponding position with the membership value of 1 in each columnUWhether the mixed image element belongs to each surface feature type or not is achieved, and therefore the set formed by the image elements belonging to each surface feature type is determined according to the hard segmentation matrix.

(3) And calculating upper approximation, lower approximation, positive domain, negative domain and boundary domain of the set in an approximation space.

In particular, a set of pixel elements belonging to each surface feature type is calculatedX _s The upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain in the approximation space comprise: determining an equivalence class set of the mixed pixel set; and calculating upper approximation, lower approximation, positive domain, negative domain and boundary domain of the set formed by the image elements belonging to the terrain types in the approximation space according to the determined equivalence class set.

Specifically, the set of equivalence classes of the mixed pixel set can be expressed by the following formula:

，

wherein,U|IND(T) An equivalence class set representing a set of mixed picture elements,T={t ₁ ,…,t _m }，

representing arbitrary picture elementsxAnd any pixelyBelonging to mixed picture elementsX，

Indicates the existence and the pixel of any wave band txPixel elementyRespectively corresponding to the equivalent relation of the wave bands.

In particular, the amount of the solvent to be used,X _s the upper approximation in the approximation space can be calculated as follows:

，

wherein,T ^* (X _s ) To representX _s An upper approximation in an approximation space.

In particular, the amount of the solvent to be used,X _s the following approximation in the approximation space can be calculated as follows:

，

wherein,T _* (X _s ) Representing a lower approximation of the set in an approximation space.

In particular, the amount of the solvent to be used,X _s the positive domain in the approximation space can be calculated as follows:

，

wherein,POS _T (X _s ) Representing a positive domain assembled in an approximation space.

In particular, the amount of the solvent to be used,X _s the negative domain in the approximation space can be calculated as follows:

，

wherein,NGT _T (X _s ) Representing the negative domain of the set in approximation space.

In particular, the amount of the solvent to be used,X _s the boundary domain in the approximation space can be calculated as follows:

，

wherein,BN _T (X _s ) Representing a boundary domain that is assembled in an approximation space.

(4) And determining the secondary membership degree of each mixed pixel belonging to each ground object type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain.

Specifically, determining the secondary membership degree of each mixed pixel belonging to each surface feature type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain, and the method comprises the following steps: determining the probability of a set formed by pixels of which the pixels belong to each surface feature type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain; and determining the secondary membership degree of each mixed pixel belonging to each surface feature type according to the probability of the determined set formed by the pixels of each surface feature type to which the pixels belong.

Specifically, determining the probability of the set of pixels belonging to each surface feature type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain means determining the distribution condition of the pixels according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain; and determining the probability of the image elements belonging to the set formed by the image elements of all the surface feature types according to the Bayesian theorem under the determined distribution condition.

Is as an example，POS _T (X _s ) Is attributed to in the mixed pixel setX _s Is used to form a set of picture elements,NGT _T (X _s ) Is not attributed to in the mixed pel setX _s Is selected from the group consisting of the picture elements of (a),BN _T (X _s ) Is that the mixed pixel set can not definitely belong toX _s Is used to form a set. Therefore, the number of the first and second electrodes is increased,BN _T (X _s ) Is the uncertainty field of the mixed picture element. Fig. 2 shows a specific example analysis diagram of a discretization method for remote sensing image features based on a fuzzy rough model type II in the embodiment of the present invention, where each rectangle representsU|IND(T) An equivalence class of (1). The circular area corresponding to reference numeral 22 representsX _s The octagonal area corresponding to reference numeral 24 representsT ^* (X _s ) And the rectangular region corresponding to reference numeral 23 representsT _* (X _s ) OrPOS _T (X _s ). The region other than the octagon corresponding to reference numeral 24 representsNGT _T (X _s ) The region inside the octagon corresponding to reference numeral 24 and outside the rectangle corresponding to reference numeral 23 indicatesBN _T (X _s ). When picture elementxWhen it occurs in the positive domain or the negative domain,xandX _s is determined whenxWhen it is present in the boundary field,xandX _s is uncertain. Namely, the distribution situation of the image elements is determined according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain.

