CN111062445A - Polarimetric SAR Image Classification Method Based on Co-regularization and Superpixels - Google Patents

Polarimetric SAR Image Classification Method Based on Co-regularization and Superpixels Download PDF

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CN111062445A
CN111062445A CN201911333974.0A CN201911333974A CN111062445A CN 111062445 A CN111062445 A CN 111062445A CN 201911333974 A CN201911333974 A CN 201911333974A CN 111062445 A CN111062445 A CN 111062445A
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黄夏渊
聂祥丽
乔红
张波
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Institute of Automation of Chinese Academy of Science
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Abstract

本发明属于图像处理技术领域,具体涉及一种基于协同正则化和超像素的极化SAR图像分类方法、系统、装置,旨在解决现有极化SAR图像分类方法分类精度低的问题。本系统方法包括基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;提取各超像素预设维度的极化特征;基于各超像素的相干矩阵、极化特征计算其与其他超像素的Wishart距离、欧式距离,并构建各超像素的第一、第二权重图;通过基于协同正则化的降维模型得到第一低维特征、第二低维特征;通过最近邻分类器得到极化SAR图像的分类结果。本发明根据像素点的空间信息,通过结合Wishart距离和极化特征,提高了极化SAR图像的分类精度。

Figure 201911333974

The invention belongs to the technical field of image processing, and in particular relates to a polarization SAR image classification method, system and device based on collaborative regularization and superpixels, and aims to solve the problem of low classification accuracy of the existing polarization SAR image classification methods. The system method includes obtaining a plurality of superpixels and their corresponding coherence matrices through a superpixel generation method based on the acquired polarimetric SAR images; extracting the polarization characteristics of each superpixel preset dimension; Calculate the Wishart distance and Euclidean distance between the features and other superpixels, and construct the first and second weight maps of each superpixel; obtain the first low-dimensional feature and the second low-dimensional feature through the dimensionality reduction model based on collaborative regularization ; Obtain the classification results of polarimetric SAR images through the nearest neighbor classifier. According to the spatial information of pixel points, the invention improves the classification accuracy of polarimetric SAR images by combining Wishart distance and polarimetric features.

Figure 201911333974

Description

基于协同正则化和超像素的极化SAR图像分类方法Polarimetric SAR Image Classification Method Based on Co-regularization and Superpixels

技术领域technical field

本发明属于图像处理技术领域,具体涉及一种基于协同正则化和超像素的极化SAR图像分类方法、系统、装置。The invention belongs to the technical field of image processing, and in particular relates to a polarization SAR image classification method, system and device based on collaborative regularization and superpixels.

背景技术Background technique

极化SAR不仅能全天时、全天候的获得大面积高分辨率图像,而且能利用多种极化组合获得详细、丰富的地物信息。因而被广泛用于军事和民用领域。其中,地物分类是极化SAR图像解译的重要内容。Polarimetric SAR can not only obtain large-area high-resolution images in all-day and all-weather, but also obtain detailed and rich ground object information by using a variety of polarization combinations. Therefore, it is widely used in military and civilian fields. Among them, the classification of ground objects is an important part of polarimetric SAR image interpretation.

Lee等人在1994年提出了一种基于Wishart距离的分类方法,成为最经典的方法之一(参见参考文献:J.S.Lee,M.R.Grunes,R.Kwok,"Classification of multi-lookpolarimetric SAR imagery based on complex Wishart distribution",Int.J.RemoteSensing,vol.15,no.11,1994.)。该方法用到的Wishart距离是由极化SAR数据的分布通过最大似然推导而来,因而适用于极化SAR图像分类,并被广泛应用。Lee et al. proposed a Wishart distance-based classification method in 1994, which became one of the most classic methods (see references: J.S.Lee, M.R.Grunes, R.Kwok, "Classification of multi-lookpolarimetric SAR imagery based on complex Wishart distribution", Int. J. RemoteSensing, vol. 15, no. 11, 1994.). The Wishart distance used in this method is derived from the distribution of polarimetric SAR data through maximum likelihood, so it is suitable for polarimetric SAR image classification and is widely used.

1999年,Lee等人结合H/A/α极化目标分解和Wishart分类器,用于极化SAR图像分类(参见参考文献:Jong-Sen Lee,M.R.Grunes,T.L.Ainsworth,Li-Jen Du,D.L.Schulerand S.R.Cloude,"Unsupervised classification using polarimetric decompositionand the complex Wishart classifier,"in IEEE Transactions on Geoscience andRemote Sensing,vol.37,no.5,pp.2249-2258,Sept.1999.)。Anfinsen等人提出了基于Wishart距离的谱聚类方法,作为Wishart分类器的初始值,文中提出了满足非负性和对称性的修正版Wishart距离,用于构造权重图(参见参考文献:S.N.Anfinsen,R.Jenssen,andT.Eltoft,“Spectral clustering of polarimetric SAR data with Wishart-deriveddistance measures,”in Proc.POLInSAR 2007,Esrin,Italy,22-26January 2007,Esrin,Italy,2007.)。In 1999, Lee et al. combined H/A/α polarimetric target decomposition and Wishart classifier for polarimetric SAR image classification (see References: Jong-Sen Lee, M.R.Grunes, T.L.Ainsworth, Li-Jen Du, D.L. Schuler and S.R. Cloude, "Unsupervised classification using polarimetric decomposition and the complex Wishart classifier," in IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 5, pp. 2249-2258, Sept. 1999.). Anfinsen et al. proposed a spectral clustering method based on Wishart distance. As the initial value of Wishart classifier, the paper proposed a modified version of Wishart distance that satisfies non-negativity and symmetry to construct a weight map (see Reference: S.N.Anfinsen , R. Jenssen, and T. Eltoft, "Spectral clustering of polarimetric SAR data with Wishart-derived distance measures," in Proc. POLInSAR 2007, Esrin, Italy, 22-26 January 2007, Esrin, Italy, 2007.).

另外,现有的极化SAR图像分类都包括特征提取和分类器设计这两个要素。因此是否能够提取合适的特征很大程度上影响了极化SAR图像的分类效果。其中,极化特征是最常用于极化SAR图像分类的特征,可从极化SAR数据、各种极化目标分解方法等中提取。Tu等人总结了42维极化特征,利用拉普拉斯特征映射(LE)提取低维特征,用于极化SAR图像分类(参见参考文献:S.T.Tu,J.Y.Chen,W.Yang,and H.Sun,“Laplacian eigenmaps-basedpolarimetric dimensionality reduction for SAR image classification,”IEEETransactions on Geoscience and Remote Sensing,vol.50,no.1,pp.170–179,Jan.2012.)。Yang等人总结了对各种极化目标分解产生的极化特征进行了总结,并提出了基于CNN的特征选择方法,选择极化特征,用于极化SAR图像分类(参见参考文献:C.Yang,B.Hou,B.Ren,Y.Hu and L.Jiao,"CNN-Based Polarimetric Decomposition FeatureSelection for PolSAR Image Classification,"in IEEE Transactions on Geoscienceand Remote Sensing,vol.57,no.11,pp.8796-8812,Nov.2019.)。In addition, the existing polarimetric SAR image classification includes two elements: feature extraction and classifier design. Therefore, whether it is possible to extract suitable features greatly affects the classification effect of polarimetric SAR images. Among them, the polarimetric feature is the most commonly used feature for polarimetric SAR image classification, which can be extracted from polarimetric SAR data, various polarimetric target decomposition methods, etc. Tu et al. summarized 42-dimensional polarimetric features and used Laplacian feature maps (LE) to extract low-dimensional features for polarimetric SAR image classification (see References: S.T.Tu, J.Y.Chen, W.Yang, and H. . Sun, "Laplacian eigenmaps-based polarimetric dimensionality reduction for SAR image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 1, pp. 170–179, Jan. 2012.). Yang et al. summarized the polarimetric features generated by the decomposition of various polarimetric targets, and proposed a CNN-based feature selection method to select polarimetric features for polarimetric SAR image classification (see Reference: C. Yang,B.Hou,B.Ren,Y.Hu and L.Jiao,"CNN-Based Polarimetric Decomposition FeatureSelection for PolSAR Image Classification,"in IEEE Transactions on Geoscienceand Remote Sensing,vol.57,no.11,pp.8796 -8812, Nov. 2019.).

