CN109446894B - Multispectral image change detection method based on probability segmentation and Gaussian mixture clustering - Google Patents
Multispectral image change detection method based on probability segmentation and Gaussian mixture clustering Download PDFInfo
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Abstract
The invention belongs to the technical field of methods or devices for identifying by using electronic equipment, and discloses a multispectral image change detection method based on probability segmentation and Gaussian mixture clustering; firstly, inputting two original multispectral images in the same region and at different time, and constructing a mixed difference image HDS by using a CVA and an SAM; secondly, performing multi-scale segmentation on the difference image by using a statistical region merging algorithm to map the difference image to a super-pixel space; and finally, initializing a Gaussian mixture model by adopting a K-means algorithm to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, fitting the probability statistical distribution of the super-pixel feature space, and obtaining a change detection result by utilizing a Bayes judgment rule based on the minimum error rate. The method better utilizes the amplitude change information and the angle change information of the spectral vector, can obtain the local structural characteristics of the image by utilizing the superpixel segmentation, and effectively improves the detection accuracy of the change region in the SAR image.
Description
Technical Field
The invention belongs to the technical field of methods or devices for identification by using electronic equipment, and particularly relates to a multispectral image change detection method based on probability segmentation and Gaussian mixture clustering.
Background
Currently, the current state of the art commonly used in the industry is such that: with the rapid development of remote sensing technology in recent years, the quantity of remote sensing data is increasing day by day, and the remote sensing data is widely used in the fields of environmental monitoring, atmospheric analysis, urban planning and the like. The change detection of the multispectral remote sensing image is applied to the fields of military affairs and civilian use, and mainly relates to positioning of natural disaster areas such as flood, fire and earthquake, urban expansion condition analysis and evaluation of striking effect in military application, so that the change detection of the multispectral remote sensing image has important significance in research. The change detection of the multispectral remote sensing image is to determine whether the ground feature changes according to the difference of the spectral characteristics or the spatial structure of the image. The general remote sensing image change detection process comprises two steps: 1) constructing a difference image; 2) and analyzing the difference image to obtain a detection result. The method is essentially a two-classification problem, namely that all pixel points of the difference image are classified into a variation class and a non-variation class. In the past decade, a large number of change detection methods have been proposed, which can be classified into a pixel-based method and an object-based method, depending on the basic unit of image analysis. The pixel-based method generally uses the gray features of the difference pixels for classification, and uses a clustering method to obtain a classification result as good as possible. However, as the resolution of the remote sensing image is continuously improved, the spatial cross correlation between the pixel and the neighborhood of the pixel is stronger and stronger, and a single pixel feature does not have a semantic feature, so that the semantic gap problem exists, so that the object-based analysis method, namely, the image is divided into homogeneous regions to obtain semi-semantic information, becomes a key technology for image understanding and identification. The object-based change detection method takes superpixels as basic units of analysis and contains rich information, such as: spectrum, texture, shape, spatial context, etc. For example, an SAR image change detection method based on superpixel segmentation and multi-method fusion firstly introduces an SLIC superpixel segmentation method, and obtains a superpixel segmentation result conforming to the actual ground object boundary by performing joint segmentation on a main image and an auxiliary image; meanwhile, 3 pixel-based change detection methods are used for obtaining an initial change detection result; and then, performing mode voting of two levels by using the super-pixel segmentation result and the initial change detection result, and removing false alarms caused by noise in the detection result and holes in a connected domain to obtain a final change detection result. However, in the method, original double-temporal remote sensing images are respectively segmented, and then distance measurement values between super pixels are calculated, and due to the difference of the shape and the structure of a target between the double-temporal images, a boundary needs to be registered before calculating a similar distance, so that the geometric information of the target is damaged.
In order to solve the problems, the invention adopts a multispectral image change detection method based on probability segmentation and Gaussian mixture clustering. In order to solve the problems that a change detection method based on a single pixel does not have semantic information and is high in calculation complexity, the image is mapped to a superpixel space from a pixel space by adopting superpixel segmentation, and regional characteristics of a change target can be better reflected; meanwhile, the Gaussian mixture model is adopted to cluster the superpixels, so that the purposes of eliminating the edge effect and improving the detection precision can be achieved, and a clearer physical significance is provided for solving the problem of change detection.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a multispectral image change detection method based on probability segmentation and Gaussian mixture clustering.