Specifically, with the determined distribution situation, determining the probability of the image element belonging to the set formed by the image elements of all surface feature types according to the Bayesian theorem, including: the probability that a pixel belongs to a set of pixels of each surface feature type can be expressed by the following formula:

，

wherein,E[i]representing the second on the set of picture elementsiA plurality of equivalence classes, each of which is defined as,G[s]is shown assThe category of the user is a category of the user,P(G[s]|E[i]) Representing equivalence classesiMiddle classsThe proportion of the pixels in the image is increased,P(E[i]|G[s]) Is expressed in all categories assIn the picture element of (1) belonging to the equivalence classiThe proportion of the pixels in the image is increased,P(E[i]) Representing equivalence classesiIn the mixed image element setUThe probability of occurrence of (a) in (b),P(G[s]) Representing categoriessIs inUThe probability of occurrence of (c).

Specifically, determining the secondary membership degree of each mixed pixel belonging to each surface feature type according to the probability of the determined set formed by the pixels of the pixel belonging to each surface feature type, comprising the following steps: the subordination degree of each mixed pixel belonging to each surface feature type can be expressed according to the following formula:

。

in an optional embodiment of the invention, by determining a set formed by pixels of all surface feature types, calculating an upper approximation, a lower approximation, a positive domain, a negative domain and a boundary domain of the set in an approximation space; determining the probability of a set formed by pixels of which the pixels belong to each surface feature type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain; and determining the secondary membership degree of each mixed pixel belonging to each surface feature type according to the probability of the determined set formed by the pixels of the pixel belonging to each surface feature type. In this process, since the discretization scheme is equivalent to dividing the set of mixed pixels into equivalence classes, elements in the same equivalence class have the same attribute value. Then the probability that the mixed pixels in the same equivalence class belong to each class can be considered as the same, and for the mixed pixels in different equivalence classes, the probability that the mixed pixels belong to each class is different, and the uncertainty caused by the difference is further fuzzification of the main membership. Therefore, the process of determining the secondary membership degree is equivalent to further fuzzifying the primary membership degree through the secondary membership degree, so that the uncertainty of the mixed pixel is accurately quantified and evaluated.

In an alternative embodiment of the present invention, in the step S14, the above step

The rough set of type II blur for each type of terrain is expressed by the following formula:

。

in an optional embodiment of the present invention, in step S15, performing a feature discretization process on the target remote sensing image data to obtain an optimal discretization result, where the method includes:

(1) acquiring an initial breakpoint set of a mixed pixel from remote sensing image data;

specifically, acquiring the initial breakpoint set of the mixed pixel from the remote sensing image data refers to acquiring the initial breakpoint set from the input remote sensing image features. The acquisition of the initial breakpoint set belongs to the mature prior art, and is not described in detail herein.

(2) Initializing a target remote sensing image data population based on the number of breakpoints of the initial breakpoint set;

specifically, the discretization is to adopt a certain specific method to divide the continuous features into a plurality of subintervals and associate the subintervals with the candidate breakpoints. Therefore, the characteristic discretization processing of the target remote sensing image can be regarded as selection of candidate breakpoints, and each discretization scheme corresponds to a division result on the mixed pixel set. The initial target remote sensing image data population is characterized in that the number of breakpoints of an initial breakpoint set is used as the individual length of the initial population, so that the initialization of the target remote sensing image data population is completed.

Exemplarily, assuming that the number of initial breakpoints is 10, since each population uses binary coding, the length of each individual is the number of initial breakpoints, that is, the length of each population is 10 bits, and corresponds to 10 breakpoints of the initial breakpoint set respectively.

(3) Iteratively executing a genetic algorithm on individuals of the target remote sensing image data population to determine an optimal discretization result; the discretization scheme corresponding to the initialized target remote sensing image data population is an initial discretization scheme, and each population individual corresponds to one discretization result.

Specifically, the discretization scheme corresponding to the individual in the initial population is an initial discretization scheme, and the discretization result corresponding to the initial discretization scheme is an initial discretization result.

Illustratively, assuming that the number of individuals in the target remote sensing image data population is 50, and the discretization scheme corresponding to the size of the target remote sensing image data population is 50, in each iteration, the 50 individuals in the population undergo selection, variation and intersection, i.e., the evolution function of the genetic algorithm, to update the population, so that the 50 individuals in the population of the next generation are different from the 50 individuals of the previous generation.