但上述方法都存在以下问题:(1)Wishart距离和极化特征对极化SAR图像分类都起到了至关重要的结果,但是均未考虑到合理、充分的结合Wishart距离和极化特征;(2)基于单个像素点,未考虑空间信息,对噪声比较敏感,分类效果不理想。However, the above methods all have the following problems: (1) Wishart distance and polarization characteristics both play a crucial role in the classification of polarimetric SAR images, but they do not consider a reasonable and sufficient combination of Wishart distance and polarization characteristics; ( 2) Based on a single pixel, the spatial information is not considered, it is sensitive to noise, and the classification effect is not ideal.

发明内容SUMMARY OF THE INVENTION

为了解决现有技术中的上述问题,即为了解决现有极化SAR图像分类方法未考虑像素点的空间信息以及未充分结合Wishart距离、极化特征,导致分类精度较低的问题,本发明第一方面,提出了一种基于协同正则化和超像素的极化SAR图像分类的方法,该方法包括:In order to solve the above problems in the prior art, that is, in order to solve the problem that the existing polarimetric SAR image classification method does not consider the spatial information of the pixel points and does not fully combine the Wishart distance and polarization features, resulting in low classification accuracy, the present invention first On the one hand, a method for polarimetric SAR image classification based on co-regularization and superpixels is proposed, which includes:

步骤S100,基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;Step S100, based on the acquired polarimetric SAR image, obtain a plurality of superpixels and their corresponding coherence matrices by a superpixel generation method;

步骤S200,获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征;Step S200, obtaining the covariance matrix of each superpixel; decomposing the coherence matrix and the covariance matrix respectively by presetting multiple types of polarization target decomposition methods, and combining the coherence matrix and the covariance matrix matrix to extract the polarization characteristics of each superpixel preset dimension;

步骤S300,以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图;Step S300, in the d × d area centered on each superpixel, calculate its Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics, and sort in ascending order; The method obtains the weight values corresponding to the sorted Wishart distance and Euclidean distance respectively, and constructs the first weight map and the second weight map corresponding to each superpixel;

步骤S400,对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵;Step S400, for each superpixel, construct a corresponding first matrix and a second matrix based on its first weight map and second weight map; The co-regularized dimensionality reduction model obtains the first low-dimensional feature and the second low-dimensional feature; the first matrix and the second matrix are Laplacian matrices of the normalized graph;

步骤S500,对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。Step S500: Average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the classification result of the polarimetric SAR image through the nearest neighbor classifier.

在一些优选的实施方式中,所述相干矩阵为每个超像素内各像素点对应相干矩阵的平均值。In some preferred embodiments, the coherence matrix is an average value of the coherence matrix corresponding to each pixel in each superpixel.

在一些优选的实施方式中,所述预设多个种类的极化目标分解方法包括Pauli分解、H/A/α分解、Freeman分解、Kroggar分解、Huynen分解。In some preferred embodiments, the preset multiple types of polarization target decomposition methods include Pauli decomposition, H/A/α decomposition, Freeman decomposition, Kroggar decomposition, and Huynen decomposition.

在一些优选的实施方式中,步骤S300中“通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值”,其方法为:In some preferred embodiments, in step S300, "respectively obtain the weight values corresponding to the sorted Wishart distance and the Euclidean distance through the preset weight assignment method", and the method is:

基于排序后的Wishart距离、欧式距离,选取前k个Wishart距离、欧式距离,分别通过下述公式得到对应的权重值;Based on the sorted Wishart distance and Euclidean distance, select the first k Wishart distances and Euclidean distances, and obtain the corresponding weight values through the following formulas respectively;

Figure BDA0002330448310000041
Figure BDA0002330448310000041

其中,wij为权重值,σ=max(d)-min(d),d为Wishart距离或欧式距离;Among them, w ij is the weight value, σ=max(d)-min(d), and d is the Wishart distance or the Euclidean distance;

其余的Wishart距离、欧式距离对应的权重值置为零。The weight values corresponding to the remaining Wishart distance and Euclidean distance are set to zero.

在一些优选的实施方式中,步骤S400中“基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵”,其方法为:In some preferred embodiments, in step S400, "constructing a corresponding first matrix and a second matrix based on its first weight map and second weight map", the method is as follows:

步骤S410,基于各超像素的第一权重图、第二权重图,构建对应的第一对角矩阵、第二对角矩阵;Step S410, based on the first weight map and the second weight map of each superpixel, construct a corresponding first diagonal matrix and a second diagonal matrix;

步骤S411,基于各超像素的第一对角矩阵、第二对角矩阵,分别结合所述第一权重图、所述第二权重图,构建各超像素对应的第一矩阵、第二矩阵。Step S411 , based on the first diagonal matrix and the second diagonal matrix of each superpixel, and combining the first weight map and the second weight map respectively, construct a first matrix and a second matrix corresponding to each superpixel.

在一些优选的实施方式中,所述基于协同正则化的降维模型,其表示为:In some preferred embodiments, the dimensionality reduction model based on co-regularization is expressed as:

Figure BDA0002330448310000042
Figure BDA0002330448310000042

其中,U(1)为第一低维特征,L(1)为第一矩阵,U(2)为第二低维特征,L(2)为第二矩阵,α为参数,N为超像素的个数,M为降维后的维数,tr(·)为矩阵的迹,T表示转置,R表示实数。Among them, U (1) is the first low-dimensional feature, L (1) is the first matrix, U (2) is the second low-dimensional feature, L (2) is the second matrix, α is the parameter, and N is the superpixel The number of , M is the dimension after dimensionality reduction, tr( ) is the trace of the matrix, T is the transpose, and R is a real number.

在一些优选的实施方式中,步骤S400中“分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征”,其方法为:In some preferred embodiments, in step S400 "respectively obtain the first low-dimensional feature and the second low-dimensional feature through a pre-built dimensionality reduction model based on collaborative regularization", the method is as follows:

步骤S420,分别对所述第一矩阵、所述第二矩阵中的特征值进行降序排序,排序后选取前M个特征值对应的特征向量作为第一低维特征、第二低维特征;Step S420, respectively sort the eigenvalues in the first matrix and the second matrix in descending order, and select the eigenvectors corresponding to the first M eigenvalues as the first low-dimensional feature and the second low-dimensional feature after sorting;

步骤A421,基于所述第一低维特征,对矩阵L(2)+αU(1)U(1)T进行特征值分解,得到多个特征值,进行降序排序,排序后选取前M个特征值对应的特征向量作为更新后的第二低维特征;Step A421, based on the first low-dimensional feature, perform eigenvalue decomposition on the matrix L (2) +αU (1) U (1)T to obtain multiple eigenvalues, sort them in descending order, and select the first M features after sorting The feature vector corresponding to the value is used as the updated second low-dimensional feature;