The invention is realized by the following steps: firstly, inputting two original multispectral images in the same region and at different time, and constructing a mixed difference image HDS by using a CVA and an SAM; secondly, performing multi-scale segmentation on the difference image by utilizing a statistical region merging algorithm of a dynamic sequencing mode to map the difference image to a super-pixel space; and finally, initializing a Gaussian mixture model by adopting a K-means algorithm to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, fitting the probability statistical distribution of the super-pixel feature space, and obtaining a change detection result by utilizing a Bayes judgment rule based on the minimum error rate.
Further, the multispectral image change detection method based on probability segmentation and Gaussian mixture clustering specifically comprises the following steps:
(1) inputting two multispectral remote sensing images X in different time same regions1And X2；
(2) To X1And X2The two images respectively obtain the angle change mapping theta and the change vector CV of the two images by using SAM and CVA, and a new mixed difference space HDS is constructed by combining the two change characteristics;
(3) merging the pixel points in the mixed difference image DI by using a statistical region merging algorithm to complete the conversion of the difference image from a pixel space to a super pixel space to obtain a merged image DT;
(4) the pixel mean values of each super pixel in the statistical merged image DT form a set X ═ Xn1 ≦ N, where N represents the total number of superpixels;
(5) the mixed probability model classification adopts a K-means algorithm to initialize a Gaussian mixture model to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, and the probability statistical distribution of the super-pixel feature space is fitted:
(6) and obtaining a final detection result of each super pixel by adopting a Bayes discrimination rule based on a minimum error rate, wherein the posterior probability is calculated as follows according to a Bayes formula:
then the bayesian decision flow rule based on the minimum error rate is as follows:
if it is
X is thennIs determined to be changed;
if it is
X is thennIs determined to be unchanged.
Further, the (5) specifically includes:
a) all data elements in the set are assumed to have a gaussian mixture distribution with two models:
wherein the content of the first and second substances,
the parameters to be solved are: mean and variance of Gaussian functions of varying and non-varying classes
And alpha1Or alpha2；
b) Solving parameter values by adopting an EM algorithm: defining the number of components k to 2, and setting a parameter mu for each component kk、σkAnd alphakWherein a k-means algorithm is used to calculate the cluster center as μkThe initial value of the parameter is used for avoiding the problem that the EM algorithm is easy to be trapped in a local optimal solution;
c) the EM algorithm finds a rough value of the parameter to be estimated:
according to the current muk，σkAnd alphakCalculating the posterior probability gamma (z)nk)：
d) The EM algorithm maximizes the likelihood function using the values of the first step:
according to gamma (z)nk) Calculating model parameter mu of new iterationk (t+1)，σk (t+1)，αk (t+1)：
e) Calculating a log-likelihood function of the Gaussian mixture model:
f) judging whether the likelihood function is converged: if converging, then the parameters are output
And alpha1And alpha2(ii) a If not, returning to the step c) to execute until meeting the convergence condition.
Another objective of the present invention is to provide a spectral image processing system applying the multispectral image change detection method based on probability segmentation and gaussian mixture clustering.
Another objective of the present invention is to provide an environment monitoring spectral image processing system applying the multispectral image change detection method based on probability segmentation and gaussian mixture clustering.
Another objective of the present invention is to provide an atmospheric analysis spectral image processing system applying the multispectral image change detection method based on probability segmentation and gaussian mixture clustering.
The invention further aims to provide a city planning spectral image processing system applying the multispectral image change detection method based on probability segmentation and Gaussian mixture clustering.