In an optional embodiment of the present invention, iteratively executing a genetic algorithm on individuals of the target remote sensing image data population to determine an optimal discretization result includes:

(1) determining a fuzzy relation between the mixed pixels based on Euclidean distances between the mixed pixels;

specifically, the fuzzy relationship between the mixed pixels can be expressed by the following formula:

，

，

wherein,d(x,y) To representxAndythe Euclidean distance of (a) is,x _h andy _h respectively representxAndyin thathPixel values over a band.

(2) Calculating the average approximate precision of the type II fuzzy rough set according to the fuzzy relation;

in an alternative embodiment of the present invention, calculating the average approximation accuracy of the type II fuzzy rough set based on the fuzzy relation comprises: calculating the upper approximation and the lower approximation of the type II fuzzy rough set according to the fuzzy relation; and calculating the average approximation precision of the fuzzy rough set II according to the determined upper approximation and the lower approximation.

Specifically, the upper approximation of the type II blur roughness set may be expressed by the following formula:

，

wherein,

，

，u2 is one of the primary degrees of membership,

，J _y2 the value range of the corresponding membership degree is represented,a(y2) representing mixed pixelsyA degree of sub-membership of 2,

and

are respectively

Minimum and maximum values of primary membership.

Specifically, the following approximation of the type II fuzzy rough set can be expressed as follows:

，

wherein,

，

，u1 is one of the primary degrees of membership,

，J _y1 the value range of the corresponding membership degree is represented,a(y1) representing mixed pixelsyA degree of sub-membership of 1.

Specifically, the average approximation accuracy of the type II fuzzy rough set can be expressed as follows:

，

wherein,

in order to average out the accuracy of the approximation,

indicating the approximate accuracy of the type II fuzzy rough set.

Specifically, the approximation accuracy of the type II fuzzy rough set can be expressed as follows:

wherein,x，y1，y2∈U。

illustratively, for

Then, the approximation accuracy of the type II fuzzy rough set can be expressed as follows:

。

(3) determining the reduction amplitude of the number of the breakpoints corresponding to the individual target remote sensing image data population according to the number of the breakpoints of the initial breakpoint set;

specifically, determining the reduction amplitude of the number of the breakpoints corresponding to the individual target remote sensing image data population according to the number of the breakpoints of the initial breakpoint set comprises the following steps: determining the length of the individual as the number of initial breakpoints; determining the selected length number of the individual as the number of the broken points of the corresponding individual; and determining the difference value between the length of the individual and the number of the individual broken points as the reduction range of the number of the broken points corresponding to the individual of the target remote sensing image data population.

Exemplarily, assuming that the number of initial breakpoints is 10, since each population uses binary coding, the length of each individual is the number of initial breakpoints, that is, the length of each population is 10 bits, and corresponds to 10 breakpoints of the initial breakpoint set respectively. Each bit in the binary code corresponds to a candidate breakpoint, and the values '1' and '0' represent that the breakpoint is selected and unselected, respectively. For each population individual, determining the number of selected binary codes as the number of breakpoint of the corresponding individual, namely determining the number of binary bits with the value of '1' to obtain the number of breakpoint of the individual. And determining the difference value between the length of the individual and the number of the individual broken points as the reduction amplitude of the number of the broken points of the corresponding individual, namely subtracting the number of the broken points of the individual from 10 to be equal to the reduction amplitude of the number of the broken points of the individual. For example, the binary code of an individual is 1110000111, the number of the fault points corresponding to the individual is 6, and the reduction of the number of the fault points of the individual is 4.

Specifically, the magnitude of the reduction in the number of breakpoints refers to the number of reductions in the number of breakpoints of individual population in each iteration process. The discrete effect is measured by comparing the quantity of the reduction of the number of the break points of the population in the iterative process, and the larger the reduction of the number of the break points is, the better the corresponding discrete effect is.