步骤S422,基于所述第二低维特征,对矩阵L(1)+αU(2)U(2)T进行特征值分解,得到多个特征值,进行降序排序,排序后选取前M个特征值对应的特征向量作为更新后的第一低维特征;Step S422, based on the second low-dimensional feature, perform eigenvalue decomposition on the matrix L (1) +αU (2) U (2)T to obtain multiple eigenvalues, sort them in descending order, and select the first M features after sorting The feature vector corresponding to the value is used as the updated first low-dimensional feature;

步骤S423,获取当前迭代次数及所述第一低维特征与所述更新后的第一低维特征的差的F-范数、所述第二低维特征与所述更新后的第二低维特征的差的F-范数的和,若该和大于预设的阈值或所述当前迭代次数大于预设的最大迭代次数,则输出第一低维特征、第二低维特征,否则循环执行步骤S421-步骤S422的方法。Step S423, obtaining the current number of iterations and the F-norm of the difference between the first low-dimensional feature and the updated first low-dimensional feature, the second low-dimensional feature and the updated second low-dimensional feature. The sum of the F-norms of the difference of the dimensional features, if the sum is greater than the preset threshold or the current number of iterations is greater than the preset maximum number of iterations, output the first low-dimensional feature and the second low-dimensional feature, otherwise loop The method of step S421-step S422 is performed.

本发明的第二方面,提出了一种基于协同正则化和超像素的极化SAR图像分类的系统,该系统包括超像素生成模块、提取极化特征模块、获取权重图模块、特征降维模块、输出分类结果模块;In the second aspect of the present invention, a system for classification of polarimetric SAR images based on collaborative regularization and superpixels is proposed. The system includes a superpixel generation module, a polarimetric feature extraction module, a weight map acquisition module, and a feature dimension reduction module. , output classification result module;

所述超像素生成模块,配置为基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;The superpixel generation module is configured to obtain a plurality of superpixels and their corresponding coherence matrices through a superpixel generation method based on the obtained polarimetric SAR image;

所述提取极化特征模块,配置为获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征;The polarization feature extraction module is configured to obtain the covariance matrix of each superpixel; decompose the coherence matrix and the covariance matrix respectively by presetting multiple types of polarization target decomposition methods, and combine the The coherence matrix and the covariance matrix are used to extract the polarization characteristics of each superpixel preset dimension;

所述获取权重图模块,配置为以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图;The described acquisition weight map module is configured to calculate the Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics in a d×d area centered on each superpixel, and sort them in ascending order; The weight values corresponding to the sorted Wishart distance and the Euclidean distance are obtained respectively through the preset weight assignment method, and the first weight map and the second weight map corresponding to each superpixel are constructed;

所述特征降维模块,配置为对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵;The feature dimension reduction module is configured to construct a corresponding first matrix and a second matrix based on the first weight map and the second weight map for each superpixel; based on the first matrix and the second matrix, Obtain the first low-dimensional feature and the second low-dimensional feature respectively through the pre-built dimensionality reduction model based on collaborative regularization; the first matrix and the second matrix are Laplacian matrices of the normalized graph;

所述输出分类结果模块,配置为对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。The output classification result module is configured to average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the polarimetric SAR image through the nearest neighbor classifier. Classification results.

本发明的第三方面,提出了一种存储装置,其中存储有多条程序,所述程序应用由处理器加载并执行以实现上述的基于协同正则化和超像素的极化SAR图像分类方法。In a third aspect of the present invention, a storage device is provided, in which a plurality of programs are stored, and the program applications are loaded and executed by a processor to realize the above-mentioned collaborative regularization and superpixel-based polarization SAR image classification method.

本发明的第四方面,提出了一种处理装置,包括处理器、存储装置;处理器,适用于执行各条程序;存储装置,适用于存储多条程序;所述程序适用于由处理器加载并执行以实现上述的基于协同正则化和超像素的极化SAR图像分类方法。In a fourth aspect of the present invention, a processing device is proposed, including a processor and a storage device; the processor is adapted to execute various programs; the storage device is adapted to store multiple programs; the programs are adapted to be loaded by the processor And execute to realize the above-mentioned collaborative regularization and superpixel based polarimetric SAR image classification method.

本发明的有益效果:Beneficial effects of the present invention:

本发明根据像素点的空间信息,通过结合Wishart距离和极化特征,提高了极化SAR图像的分类精度。本发明通过利用像素点的空间信息,对极化SAR图像进行了超像素分割,将超像素作为处理单元,大大的降低计算负担和噪声影响。According to the spatial information of pixel points, the invention improves the classification accuracy of polarimetric SAR images by combining Wishart distance and polarimetric features. By utilizing the spatial information of the pixel points, the present invention performs superpixel segmentation on the polarimetric SAR image, and uses the superpixel as a processing unit, thereby greatly reducing the computational burden and the influence of noise.

同时,本发明获取多个超像素的相干矩阵及协方差矩阵,并通过多个种类的极化目标分解方法对矩阵进行分解,充分提取极化特征。并分别利用相干矩阵的Wishart距离和极化特征的欧氏距离,通过局部搜索邻近样本,构造权重图,并利用协同正则的方式进行特征提取,提高了分类精度。At the same time, the present invention obtains the coherence matrix and covariance matrix of a plurality of superpixels, and decomposes the matrix through a plurality of polarization target decomposition methods to fully extract polarization features. By using the Wishart distance of the coherence matrix and the Euclidean distance of the polarization feature respectively, the weight map is constructed by locally searching for the adjacent samples, and the feature extraction is carried out by using the collaborative regularization method, which improves the classification accuracy.

附图说明Description of drawings

通过阅读参照以下附图所做的对非限制性实施例所做的详细描述,本申请的其他特征、目的和优点将会变得更明显。Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments taken with reference to the following drawings.

图1是本发明一种实施例的基于协同正则化和超像素的极化SAR图像分类方法的流程示意图;1 is a schematic flowchart of a method for classifying polarimetric SAR images based on collaborative regularization and superpixels according to an embodiment of the present invention;

图2是本发明一种实施例的基于协同正则化和超像素的极化SAR图像分类系统的框架示意图;2 is a schematic diagram of a framework of a polarimetric SAR image classification system based on collaborative regularization and superpixels according to an embodiment of the present invention;

图3是本发明一种实施例的极化SAR图像的伪彩示意图;3 is a pseudo-color schematic diagram of a polarimetric SAR image according to an embodiment of the present invention;

图4是本发明一种实施例的极化SAR图像真实地物标记的示意图;FIG. 4 is a schematic diagram of a polarimetric SAR image real feature marker according to an embodiment of the present invention;

图5是本发明一种实施例的基于像素点的Wishart分类方法的分类结果的示意图;5 is a schematic diagram of a classification result of a pixel-based Wishart classification method according to an embodiment of the present invention;

图6是本发明一种实施例的基于超像素的Wihart分类方法的分类结果的示意图;6 is a schematic diagram of a classification result of the Wihart classification method based on superpixels according to an embodiment of the present invention;

图7是本发明一种实施例的基于协同正则化和超像素的极化SAR图像分类方法的分类结果的示意图。7 is a schematic diagram of a classification result of a polarimetric SAR image classification method based on collaborative regularization and superpixels according to an embodiment of the present invention.

具体实施方式Detailed ways

为使本发明的目的、技术方案和优点更加清楚,下面将结合附图对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not All examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是,此处所描述的具体实施例仅用于解释相关发明,而非对该发明的限定。另外还需要说明的是,为了便于描述,附图中仅示出了与有关发明相关的部分。The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the related invention, but not to limit the invention. In addition, it should be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

需要说明的是,在不冲突的情况下,本申请中的实施例及实施例中的特征可以相互组合。It should be noted that the embodiments in the present application and the features of the embodiments may be combined with each other in the case of no conflict.