In summary, the advantages and positive effects of the invention are: in the process of generating the difference image, a mixed difference image is constructed by combining the CVA and the SAM; compared with the prior art, the method for generating the difference image by using a difference method or only using CVA and the like is adopted; amplitude change information and angle change information of the multispectral image can be extracted simultaneously, and the precision of the difference image is improved;
the mixed difference image is subjected to multi-scale segmentation by adopting a statistical region merging algorithm, the similarity between adjacent pixels and the local structural characteristics of the image are fully utilized, and the difference image is mapped to a super-pixel space from the pixel space, so that a low-dimensional semi-semantic clustering space for change detection is obtained, and the calculation complexity is reduced;
according to the method, the Gaussian mixture model is adopted to perform spatial clustering on the superpixels so as to obtain a final change detection result, and aiming at the problem of initial value sensitivity of the Gaussian mixture model algorithm, the Gaussian mixture model initialized based on K-means is introduced to avoid a local optimal phenomenon and improve the convergence speed of the model. Compared with the classical clustering methods such as K-means and FCM, the Gaussian mixture model can achieve the purposes of eliminating the edge effect and improving the detection precision.
Drawings
Fig. 1 is a flowchart of a multispectral image change detection method based on probability segmentation and gaussian mixture clustering according to an embodiment of the present invention.
Fig. 2 is a flowchart of an implementation of the multispectral image change detection method based on probability segmentation and gaussian mixture clustering according to the embodiment of the present invention.
Fig. 3 is a diagram illustrating the results of detecting changes in the seian river channel data set according to an embodiment of the present invention.
FIG. 4 is a diagram illustrating the result of performing change detection on a west amp dam building data set according to an embodiment of the present invention.
FIG. 5 is a diagram of a conventional M provided by an embodiment of the present invention2C2And a change detection result graph of the VA algorithm and the AFS algorithm on the Xian river channel data set.
FIG. 6 is a diagram of a conventional M provided by an embodiment of the present invention2C2And (3) detecting a result graph of the change of the data set of the west-safe dam building by using the VA algorithm and the AFS algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problem of low detection accuracy of a change region in the existing multispectral image; the invention provides a multispectral image change detection method based on probability segmentation and Gaussian mixture clustering, which is used for realizing detection of a change area in a multispectral image and improving the detection accuracy.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the multispectral image change detection method based on probability segmentation and gaussian mixture clustering provided by the embodiment of the present invention includes the following steps:
s101: the capabilities that the CVA and the SAM can describe difference information from two different angles of the size and the direction of two spectral vectors are utilized, and a mixed difference image is constructed by combining the two methods;
s102: carrying out multi-scale segmentation on the difference image by utilizing a statistical region merging algorithm of a dynamic sequencing mode to map the difference image to a super-pixel space;
s103: a Gaussian mixture model is initialized by adopting a K-means algorithm to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, probability statistical distribution of a super-pixel feature space is fitted, and a change detection result is obtained by utilizing a Bayes judgment rule based on the minimum error rate.
The multispectral image change detection method based on probability segmentation and Gaussian mixture clustering provided by the embodiment of the invention specifically comprises the following steps:
(1) inputting two multispectral remote sensing images X in different time same regions1And X2；
(2) To X1And X2The two images respectively obtain the angle change mapping theta and the change loss CV of the two images by using SAM and CVA, and a new mixed difference space HDS is constructed by combining the two change characteristics;
(3) merging the pixel points in the mixed difference image DI by using a statistical region merging algorithm to complete the conversion of the difference image from a pixel space to a super pixel space to obtain a merged image DT;
(4) the pixel mean values of each super pixel in the statistical merged image DT form a set X ═ Xn1 ≦ N, where N represents the total number of superpixels;
(5) the mixed probability model classification adopts a K-means algorithm to initialize a Gaussian mixture model to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, and the probability statistical distribution of the super-pixel feature space is fitted:
5a) all data elements in the set are assumed to have a gaussian mixture distribution with two models:
wherein the content of the first and second substances,
the parameters to be solved are: mean and variance of Gaussian functions of varying and non-varying classes
And alpha1Or alpha2；
5b) Solving parameter values by adopting an EM algorithm: defining the number of components k to 2, and setting a parameter mu for each component kk、σkAnd alphakWherein a k-means algorithm is used to calculate the cluster center as μkThe initial value of the parameter is used for avoiding the problem that the EM algorithm is easy to be trapped in a local optimal solution;
5c) the first step of the EM algorithm, finds the rough values of the parameters to be estimated:
according to the current muk，σkAnd alphakCalculating the posterior probability gamma (z)nk)：
5d) The second step of the EM algorithm, using the values of the first step to maximize the likelihood function:
according to gamma (z)nk) Calculating model parameter mu of new iterationk (t+1)，σk (t+1)，αk (t+1)：
5e) Calculating a log-likelihood function of the Gaussian mixture model:
5f) judging whether the likelihood function is converged: if converging, then the parameters are output
And alpha1And alpha2(ii) a If not, returning to the step 5c) to execute until meeting the convergence condition.