(4) Determining a fitness function of the II-type fuzzy rough set according to the amplitude of the reduction of the number of the fault points and the average approximate precision;

specifically, the fitness function of the type II fuzzy rough set is used for calculating the fitness value of each target remote sensing image data population individual, and the fitness function is formed by weighted summation of the amplitude of the reduction of the number of the fault points and the average approximate precision of the type II fuzzy rough set.

In an alternative embodiment of the invention, the fitness function for the type II fuzzy rough set is expressed by the following equation:

，

wherein,αandβis a weight coefficient, <' >DL is the magnitude of the reduction in the number of breakpoints,

is the average approximation accuracy.

Specifically, the weight coefficient is selected according to an actual working condition, and the reasonability of the weight setting is generally judged according to the characteristics of the data set and experimental observation, which is not specifically limited in the present application.

(5) And determining the fitness value of the II-type fuzzy rough set according to the fitness function of the II-type fuzzy rough set, and taking the individual of each target remote sensing image data population corresponding to the optimal fitness value as the optimal discretization result.

Specifically, a plurality of discretization schemes are used as population individuals in a genetic algorithm, the individuals with the maximum fitness value are searched through iterative calculation through the evolution function of the genetic algorithm, and the individuals with the maximum fitness value are used as the individuals with the optimal fitness value. And the discretization scheme corresponding to the optimal fitness value is the optimal discretization scheme.

Illustratively, assuming that there are 50 individuals in the target remote sensing image data population, the fitness function corresponding to the population individuals has 50 values. In each iteration, these 50 individuals in the population will undergo evolution to update the population. So that 50 individuals in the population of the next generation are different from 50 individuals of the previous generation. In each iteration process, the global variable records the individual with the highest fitness value in 50 individuals. When the fitness value of the next generation of existing individuals is higher than the fitness value of the individual of the global variable record, the global variable is updated with the individual having the higher fitness value. After all iterations are finished, the global variable records the optimal individual, and the discretization scheme corresponding to the optimal individual is the optimal discretization scheme.

In an alternative embodiment of the invention, the fitness function for the type II fuzzy rough set is constructed by calculating the average approximation accuracy of the type II fuzzy rough set and determining the magnitude of the reduction in the number of breakpoints. And determining an individual with an optimal fitness value through a genetic algorithm, describing fuzzy components in the remote sensing image characteristic discretization process by using the primary membership and the secondary membership corresponding to the mixed pixel, fuzzifying the discretization process by using the primary membership, further fuzzifying the primary membership by using the secondary membership, and obtaining a more accurate discretization result through a constructed fitness function of the II-type fuzzy rough set.

Example 2

The embodiment provides a remote sensing image feature discretization device based on a type II fuzzy rough model, as shown in fig. 3, fig. 3 is a connection diagram of the remote sensing image feature discretization device based on the type II fuzzy rough model according to an optional embodiment of the present invention, including: the image processing device comprises a mixed pixel extracting unit 31, a main membership degree determining unit 32, a secondary membership degree determining unit 33, a fuzzy rough set determining unit 34 and an optimal discretization result determining unit 35.

The mixed pixel extracting unit 31 is configured to acquire target remote sensing image data and extract mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types. For details, reference may be made to the related description of step S11 of any of the above method embodiments, and details are not repeated herein.

And the main membership determining unit 32 is configured to determine the main membership of each mixed pixel corresponding to each surface feature type according to the mixed pixels. For details, reference may be made to the related description of step S12 of any of the above method embodiments, and details are not repeated herein.

And the secondary membership degree determining unit 33 is configured to calculate the secondary membership degree of each mixed pixel belonging to each surface feature type according to the primary membership degree. For details, reference may be made to the related description of step S13 of any of the above method embodiments, and details are not repeated herein.

And a fuzzy rough set determining unit 34 configured to determine type II fuzzy rough sets for each feature type according to the primary and secondary membership degrees. For details, reference may be made to the related description of step S14 of any of the above method embodiments, and details are not repeated herein.

And the optimal discretization result determining unit 35 is configured to perform characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result. For details, reference may be made to the related description of step S15 of any of the above method embodiments, and details are not repeated herein.