本发明的基于协同正则化和超像素的极化SAR图像分类方法,如图1所示,包括以下步骤:The polarimetric SAR image classification method based on collaborative regularization and superpixels of the present invention, as shown in Figure 1, includes the following steps:

步骤S100,基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;Step S100, based on the acquired polarimetric SAR image, obtain a plurality of superpixels and their corresponding coherence matrices by a superpixel generation method;

步骤S200,获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征;Step S200, obtaining the covariance matrix of each superpixel; decomposing the coherence matrix and the covariance matrix respectively by presetting multiple types of polarization target decomposition methods, and combining the coherence matrix and the covariance matrix matrix to extract the polarization characteristics of each superpixel preset dimension;

步骤S300,以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图;Step S300, in the d × d area centered on each superpixel, calculate its Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics, and sort in ascending order; The method obtains the weight values corresponding to the sorted Wishart distance and Euclidean distance respectively, and constructs the first weight map and the second weight map corresponding to each superpixel;

步骤S400,对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵;Step S400, for each superpixel, construct a corresponding first matrix and a second matrix based on its first weight map and second weight map; The co-regularized dimensionality reduction model obtains the first low-dimensional feature and the second low-dimensional feature; the first matrix and the second matrix are Laplacian matrices of the normalized graph;

步骤S500,对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。Step S500: Average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the classification result of the polarimetric SAR image through the nearest neighbor classifier.

为了更清晰地对本发明基于协同正则化和超像素的极化SAR图像分类方法进行说明,下面结合附图对本发明方法一种实施例中各步骤进行展开详述。In order to more clearly describe the polarization SAR image classification method based on synergistic regularization and superpixels of the present invention, each step in an embodiment of the method of the present invention will be described in detail below with reference to the accompanying drawings.

步骤S100,基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵。Step S100, based on the acquired polarimetric SAR image, obtain a plurality of superpixels and their corresponding coherence matrices through a superpixel generation method.

合成孔径雷达(SAR)是一种全天候、全天时的主动微波遥感成像雷达,在地质勘测、灾害控制、参数反演和军事领域等方面应用广泛。极化SAR可记录地面目标完整的极化散射信息,极大提高了图像解释分析的精度。而对极化图像的分类是面向对象的极化SAR图像解译处理的基础和前提。Synthetic Aperture Radar (SAR) is an all-weather, all-weather active microwave remote sensing imaging radar, which is widely used in geological survey, disaster control, parameter inversion and military fields. Polarimetric SAR can record the complete polarimetric scattering information of ground targets, which greatly improves the accuracy of image interpretation and analysis. The classification of polarimetric images is the basis and premise of object-oriented polarimetric SAR image interpretation.

在本实施例中,基于获取的极化SAR图像,通过自适应的超像素生成方法获取多个超像素。其中自适应的超像生成方法可参考文献:“D.Xiang,Y.Ban,W.Wang and Y.Su,"Adaptive Superpixel Generation for Polarimetric SAR Images With LocalIterative Clustering and SIRV Model,"in IEEE Transactions on Geoscience andRemote Sensing,vol.55,no.6,pp.3115-3131,June 2017.”。根据获取的多个超像素,计算各超像素中包含所有像素点的相干矩阵的平均数,作为各超像素的相干矩阵Ti,i为下标。In this embodiment, based on the acquired polarimetric SAR image, a plurality of superpixels are acquired through an adaptive superpixel generation method. The adaptive superpixel generation method can refer to the literature: "D.Xiang,Y.Ban,W.Wang and Y.Su,"Adaptive Superpixel Generation for Polarimetric SAR Images With LocalIterative Clustering and SIRV Model,"in IEEE Transactions on Geoscience andRemote Sensing, vol. 55, no. 6, pp. 3115-3131, June 2017.”. According to the obtained multiple superpixels, the average number of coherence matrices including all pixel points in each superpixel is calculated as the coherence matrix T i of each superpixel, where i is a subscript.

步骤S200,获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征。Step S200, obtaining the covariance matrix of each superpixel; decomposing the coherence matrix and the covariance matrix respectively by presetting multiple types of polarization target decomposition methods, and combining the coherence matrix and the covariance matrix matrix to extract the polarization features of each superpixel preset dimension.

在本实施例中,先获取各超像素的协方差矩阵,通过多个种类的极化目标分解方法,如Pauli分解,H/A/α分解、Freeman分解、Kroggar分解、Huynen分解,对协方差矩阵、相干矩阵进行分解,并结合相干矩阵、协方差矩阵,提取各超像素内每个像素点的预设维度的极化特征,并归一化到0到1之间。本发明中,预设维度优选设置为30维。In this embodiment, the covariance matrix of each superpixel is obtained first, and the covariance is analyzed by various polarization target decomposition methods, such as Pauli decomposition, H/A/α decomposition, Freeman decomposition, Kroggar decomposition, and Huynen decomposition. The matrix and coherence matrix are decomposed, and combined with the coherence matrix and covariance matrix, the polarization characteristics of the preset dimension of each pixel in each superpixel are extracted, and normalized to between 0 and 1. In the present invention, the preset dimension is preferably set to 30 dimensions.

计算各超像素中包含的所有像素点的极化特征的平均数,作为超像素的极化特征xi,假设超像素的个数为N,则极化SAR图像对应的极化特征为

Figure BDA0002330448310000101
Calculate the average number of polarization features of all pixels contained in each superpixel, as the polarization feature x i of the superpixel, assuming that the number of superpixels is N, the polarization feature corresponding to the polarization SAR image is
Figure BDA0002330448310000101

步骤S300,以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图。Step S300, in the d × d area centered on each superpixel, calculate its Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics, and sort in ascending order; The method obtains the weight values corresponding to the sorted Wishart distance and Euclidean distance respectively, and constructs the first weight map and the second weight map corresponding to each superpixel.

在本实施例中,以每一个超像素为中心的d×d区域内,分别搜索与其Wishart距离dW、欧式距离dP最近的k个超像素,如果超像素j是超像素i的邻近样本,通过公式(1)获取对应的权重值,构建稀疏的权重图W(1)、W(2)。公式(1)如下所示:In this embodiment, in the d×d area centered on each superpixel , search for the k nearest superpixels with Wishart distance dW and Euclidean distance dP respectively, if superpixel j is a neighboring sample of superpixel i , obtain the corresponding weight value through formula (1), and construct the sparse weight map W (1) , W (2) . Formula (1) is as follows:

Figure BDA0002330448310000102
Figure BDA0002330448310000102

其中,wij为权重值,σ=max(d)-min(d),d为Wishart距离或欧式距离。Wherein, w ij is the weight value, σ=max(d)-min(d), and d is the Wishart distance or the Euclidean distance.

构建权重图的具体处理过程如下:The specific process of constructing the weight map is as follows:

以每一个超像素为中心的d×d区域内,基于其相干矩阵,计算其与其他超像素的Wishart距离。本实施例中,采用满足非负性和对称性的Wishart距离,其计算如公式(2)所示:In the d × d area centered on each superpixel, based on its coherence matrix, its Wishart distance from other superpixels is calculated. In this embodiment, the Wishart distance satisfying non-negativity and symmetry is adopted, and its calculation is shown in formula (2):

Figure BDA0002330448310000103
Figure BDA0002330448310000103

其中,j为下标,tr(·)为矩阵的迹。Among them, j is the subscript, and tr( ) is the trace of the matrix.