(6) And obtaining a final detection result of each super pixel by adopting a Bayesian discrimination rule based on the minimum error rate. According to the Bayesian formula, the posterior probability is calculated as follows:
then the bayesian decision flow rule based on the minimum error rate is as follows:
if it is
X is thennIs determined to be changed;
if it is
X is thennIs determined to be unchanged.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 2, the multispectral image change detection method based on probability segmentation and gaussian mixture clustering provided by the embodiment of the present invention specifically includes the following steps:
step one, inputting two multispectral remote sensing images X in different time same regions1And X2；
Step two, for X1And X2The two images respectively obtain the angle mapping theta and the variation loss CV by using SAM and CVA, and a new mixed difference space HDS is constructed by combining two variation characteristics:
2a) order S1＝(x1 1，x1 2，...x1 L) And S2＝(x2 1，x2 2，...x2 L) Representing a set of multi-spectral images X1And X2The intermediate coordinate is a spectral vector of (i, j), and the multispectral image contains four bands of red, green, blue and near infrared, so that L is 4, and the angle mapping θ is solved:
wherein the content of the first and second substances,
representing a spectral vector S1The b-th component.
2b) Calculating the change vector CV:
2c) the mixed difference vector HDS is constructed by combining two variation features:
wherein, theta*α θ (α ═ max (cv))/k, k is a constant, and k is set to 45 herein.
2d) Obtaining a mixed difference image DI by one-dimensional mapping the mixed difference space:
DI＝||HDS||2；
combining pixel points in the mixed difference image DI by using a statistical region combination algorithm to complete the conversion of the difference image from a pixel space to a region space to obtain a combined image DT;
3a) a similarity weight is calculated for each pair of pixels in the mixed difference image DI:
f(p,p')＝|p-p'|；
where p and p' represent the gray values of two adjacent pixels or the pixel mean of two adjacent regions.
3b) And sequencing the similarity weights in an ascending order from small to large, and sequentially selecting the pixel pairs according to the sequencing order to judge whether the pixel pairs are combined:
if it satisfies
Then the merging is carried out, wherein,
represents the average pixel value of the R region. R|R|Representing a set of regions with R pixels,
the method comprises the following steps that I is an empirical constant, the number of pixels contained in an image is represented by I, and Q represents the complexity of a real scene; otherwise, merging; when each group of pixel pairs completes the judgment process, the merged image DT is obtained.
Step four, the pixel mean value of each super pixel in the statistical merging image DT forms a set X ═ XnAnd |1 is less than or equal to N is less than or equal to N, and N represents the total number of the superpixels. The calculation of the mean pixel value of the superpixel comprises the following steps: for each super pixel, calculating the sum of pixel values of all pixel points contained in the region of each super pixel, and dividing the sum by the number of the pixel points to obtain the pixel of the super pixelAnd (4) average value.
Step five, fitting the probability statistical distribution of the super-pixel feature space by adopting a Gaussian mixture model:
5a) all data elements in the set are assumed to have a gaussian mixture distribution with two models, namely:
wherein the content of the first and second substances,
the parameters to be solved are: mean and variance of Gaussian functions of varying and non-varying classes
And alpha1And alpha2；
5b) Solving the parameter values using the EM algorithm:
defining the number of components k to 2, and setting a parameter mu for each component kk、σkAnd alphakWherein a k-means algorithm is used to calculate the cluster center as μkInitial values of the parameters to avoid the problem of the EM algorithm being prone to local optimal solution.