The invention provides a remote sensing image characteristic discretization device based on a II-type fuzzy rough model, which comprises: and the mixed pixel extraction unit 31 is configured to acquire target remote sensing image data and extract mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types. And the main membership determining unit 32 is configured to determine the main membership of each mixed pixel corresponding to each surface feature type according to the mixed pixels. And the secondary membership degree determining unit 33 is configured to calculate the secondary membership degree of each mixed pixel belonging to each surface feature type according to the primary membership degree. And a fuzzy rough set determining unit 34 configured to determine a type II fuzzy rough set for each feature type according to the primary and secondary membership degrees. And the optimal discretization result determining unit 35 is configured to perform characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result. According to the embodiment of the invention, the rough set and the fuzzy set are combined, the fuzzy component in the remote sensing image characteristic discretization process is described by the primary membership and the secondary membership corresponding to the mixed pixel, the discretization process is fuzzified by the primary membership, and the primary membership is further fuzzified by the secondary membership, so that the uncertainty of the mixed pixel is accurately quantified and evaluated, and a more accurate discretization result is obtained.

In an alternative embodiment of the present invention, the primary membership determining unit 32 includes: and iteratively calculating the subunits and determining the subunits according to the main membership degree. The details can be seen in the related description of determining the main membership degree of each mixed image element corresponding to each surface feature type according to the mixed image elements in any of the above method embodiments.

And the iterative computation subunit is configured to iteratively compute a fuzzy mean vector and a fuzzy covariance matrix of a preset fuzzy partition matrix, wherein the preset fuzzy partition matrix is composed of membership degrees of the mixed pixels corresponding to various ground object types.

And the main membership degree determining subunit is configured to determine the main membership degree of each mixed pixel corresponding to each ground object type according to the fuzzy partition matrix when the iterative computation meets the iterative termination condition.

In an optional embodiment of the present invention, the sub-membership determining subunit includes an abundance determining subunit configured to determine, according to the fuzzy partition matrix, an abundance corresponding to each mixed pixel, and use the abundance as a main membership of each feature type corresponding to each mixed pixel. For details, reference may be made to the description of determining the main membership degree of each mixed pixel corresponding to each surface feature type according to the fuzzy partition matrix when the iterative computation satisfies the iterative termination condition in any of the above method embodiments.

In an alternative embodiment of the present invention, the secondary membership determining unit 33 includes: the hard segmentation matrix determines a subunit, the pixel set determines a subunit, the approximate space boundary determines a subunit, and the secondary membership determines a subunit. For details, reference may be made to the description related to calculating the secondary membership degree of each mixed image element to each surface feature type according to the primary membership degree in any of the above method embodiments.

And a hard segmentation matrix determination subunit configured to determine a hard segmentation matrix according to the fuzzy segmentation matrix when the iteration computation satisfies the iteration termination condition.

And the image element set determining subunit is configured to determine a set formed by image elements belonging to various surface feature types according to the hard segmentation matrix.

An approximation space boundary determining subunit configured to calculate an upper approximation, a lower approximation, a positive domain, a negative domain, a boundary domain aggregated in an approximation space.

And the secondary membership determining subunit is configured to determine the secondary membership of each mixed pixel belonging to each surface feature type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain.

In an optional embodiment of the present invention, the optimal discretization result determining unit 35 includes an initial breakpoint set obtaining subunit, a population initializing subunit, and an optimal discretization result determining subunit. For details, reference may be made to relevant description about discretization processing on target remote sensing image data to obtain an optimal discretization result in any of the above method embodiments.

And the initial breakpoint set acquisition subunit is configured to acquire an initial breakpoint set of the mixed pixel from the remote sensing image data.

And the population initialization subunit is configured to initialize the target remote sensing image data population based on the number of breakpoints of the initial breakpoint set. The discretization scheme corresponding to the initialized target remote sensing image data population is an initial discretization scheme, and each population individual corresponds to one discretization result.

And the optimal discretization result determining subunit is configured to iteratively execute a genetic algorithm on the individuals of the target remote sensing image data population to determine an optimal discretization result.

In an optional embodiment of the present invention, the optimal discretization result determining subunit includes: the method comprises a pixel fuzzy relation determining subunit, a calculating subunit, an amplitude determining subunit with reduced number of broken points, a fitness function determining subunit and a fitness value determining subunit. For details, reference may be made to any of the above-described method embodiments regarding iteratively performing a genetic algorithm on an individual of a target remote sensing image data population to determine a relevant description of an optimal discretization result.