根据获取的Wishart距离进行升序排序,选取前k个Wishart距离,通过公式(3)得到对应的权重值。公式(3)如下所示:Sort in ascending order according to the obtained Wishart distance, select the first k Wishart distances, and obtain the corresponding weight value through formula (3). Formula (3) is as follows:

Figure BDA0002330448310000111
Figure BDA0002330448310000111

其中,

Figure BDA0002330448310000112
为Wishart距离对应的权重值。in,
Figure BDA0002330448310000112
is the weight value corresponding to Wishart distance.

其他Wishart距离对应的权重值置为0,基于获取的权重值,构建各超像素对应的第一权重图。The weight values corresponding to other Wishart distances are set to 0, and the first weight map corresponding to each superpixel is constructed based on the obtained weight values.

同理,以每一个超像素为中心的d×d区域内,基于其极化特征,计算其与其他超像素的欧式距离,根据获取的欧式距离进行升序排序,选取前k个欧式距离,通过公式(4)得到对应的权重值。公式(4)如下所示:In the same way, in the d × d area centered on each superpixel, based on its polarization characteristics, calculate its Euclidean distance from other superpixels, sort in ascending order according to the obtained Euclidean distance, select the first k Euclidean distances, and pass Formula (4) obtains the corresponding weight value. Formula (4) is as follows:

Figure BDA0002330448310000113
Figure BDA0002330448310000113

其中,

Figure BDA0002330448310000114
为欧式距离对应的权重值。in,
Figure BDA0002330448310000114
is the weight value corresponding to the Euclidean distance.

其他欧式距离对应的权重值置为0,基于获取的权重值,构建各超像素对应的第二权重图。The weight values corresponding to other Euclidean distances are set to 0, and a second weight map corresponding to each superpixel is constructed based on the obtained weight values.

步骤S400,对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的低维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵。Step S400, for each superpixel, construct a corresponding first matrix and a second matrix based on its first weight map and second weight map; The co-regularized low-dimensional model obtains the first low-dimensional feature and the second low-dimensional feature; the first and second matrices are Laplacian matrices of the normalized graph.

在本实施例中,对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵。具体处理步骤如下:In this embodiment, for each superpixel, a corresponding first matrix and a second matrix are constructed based on its first weight map and second weight map. The specific processing steps are as follows:

步骤S410,基于各超像素的第一权重图、第二权重图,构建对应的第一对角矩阵D(1)、第二对角矩阵D(2)Step S410, based on the first weight map and the second weight map of each superpixel, construct a corresponding first diagonal matrix D (1) and a second diagonal matrix D (2) .

构建过程如公式(5)(6)所示:The construction process is shown in formula (5) (6):

Figure BDA0002330448310000115
Figure BDA0002330448310000115

Figure BDA0002330448310000116
Figure BDA0002330448310000116

步骤S411,基于各超像素的第一对角矩阵、第二对角矩阵,分别结合所述第一权重图、所述第二权重图,构建各超像素对应的第一矩阵L(1)、第二矩阵L(2)。其中,L(1)、L(2)是W(1)、W(2)对应的归一化图Laplacian矩阵。Step S411, based on the first diagonal matrix and the second diagonal matrix of each superpixel, and in combination with the first weight map and the second weight map, respectively, construct the first matrix L (1) corresponding to each superpixel, The second matrix L (2) . Among them, L (1) and L (2) are the normalized graph Laplacian matrices corresponding to W (1) and W (2) .

构建过程如公式(7)(8)所示:The construction process is shown in formula (7) (8):

L(1)=I-D(1)-1/2W(1)D(1)-1/2 (7)L (1) = ID (1)-1/2 W (1) D (1)-1/2 (7)

L(2)=I-D(2)-1/2W(2)D(2)-1/2 (8)L (2) = ID (2)-1/2 W (2) D (2)-1/2 (8)

其中,I为单位矩阵。where I is the identity matrix.

通过第一矩阵、第二矩阵,构建基于协同正则化的降维模型。其中,构建的基于协同正则化的降维模型,如公式(9)所示:Through the first matrix and the second matrix, a dimensionality reduction model based on cooperative regularization is constructed. Among them, the constructed dimensionality reduction model based on collaborative regularization is shown in formula (9):

Figure BDA0002330448310000121
Figure BDA0002330448310000121

其中,U(1)为第一低维特征,L(1)为第一矩阵,U(2)为第二低维特征,L(2)为第二矩阵,α为参数,M为降维后的维数,T表示转置,R表示实数,

Figure BDA0002330448310000122
为F-范数的平方。本公式中的第三项为正则项。Among them, U (1) is the first low-dimensional feature, L (1) is the first matrix, U (2) is the second low-dimensional feature, L (2) is the second matrix, α is a parameter, and M is dimensionality reduction After the dimension, T represents the transpose, R represents the real number,
Figure BDA0002330448310000122
is the square of the F-norm. The third term in this formula is the regular term.

经过简化,上述公式可以表示如公式(10):After simplification, the above formula can be expressed as formula (10):

Figure BDA0002330448310000123
Figure BDA0002330448310000123

通过构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征。具体处理步骤如下:The first low-dimensional feature and the second low-dimensional feature are obtained through the constructed dimensionality reduction model based on co-regularization. The specific processing steps are as follows:

步骤S420,分别对第一矩阵、第二矩阵中的特征值进行降序排序,排序后选取前M个特征值对应的特征向量作为第一低维特征、第二低维特征;Step S420, sorting the eigenvalues in the first matrix and the second matrix in descending order respectively, and selecting the eigenvectors corresponding to the first M eigenvalues as the first low-dimensional feature and the second low-dimensional feature after sorting;

步骤A421,基于第一低维特征,对矩阵L(2)+αU(1)U(1)T进行特征值分解,得到多个特征值,进行降序排序,排序后选取前M个特征值对应的特征向量作为更新后的第二低维特征;即固定第一低维特征,求解第二低维特征;Step A421, based on the first low-dimensional feature, perform eigenvalue decomposition on the matrix L (2) +αU (1) U (1)T to obtain a plurality of eigenvalues, sort them in descending order, and select the first M eigenvalues corresponding to them after sorting. The feature vector of is used as the updated second low-dimensional feature; that is, the first low-dimensional feature is fixed, and the second low-dimensional feature is solved;

步骤S422,基于第二低维特征,对矩阵L(1)+αU(2)U(2)T进行特征值分解,得到多个特征值,进行降序排序,排序后选取前M个特征值对应的特征向量作为更新后的第一低维特征;Step S422, based on the second low-dimensional feature, perform eigenvalue decomposition on the matrix L (1) + αU (2) U (2) T to obtain a plurality of eigenvalues, perform descending sorting, and select the first M eigenvalues corresponding to the sorting. The feature vector of is used as the updated first low-dimensional feature;

步骤S423,获取当前迭代次数及第一低维特征与更新后的第一低维特征的差的F-范数、第二低维特征与更新后的第二低维特征的差的F-范数的和,若该和小于预设的阈值或所述当前迭代次数小于预设的最大迭代次数,则循环执行步骤S421-步骤S422的方法。其中,对预设阈值进行判断如公式(11)所示:Step S423, obtaining the current iteration number and the F-norm of the difference between the first low-dimensional feature and the updated first low-dimensional feature, and the F-norm of the difference between the second low-dimensional feature and the updated second low-dimensional feature. If the sum is less than the preset threshold or the current iteration number is less than the preset maximum number of iterations, the method of step S421-step S422 is executed cyclically. Among them, the preset threshold is judged as shown in formula (11):

Figure BDA0002330448310000131
Figure BDA0002330448310000131

其中,

Figure BDA0002330448310000132
为未更新的第一、第二低维特征,
Figure BDA0002330448310000133
为更新后的第一、第二低维特征,ε为预设的阈值。in,
Figure BDA0002330448310000132
are the unupdated first and second low-dimensional features,
Figure BDA0002330448310000133
are the updated first and second low-dimensional features, and ε is a preset threshold.