The k-means algorithm accepts the parameter k 2, performs clustering with k points in space as the center, and classifies the objects closest to them. And (4) gradually updating the value of each clustering center through an iterative method until the best clustering result is obtained. Assuming that the sample set is to be divided into k classes, the algorithm is described as follows: (1) properly selecting initial centers (2) of k classes, in the ith iteration, calculating the distances from any sample to the k centers, updating the center values (4) of the classes by using methods such as mean values and the like for the class (3) of which the sample is classified to the center with the shortest distance, and ending the iteration if the values are kept unchanged after the updating by using the iteration methods of (2) and (3), otherwise, continuing the iteration. E.g. the set of raw data is (x)1,x2,…,xn) And each xiFor d-dimensional vectors, the purpose of k-means clusteringThat is, given a number of classification groups k, where k ≦ n, the raw data is classified into k classes S ═ S1,S2,…,SkIts objective function is as follows:
5c) the first step of the EM algorithm, finds the rough values of the parameters to be estimated:
according to the current muk，σkAnd alphakCalculating the posterior probability gamma (z)nk)：
5d) The second step of the EM algorithm, using the values of the first step to maximize the likelihood function:
according to gamma (z)nk) Calculating model parameter mu of new iterationk (t+1)，σk (t+1)，αk (t+1)：
5e) Calculating a log-likelihood function of the Gaussian mixture model:
5f) judging whether the likelihood function is converged: if converging, then the parameters are output
And alpha1And alpha2(ii) a If not, returning to the step 5c) to execute until meeting the convergence condition.
And 6, obtaining a final detection result of each super pixel by adopting a Bayes judgment rule based on the minimum error rate. According to the Bayesian formula, the posterior probability is calculated as follows:
then the bayesian decision flow rule based on the minimum error rate is as follows:
if it is
X is thennIs determined to be changed;
if it is
X is thennIs determined to be unchanged.
The application effect of the present invention will be described in detail with reference to simulation experiments.
1. Simulation conditions
The invention uses MATLAB R2016a to complete the simulation experiment of the invention on a PC with a CPU of Intel (R) Core i5-4590, CPU3.30GHz, RAM 8.00GB and Windows 7 operating system.
2. Content of simulation experiment
In the experiment, two data sets of the Xian river channel and the Xian dam building are selected for verification of change detection results, the size of the image of the Xian river channel is 301 multiplied by 4, and the size of the image of the data set of the Xian 27984 dam building is 134 multiplied by 132 multiplied by 4, wherein the data set of the Xian dam building comprises 4 wave bands of blue, green, red and near infrared.
Simulation 1, the method of the invention is adopted to perform change detection on the data sets of the canal of west ampere and the data sets of the west ampere dam building, and the detection results are shown in fig. 3 and 4, wherein:
(1) FIG. 3(a) shows a pre-change image in the canal dataset of Xian ;
(2) FIG. 3(b) shows a post-alteration image of the canal data set of Xian ;
(3) FIG. 3(c) is a graph showing a standard reference variation in the canal data set of Xian ;
(4) FIG. 3(d) is a graph showing the results of multispectral image change detection using the present invention;
(5) FIG. 4(a) shows a pre-change image in a Seaman dam building data set;
(6) FIG. 4(b) shows a post-change image in a Seisan dam building data set;
(7) FIG. 4(c) shows a standard reference variation in the Seaman dam building data set;
(8) FIG. 4(d) is a graph showing the results of multispectral image change detection using the present invention;
(1) FIG. 5(a) shows that the Seaman river channel data set employs M2C2Detecting the result of the VA algorithm;
(2) FIG. 5(b) shows the detection result of the river channel data set of Xian using the AFS algorithm;
(3) FIG. 5(c) shows the results of a detection of the Seaman river channel data set using the method of the present invention;
(4) FIG. 6(a) shows that the Seaman dam building data set employs M2C2Detecting the result of the VA algorithm;
(5) FIG. 6(b) shows the detection result of the Western Ann dam building data set using the AFS algorithm;
(6) FIG. 6(c) shows the results of a detection of the Seaman dam building data set using the method of the present invention.