And the inter-pixel fuzzy relation determining subunit is configured to determine the fuzzy relation between the mixed pixels based on the Euclidean distance between the mixed pixels.

And the calculating subunit is configured to calculate the average approximation precision of the type II fuzzy rough set according to the fuzzy relation.

And the amplitude determining subunit is configured to determine the amplitude of the reduction of the number of the breakpoints corresponding to the target remote sensing image data population individuals according to the number of the breakpoints of the initial breakpoint set.

And the fitness function determining subunit is configured to determine the fitness function of the type II fuzzy rough set according to the reduction amplitude of the number of the fault points and the average approximate precision.

And the fitness value determining subunit is configured to determine the fitness value of the type II fuzzy rough set according to the fitness function of the type II fuzzy rough set, and take the individual of each target remote sensing image data population corresponding to the optimal fitness value as an optimal discretization result.

The invention provides a remote sensing image characteristic discretization device based on a II-type fuzzy rough model, which comprises: and the mixed pixel extraction unit 31 is configured to acquire target remote sensing image data and extract mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types. And the main membership determining unit 32 is configured to determine the main membership of each mixed pixel corresponding to each surface feature type according to the mixed pixels. And the secondary membership degree determining unit 33 is configured to calculate the secondary membership degree of each mixed pixel belonging to each surface feature type according to the primary membership degree. And a fuzzy rough set determining unit 34 configured to determine type II fuzzy rough sets for each feature type according to the primary and secondary membership degrees. And the optimal discretization result determining unit 35 is configured to perform characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result. According to the embodiment of the invention, the rough set and the fuzzy set are combined, the fuzzy component in the remote sensing image characteristic discretization process is described by the primary membership and the secondary membership corresponding to the mixed pixel, the discretization process is fuzzified by the primary membership, and the primary membership is further fuzzified by the secondary membership, so that the uncertainty of the mixed pixel is accurately quantified and evaluated, and a more accurate discretization result is obtained.

Example 3

An embodiment of the present invention also provides a non-transitory computer storage medium having stored thereon computer-executable instructions that may perform the method described in any of the method embodiments above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

Example 4

An embodiment of the present invention further provides a computer device, as shown in fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, and the computer device may include at least one processor 41, at least one communication interface 42, at least one communication bus 43, and at least one memory 44, where the communication interface 42 may include a Display (Display) and a Keyboard (Keyboard), and the alternative communication interface 42 may also include a standard wired interface and a standard wireless interface. The Memory 44 may be a high-speed RAM Memory (volatile Random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 44 may alternatively be at least one memory device located remotely from the aforementioned processor 41. Wherein the processor 41 may be in connection with the apparatus described in fig. 3, the memory 44 stores an application program, and the processor 41 calls the program code stored in the memory 44 for performing the steps of the method described in any of the above method embodiments.

The communication bus 43 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.

The memory 44 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (such as a flash memory), a hard disk (HDD) or a solid-state drive (SSD); the memory 44 may also comprise a combination of the above-mentioned kinds of memories.

The processor 41 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of CPU and NP.

The processor 41 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.

Optionally, the memory 44 is also used to store program instructions. Processor 41 may call program instructions to implement the methods described in any of the embodiments of the present invention.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims

1. A remote sensing image characteristic discretization method based on a II-type fuzzy rough model is characterized by comprising the following steps: acquiring target remote sensing image data, and extracting mixed pixels from the target remote sensing image data, wherein each mixed pixel respectively comprises spectral response characteristics of multiple surface feature types; determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; calculating the secondary membership degree of each mixed pixel belonging to each ground object type according to the primary membership degree; determining a II-type fuzzy rough set of each ground object type according to the primary membership degree and the secondary membership degree; and carrying out characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result.

2. The remote sensing image characteristic discretization method based on the II-type fuzzy rough model, according to the claim 1, wherein the determining the main membership degree of each mixed pixel corresponding to each surface feature type according to the mixed pixels comprises: iteratively calculating a fuzzy mean vector and a fuzzy covariance matrix of a preset fuzzy partition matrix, wherein the preset fuzzy partition matrix is composed of membership degrees of mixed pixels corresponding to various ground object types; and determining the main membership degree of each mixed pixel corresponding to each ground feature type according to the fuzzy partition matrix when the iterative computation meets the iterative termination condition.