否则输出最终的第一低维特征、第二低维特征。Otherwise, output the final first low-dimensional feature and the second low-dimensional feature.

步骤S500,对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。Step S500: Average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the classification result of the polarimetric SAR image through the nearest neighbor classifier.

在本实施例中,将各超像素的第一低维特征、第二低维特征进行平均,得到平均特征作为分类的特征。其中,平均的过程如公式(12)所示:In this embodiment, the first low-dimensional feature and the second low-dimensional feature of each superpixel are averaged to obtain the average feature as a feature for classification. Among them, the average process is shown in formula (12):

U=(U(1)+U(2))/2 (12)U=(U (1) +U (2) )/2 (12)

其中,U为平均特征。where U is the average feature.

根据各超像素的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。According to the average feature of each superpixel, the classification result of the polarimetric SAR image is obtained through the nearest neighbor classifier.

本发明中,通过仿真实验将本发明方法与基于像素点的Wishart分类方法、基于超像素的Wishart分类方法进行对比,从而证明本发明对极化SAR图像分类的有效性。每一类随机选择10%作为训练集,剩下的作为测试集,求得分类准确率。In the present invention, the method of the present invention is compared with the Wishart classification method based on pixel points and the Wishart classification method based on superpixels through simulation experiments, so as to prove the effectiveness of the present invention in classifying polarimetric SAR images. 10% of each class is randomly selected as the training set, and the rest is used as the test set to obtain the classification accuracy.

仿真实验是在Intel(R)Core(TM)i9-8950HK CPU 2.90GHz 32G RAM的服务器硬件环境和Matlab R2016a的软件环境下进行的。利用的实验数据为Flevoland(平原)地区农田数据,大小为200×320。The simulation experiments are carried out in the server hardware environment of Intel(R) Core(TM) i9-8950HK CPU 2.90GHz 32G RAM and the software environment of Matlab R2016a. The experimental data used are farmland data in the Flevoland (plain) area, with a size of 200 × 320.

表1为三种方法在仿真中得到的分类准确率:Table 1 shows the classification accuracy obtained by the three methods in the simulation:

表1Table 1

Figure BDA0002330448310000141
Figure BDA0002330448310000141

从表1中可见,利用本发明得到的分类准确率明显高于经典的Wishart分类方法,且基于超像素的Wishart分类方法的分类准确率高于基于单个像素点的Wishart分类方法的准确率,说明了基于超像素方法的优势,进而充分说明了本发明所述方法对极化SAR图像分类具有良好的分类效果。其中,表1中,左侧的英文为各农田种植的农作物的名称,Potatoes为土豆,Grass为草地,Beet为甜菜,Lucerne为苜蓿,Wheat为麦田,Stem beans为干豆,Bare soil为裸地,Rapeseed为油菜籽。Total accuracy为总的正确率。As can be seen from Table 1, the classification accuracy obtained by using the present invention is significantly higher than the classical Wishart classification method, and the classification accuracy of the Wishart classification method based on superpixels is higher than the accuracy rate of the Wishart classification method based on a single pixel point. The advantages of the superpixel-based method are shown, and it is fully demonstrated that the method of the present invention has a good classification effect on polarimetric SAR image classification. Among them, in Table 1, the English on the left is the name of the crops grown in each farmland, Potatoes is potatoes, Grass is grass, Beet is sugar beet, Lucerne is alfalfa, Wheat is wheat field, Stem beans is dry beans, Bare soil is bare land , Rapeseed for Rapeseed. Total accuracy is the total correct rate.

为了更好的说明分类效果,本发明通过图例进行展示。图3为用于仿真实验的极化SAR图像的伪彩色图,图4为极化化SAR图像的真实地物标记图,图5为基于像素点的Wishart分类方法的分类结果图,图6为基于超像素的Wishart分类方法的分类结果图,图7为本发明方法的分类结果图。通过对比,同样可以看出本发明方法具有良好的分类效果。In order to better illustrate the classification effect, the present invention is shown by a legend. Fig. 3 is a pseudo-color image of the polarimetric SAR image used for the simulation experiment, Fig. 4 is a real ground object labeling graph of the polarimetric SAR image, Fig. 5 is a classification result diagram of the Wishart classification method based on pixel points, and Fig. 6 is a A classification result diagram of the Wishart classification method based on superpixels, and FIG. 7 is a classification result diagram of the method of the present invention. By comparison, it can also be seen that the method of the present invention has a good classification effect.

本发明第二实施例的一种基于协同正则化和超像素的极化SAR图像分类系统,如图2所示,包括:超像素生成模块100、提取极化特征模块200、获取权重图模块300、特征降维模块400、输出分类结果模块500;A polarimetric SAR image classification system based on collaborative regularization and superpixels according to the second embodiment of the present invention, as shown in FIG. 2 , includes: a superpixel generation module 100 , a polarimetric feature extraction module 200 , and a weight map acquisition module 300 , feature dimension reduction module 400, output classification result module 500;

所述超像素生成模块100,配置为基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;The superpixel generation module 100 is configured to obtain a plurality of superpixels and their corresponding coherence matrices through a superpixel generation method based on the obtained polarimetric SAR image;

所述提取极化特征模块200,配置为获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征;The polarization feature extraction module 200 is configured to obtain the covariance matrix of each superpixel; the coherence matrix and the covariance matrix are decomposed respectively by preset multiple types of polarization target decomposition methods, and combined with all the polarization target decomposition methods. The coherence matrix and the covariance matrix are used to extract the polarization characteristics of each superpixel preset dimension;

所述获取权重图模块300,配置为以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图;The acquisition weight map module 300 is configured to calculate the Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics in a d×d area centered on each superpixel, and perform ascending sorting. ; Obtain the weight values corresponding to the sorted Wishart distance and the Euclidean distance through the preset weight assignment method, and construct the first weight map and the second weight map corresponding to each superpixel;

所述特征降维模块400,配置为对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵;The feature dimension reduction module 400 is configured to construct a corresponding first matrix and a second matrix based on its first weight map and second weight map for each superpixel; based on the first matrix and the second matrix , respectively obtain the first low-dimensional feature and the second low-dimensional feature through a pre-built dimensionality reduction model based on collaborative regularization; the first matrix and the second matrix are Laplacian matrices of the normalized graph;

所述输出分类结果模块500,配置为对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。The output classification result module 500 is configured to average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the polarimetric SAR image through a nearest neighbor classifier classification results.

所述技术领域的技术人员可以清楚的了解到,为描述的方便和简洁,上述描述的系统的具体的工作过程及有关说明,可以参考前述方法实施例中的对应过程,在此不再赘述。Those skilled in the technical field can clearly understand that, for the convenience and brevity of description, for the specific working process and related description of the system described above, reference may be made to the corresponding process in the foregoing method embodiments, which will not be repeated here.