3. Simulation experiment results and analysis
As can be seen from fig. 3, the method of the present invention can effectively detect the changed regions in the multispectral image. From FIG. 4 canIt is seen that in contrast to M2C2VA algorithm and AFS algorithm, the method of the invention can effectively improve the detection accuracy of the change area in the multispectral image. The method has the advantages of high detection precision, completeness maintenance of target geometric details and remarkable improvement of the performance of multispectral image change detection.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A multispectral image change detection method based on probability segmentation and Gaussian mixture clustering is characterized by comprising the following steps: firstly, inputting two original multispectral images in the same region and at different time, and constructing a mixed difference image HDS by using a CVA and an SAM; secondly, performing multi-scale segmentation on the difference image by using a statistical region merging algorithm to map the difference image to a super-pixel space; finally, initializing a Gaussian mixture model by adopting a K-means algorithm to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, fitting the probability statistical distribution of the super-pixel feature space, and obtaining a change detection result by utilizing a Bayes judgment rule based on the minimum error rate;
the multispectral image change detection method based on probability segmentation and Gaussian mixture clustering specifically comprises the following steps:
(1) inputting two multispectral remote sensing images X in different time same regions1And X2；
(2) To X1And X2The two images respectively obtain the angle change mapping theta and the change loss CV of the two images by using SAM and CVA, and a new mixed difference image HDS is constructed by combining the two change characteristics;
(3) merging the pixel points in the mixed difference image HDS by using a statistical region merging algorithm to complete the conversion of the difference image from a pixel space to a super pixel space to obtain a merged image DT;
(4) the pixel mean values of each super pixel in the statistical merged image DT form a set X ═ Xn|1≤n≤NN denotes the total number of superpixels;
(5) the mixed probability model classification adopts a K-means algorithm to initialize a Gaussian mixture model to overcome the defect that the Gaussian mixture model is easy to converge on a local optimal solution, and the probability statistical distribution of the super-pixel feature space is fitted:
(6) and obtaining a final detection result of each super pixel by adopting a Bayes discrimination rule based on a minimum error rate, wherein the posterior probability is calculated as follows according to a Bayes formula:
then the bayesian decision flow rule based on the minimum error rate is as follows:
if it is
X is thennIs determined to be changed;
if it is
X is thennIs determined to be unchanged;
the (5) specifically includes:
a) all data elements in the set are assumed to have a gaussian mixture distribution with two models:
wherein the content of the first and second substances,
the parameters to be solved are: mean and variance of Gaussian functions of varying and non-varying classes
And alpha1Or alpha2；
b) By usingSolving parameter values by an EM algorithm: defining the number of components k to 2, and setting a parameter mu for each component kk、σkAnd alphakWherein a k-means algorithm is used to calculate the cluster center as μkThe initial value of the parameter is used for avoiding the problem that the EM algorithm is easy to be trapped in a local optimal solution;
c) the EM algorithm finds a rough value of the parameter to be estimated:
according to the current muk，σkAnd alphakCalculating the posterior probability gamma (z)nk)：
d) The EM algorithm maximizes the likelihood function using the values of the first step:
according to gamma (z)nk) Model parameter calculation for new iteration
e) Calculating a log-likelihood function of the Gaussian mixture model:
f) judging whether the likelihood function is converged: if converging, then the parameters are output
And alpha1And alpha2(ii) a If not, returning to the step c) to execute until meeting the convergence condition.
2. A spectral image processing system applying the multispectral image change detection method based on probability segmentation and gaussian mixture clustering according to claim 1.
3. An environment monitoring spectral image processing system applying the multispectral image change detection method based on probability segmentation and Gaussian mixture clustering of claim 1.
4. An atmospheric analysis spectral image processing system applying the multispectral image change detection method based on probability segmentation and Gaussian mixture clustering according to claim 1.
5. A city planning spectral image processing system applying the probability segmentation and Gaussian mixture clustering based multispectral image change detection method of claim 1.
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