3. The remote sensing image characteristic discretization method based on the II-type fuzzy rough model, according to the fuzzy segmentation matrix when the iteration calculation meets the iteration termination condition, determining the main membership degree of each mixed pixel corresponding to each ground object type, comprising: and determining the abundance corresponding to each mixed pixel according to the fuzzy partition matrix, and taking the abundance as the main membership degree of each ground object type corresponding to each mixed pixel.

4. The remote sensing image characteristic discretization method based on the fuzzy rough model type II according to claim 1, wherein the calculating of the secondary membership degree of each mixed pixel belonging to each surface feature type according to the primary membership degree comprises: determining a hard segmentation matrix according to a fuzzy segmentation matrix when iteration calculation meets an iteration termination condition; determining a set formed by pixels belonging to each surface feature type according to the hard segmentation matrix; calculating upper approximation, lower approximation, a positive domain, a negative domain, and a boundary domain of the set in an approximation space; and determining the secondary membership degree of each mixed pixel belonging to each ground object type according to the upper approximation, the lower approximation, the positive domain, the negative domain and the boundary domain.

5. The remote sensing image feature discretization method based on the fuzzy rough model type II according to claim 1, wherein the performing the feature discretization on the target remote sensing image data to obtain an optimal discretization result comprises: acquiring an initial breakpoint set of the mixed pixel from the remote sensing image data; initializing the target remote sensing image data population based on the number of breakpoints of the initial breakpoint set; iteratively executing a genetic algorithm on the individual of the target remote sensing image data population to determine an optimal discretization result; and the discretization scheme corresponding to the initialized target remote sensing image data population is an initial discretization scheme, and each population individual corresponds to one discretization result.

6. The remote sensing image characteristic discretization method based on the fuzzy rough model type II according to claim 5, wherein the step of iteratively executing a genetic algorithm on the individuals of the target remote sensing image data population to determine an optimal discretization result comprises the following steps: determining a fuzzy relation between the mixed pixels based on the Euclidean distance between the mixed pixels; calculating the average approximate precision of the type II fuzzy rough set according to the fuzzy relation; determining the reduction amplitude of the number of the breakpoints corresponding to the target remote sensing image data population individuals according to the number of the breakpoints of the initial breakpoint set; determining a fitness function of the II-type fuzzy rough set according to the amplitude of the reduction of the number of the fault points and the average approximate precision; and determining the fitness value of the II-type fuzzy rough set according to the fitness function of the II-type fuzzy rough set, and taking the individual of each target remote sensing image data population corresponding to the optimal fitness value as the optimal discretization result.

7. The remote sensing image characteristic discretization method based on the type II fuzzy rough model, according to the claim 6, wherein the fitness function of the type II fuzzy rough set is expressed by the following formula:

，

is the average approximation accuracy.

8. The utility model provides a remote sensing image characteristic discretization device based on rough model of II type fuzzy, its characterized in that includes: the mixed pixel extraction unit is configured to acquire target remote sensing image data and extract mixed pixels from the target remote sensing image data, and each mixed pixel comprises spectral response characteristics of multiple surface feature types; the main membership degree determining unit is configured to determine the main membership degree of each mixed pixel corresponding to each ground feature type according to the mixed pixels; the secondary membership degree determining unit is configured to calculate the secondary membership degree of each mixed pixel belonging to each ground feature type according to the primary membership degree; a fuzzy rough set determining unit configured to determine a type II fuzzy rough set of each ground object type according to the primary membership and the secondary membership; and the optimal discretization result determining unit is configured to perform characteristic discretization processing on the target remote sensing image data to obtain an optimal discretization result.

9. A non-transitory computer-readable storage medium storing computer instructions which, when executed by a processor, implement the method for discretizing a feature of a remote sensing image based on a fuzzy rough model of type II according to any one of claims 1 to 7.

10. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method of discretizing a feature of a remotely sensed image based on a fuzzy coarse model of type II according to any of claims 1-7.