需要说明的是,上述实施例提供的基于协同正则化和超像素的极化SAR图像分类系统,仅以上述各功能模块的划分进行举例说明,在实际应用中,可以根据需要而将上述功能分配由不同的功能模块来完成,即将本发明实施例中的模块或者步骤再分解或者组合,例如,上述实施例的模块可以合并为一个模块,也可以进一步拆分成多个子模块,以完成以上描述的全部或者部分功能。对于本发明实施例中涉及的模块、步骤的名称,仅仅是为了区分各个模块或者步骤,不视为对本发明的不当限定。It should be noted that the polarimetric SAR image classification system based on collaborative regularization and superpixels provided by the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be allocated as required. It is completed by different functional modules, that is, the modules or steps in the embodiments of the present invention are decomposed or combined. For example, the modules in the above embodiments can be combined into one module, and can also be further split into multiple sub-modules to complete the above description. all or part of the functions. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing each module or step, and should not be regarded as an improper limitation of the present invention.

本发明第三实施例的一种存储装置,其中存储有多条程序,所述程序适用于由处理器加载并实现上述的基于协同正则化和超像素的极化SAR图像分类方法。A storage device according to the third embodiment of the present invention stores a plurality of programs, and the programs are suitable for being loaded by a processor and implementing the above-mentioned method for classifying polarimetric SAR images based on cooperative regularization and superpixels.

本发明第四实施例的一种处理装置,包括处理器、存储装置;处理器,适于执行各条程序;存储装置,适于存储多条程序;所述程序适于由处理器加载并执行以实现上述的基于协同正则化和超像素的极化SAR图像分类方法。A processing device according to a fourth embodiment of the present invention includes a processor and a storage device; the processor is adapted to execute various programs; the storage device is adapted to store multiple programs; the programs are adapted to be loaded and executed by the processor In order to realize the above-mentioned co-regularization and superpixel based polarimetric SAR image classification method.

所述技术领域的技术人员可以清楚的了解到,未描述的方便和简洁,上述描述的存储装置、处理装置的具体工作过程及有关说明,可以参考前述方法实例中的对应过程,在此不再赘述。Those skilled in the technical field can clearly understand that the undescribed convenience and brevity are not described. Repeat.

本领域技术人员应该能够意识到,结合本文中所公开的实施例描述的各示例的模块、方法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,软件模块、方法步骤对应的程序可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。为了清楚地说明电子硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以电子硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。本领域技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本发明的范围。Those skilled in the art should be able to realize that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two, and the programs corresponding to the software modules and method steps Can be placed in random access memory (RAM), internal memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or as known in the art in any other form of storage medium. In order to clearly illustrate the interchangeability of electronic hardware and software, the components and steps of each example have been described generally in terms of functionality in the foregoing description. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods of implementing the described functionality for each particular application, but such implementations should not be considered beyond the scope of the present invention.

术语“第一”、“第二”等是用于区别类似的对象,而不是用于描述或表示特定的顺序或先后次序。The terms "first," "second," etc. are used to distinguish between similar objects, and are not used to describe or indicate a particular order or sequence.

术语“包括”或者任何其它类似用语旨在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备/装置不仅包括那些要素,而且还包括没有明确列出的其它要素,或者还包括这些过程、方法、物品或者设备/装置所固有的要素。The term "comprising" or any other similar term is intended to encompass a non-exclusive inclusion such that a process, method, article or device/means comprising a list of elements includes not only those elements but also other elements not expressly listed, or Also included are elements inherent to these processes, methods, articles or devices/devices.

至此,已经结合附图所示的优选实施方式描述了本发明的技术方案,但是,本领域技术人员容易理解的是,本发明的保护范围显然不局限于这些具体实施方式。在不偏离本发明的原理的前提下,本领域技术人员可以对相关技术特征作出等同的更改或替换,这些更改或替换之后的技术方案都将落入本发明的保护范围之内。So far, the technical solutions of the present invention have been described with reference to the preferred embodiments shown in the accompanying drawings, however, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims (10)

1.一种基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,该方法包括:1. a polarization SAR image classification method based on collaborative regularization and superpixel, is characterized in that, this method comprises: 步骤S100,基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;Step S100, based on the acquired polarimetric SAR image, obtain a plurality of superpixels and their corresponding coherence matrices by a superpixel generation method; 步骤S200,获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征;Step S200, obtaining the covariance matrix of each superpixel; decomposing the coherence matrix and the covariance matrix respectively by presetting multiple types of polarization target decomposition methods, and combining the coherence matrix and the covariance matrix matrix to extract the polarization characteristics of each superpixel preset dimension; 步骤S300,以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图;Step S300, in the d × d area centered on each superpixel, calculate its Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics, and sort in ascending order; The method obtains the weight values corresponding to the sorted Wishart distance and Euclidean distance respectively, and constructs the first weight map and the second weight map corresponding to each superpixel; 步骤S400,对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵;Step S400, for each superpixel, construct a corresponding first matrix and a second matrix based on its first weight map and second weight map; The co-regularized dimensionality reduction model obtains the first low-dimensional feature and the second low-dimensional feature; the first matrix and the second matrix are Laplacian matrices of the normalized graph; 步骤S500,对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。Step S500: Average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the classification result of the polarimetric SAR image through the nearest neighbor classifier. 2.根据权利要求1所述的基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,所述相干矩阵为每个超像素内各像素点对应相干矩阵的平均值。2 . The method for classifying polarimetric SAR images based on collaborative regularization and superpixels according to claim 1 , wherein the coherence matrix is an average value of the coherence matrices corresponding to each pixel in each superpixel. 3 . 3.根据权利要求1所述的基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,所述预设多个种类的极化目标分解方法包括Pauli分解、H/A/α分解、Freeman分解、Kroggar分解、Huynen分解。3. the polarization SAR image classification method based on collaborative regularization and superpixel according to claim 1, is characterized in that, the polarization target decomposition method of described preset multiple types comprises Pauli decomposition, H/A/α Decomposition, Freeman Decomposition, Kroggar Decomposition, Huynen Decomposition. 4.根据权利要求1所述的基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,步骤S300中“通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值”,其方法为:4. the polarimetric SAR image classification method based on collaborative regularization and superpixels according to claim 1, is characterized in that, in step S300, "respectively obtain the Wishart distance after sorting, the Euclidean distance corresponding to by the preset weight assignment method. The weight value of ", the method is: 基于排序后的Wishart距离、欧式距离,选取前k个Wishart距离、欧式距离,分别通过下述公式得到对应的权重值:Based on the sorted Wishart distance and Euclidean distance, select the first k Wishart distances and Euclidean distances, and obtain the corresponding weight values through the following formulas:
Figure FDA0002330448300000021
Figure FDA0002330448300000021
其中,wij为权重值,σ=max(d)-min(d),d为Wishart距离或欧式距离;Among them, w ij is the weight value, σ=max(d)-min(d), and d is the Wishart distance or the Euclidean distance; 其余的Wishart距离、欧式距离对应的权重值置为零。The weight values corresponding to the remaining Wishart distance and Euclidean distance are set to zero.
5.根据权利要求1所述的基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,步骤S400中“基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵”,其方法为:5. the polarimetric SAR image classification method based on collaborative regularization and superpixel according to claim 1, is characterized in that, in step S400 " build the corresponding first matrix based on its first weight map, the second weight map, The second matrix", whose method is: 步骤S410,基于各超像素的第一权重图、第二权重图,构建对应的第一对角矩阵、第二对角矩阵;Step S410, based on the first weight map and the second weight map of each superpixel, construct a corresponding first diagonal matrix and a second diagonal matrix; 步骤S411,基于各超像素的第一对角矩阵、第二对角矩阵,分别结合所述第一权重图、所述第二权重图,构建各超像素对应的第一矩阵、第二矩阵。Step S411 , based on the first diagonal matrix and the second diagonal matrix of each superpixel, and combining the first weight map and the second weight map respectively, construct a first matrix and a second matrix corresponding to each superpixel. 6.根据权利要求1所述的基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,所述基于协同正则化的降维模型,其表示为:6. The polarimetric SAR image classification method based on collaborative regularization and superpixels according to claim 1, wherein the described dimensionality reduction model based on collaborative regularization is expressed as:
Figure FDA0002330448300000022
Figure FDA0002330448300000022
其中,U(1)为第一低维特征,L(1)为第一矩阵,U(2)为第二低维特征,L(2)为第二矩阵,α为参数,N为超像素的个数,M为降维后的维数,tr(·)为矩阵的迹,T表示转置,R表示实数。Among them, U (1) is the first low-dimensional feature, L (1) is the first matrix, U (2) is the second low-dimensional feature, L (2) is the second matrix, α is the parameter, and N is the superpixel The number of , M is the dimension after dimensionality reduction, tr( ) is the trace of the matrix, T is the transpose, and R is a real number.
7.根据权利要求6所述的基于协同正则化和超像素的极化SAR图像分类方法,其特征在于,步骤S400中“分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征”,其方法为:7. the polarimetric SAR image classification method based on collaborative regularization and superpixels according to claim 6, is characterized in that, in step S400, "respectively obtain the first low-dimensional dimensionality reduction model based on pre-built collaborative regularization. feature, the second low-dimensional feature", the method is: 步骤S420,分别对所述第一矩阵、所述第二矩阵中的特征值进行降序排序,排序后选取前M个特征值对应的特征向量作为第一降低维特征、第二低维特征;Step S420, respectively sort the eigenvalues in the first matrix and the second matrix in descending order, and select the eigenvectors corresponding to the first M eigenvalues as the first reduced-dimensional feature and the second low-dimensional feature after sorting; 步骤A421,基于所述第一低维特征,对矩阵L(2)+αU(1)U(1)T进行特征值分解,得到多个特征值,进行降序排序,排序后选取前M个特征值对应的特征向量作为更新后的第二低维特征;Step A421, based on the first low-dimensional feature, perform eigenvalue decomposition on the matrix L (2) +αU (1) U (1)T to obtain multiple eigenvalues, sort them in descending order, and select the first M features after sorting The feature vector corresponding to the value is used as the updated second low-dimensional feature; 步骤S422,基于所述第二低维特征,对矩阵L(1)+αU(2)U(2)T进行特征值分解,得到多个特征值,进行降序排序,排序后选取前M个特征值对应的特征向量作为更新后的第一低维特征;Step S422, based on the second low-dimensional feature, perform eigenvalue decomposition on the matrix L (1) +αU (2) U (2)T to obtain multiple eigenvalues, sort them in descending order, and select the first M features after sorting The feature vector corresponding to the value is used as the updated first low-dimensional feature; 步骤S423,获取当前迭代次数及所述第一低维特征与所述更新后的第一低维特征的差的F-范数、所述第二低维特征与所述更新后的第二低维特征的差的F-范数的和,若该和大于预设的阈值或所述当前迭代次数大于预设的最大迭代次数,则输出第一低维特征、第二低维特征,否则循环执行步骤S421-步骤S422的方法。Step S423, obtaining the current number of iterations and the F-norm of the difference between the first low-dimensional feature and the updated first low-dimensional feature, the second low-dimensional feature and the updated second low-dimensional feature. The sum of the F-norms of the difference of the dimensional features, if the sum is greater than the preset threshold or the current number of iterations is greater than the preset maximum number of iterations, output the first low-dimensional feature and the second low-dimensional feature, otherwise loop The method of step S421-step S422 is performed. 8.一种基于协同正则化和超像素的极化SAR图像分类系统,其特征在于,该系统包括超像素生成模块、提取极化特征模块、获取权重图模块、特征降维模块、输出分类结果模块;8. A polarimetric SAR image classification system based on collaborative regularization and superpixels, characterized in that the system comprises a superpixel generation module, a polarimetric feature extraction module, an acquisition weight map module, a feature dimension reduction module, and an output classification result. module; 所述超像素生成模块,配置为基于获取的极化SAR图像,通过超像素生成方法得到多个超像素及其对应的相干矩阵;The superpixel generation module is configured to obtain a plurality of superpixels and their corresponding coherence matrices through a superpixel generation method based on the obtained polarimetric SAR image; 所述提取极化特征模块,配置为获取各超像素的协方差矩阵;通过预设多个种类的极化目标分解方法分别对所述相干矩阵、所述协方差矩阵进行分解,并结合所述相干矩阵、所述协方差矩阵,提取各超像素预设维度的极化特征;The polarization feature extraction module is configured to obtain the covariance matrix of each superpixel; decompose the coherence matrix and the covariance matrix respectively by presetting multiple types of polarization target decomposition methods, and combine the The coherence matrix and the covariance matrix are used to extract the polarization characteristics of each superpixel preset dimension; 所述获取权重图模块,配置为以每一个超像素为中心的d×d区域内,基于其相干矩阵、极化特征分别计算其与其他超像素的Wishart距离、欧式距离,并进行升序排序;通过预设的权重赋值方法分别获取排序后的Wishart距离、欧式距离对应的权重值,构建各超像素对应的第一权重图、第二权重图;The described acquisition weight map module is configured to calculate the Wishart distance and Euclidean distance with other superpixels based on its coherence matrix and polarization characteristics in a d×d area centered on each superpixel, and sort them in ascending order; The weight values corresponding to the sorted Wishart distance and the Euclidean distance are obtained respectively through the preset weight assignment method, and the first weight map and the second weight map corresponding to each superpixel are constructed; 所述特征降维模块,配置为对每一个超像素,基于其第一权重图、第二权重图构建对应的第一矩阵、第二矩阵;基于所述第一矩阵、所述第二矩阵,分别通过预构建的基于协同正则化的降维模型得到第一低维特征、第二低维特征;所述第一矩阵、第二矩阵为归一化图的Laplacian矩阵;The feature dimension reduction module is configured to construct a corresponding first matrix and a second matrix based on the first weight map and the second weight map for each superpixel; based on the first matrix and the second matrix, Obtain the first low-dimensional feature and the second low-dimensional feature respectively through the pre-built dimensionality reduction model based on collaborative regularization; the first matrix and the second matrix are Laplacian matrices of the normalized graph; 所述输出分类结果模块,配置为对所述第一低维特征和所述第二低维特征进行平均,获取各超像素对应的平均特征,通过最近邻分类器得到所述极化SAR图像的分类结果。The output classification result module is configured to average the first low-dimensional feature and the second low-dimensional feature, obtain the average feature corresponding to each superpixel, and obtain the polarimetric SAR image through the nearest neighbor classifier. Classification results. 9.一种存储装置,其中存储有多条程序,其特征在于,所述程序应用由处理器加载并执行以实现权利要求1-7任一项所述的基于协同正则化和超像素的极化SAR图像分类方法。9. A storage device, wherein storing a plurality of programs, is characterized in that, described program application is loaded and executed by processor to realize the polar based on cooperative regularization and superpixel described in any one of claim 1-7. SAR image classification method. 10.一种处理装置,包括处理器、存储装置;处理器,适用于执行各条程序;存储装置,适用于存储多条程序;其特征在于,所述程序适用于由处理器加载并执行以实现权利要求1-7任一项所述的基于协同正则化和超像素的极化SAR图像分类方法。10. A processing device, comprising a processor and a storage device; the processor is adapted to execute each program; the storage device is adapted to store a plurality of programs; characterized in that the program is adapted to be loaded and executed by the processor to The method for classifying polarimetric SAR images based on cooperative regularization and superpixels according to any one of claims 1 to 7 is implemented.
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