基于核理论的遥感图像分类方法研究
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摘要
针对遥感图像分类中存在的非线性、计算复杂性、模糊性和少的标记数据等问题,以核理论为基础,结合半监督技术和近邻特性等,对广义判别分析(GDA)、模糊C-均值(FCM)和光谱角匹配(SAM)算法进行核扩展,构建了基于核理论的遥感图像分类框架,并应用于遥感图像的训练数据减少、非线性特征提取和分类,提高了遥感图像的分类精度和效率,降低了计算复杂度。论文的主要研究工作和成果:
1.针对大数据集进行训练数据减少问题,提出用非线性支持向量机(NSVM)的支持向量来减少遥感图像分类中的训练数据。NSVM方法在保证分类器泛化能力的情况下,减少了训练分类器的数据,降低了计算复杂度。
2.针对大数据集进行非线性特征提取的计算复杂度高问题,提出Greedy GDA(GGDA)的训练数据减少和非线性特征提取方法,并应用于遥感图像数据。实验结果表明:GGDA和GDA方法的特征提取性能优于其它对比方法;GGDA方法不仅较好地保持了GDA方法的特征提取性能,而且减少了大数据集进行非线性特征提取的计算复杂度。
3.针对传统分类器缺乏考虑遥感图像分类中的非线性、模糊性和少的标记数据等问题,提出一种半监督核FCM(SSKFCM)算法的遥感图像分类方法。该算法使原来在低维空间非线性不可分模式在高维空间变成线性可分,同时,该算法通过半监督学习技术使用标记和未标记数据一起提高了遥感图像的分类精度。
4.针对FCM和核FCM(KFCM)算法的最小化误差平方和目标函数具有对数据集进行等划分趋势的缺陷,提出近邻样本密度加权、近邻样本隶属度加权、近邻样本密度和隶属度加权的FCM和KFCM算法。即用近邻样本密度加权系数来影响最小化误差平方和的目标函数,使加权系数高的样本对误差的影响大;用近邻样本隶属度加权系数,使近邻样本有趋向近似相同的隶属度。实验结果表明:几种加权的FCM和KFCM算法都在一定程度克服了FCM和KFCM算法的分类性能,提高了遥感图像的无监督分类能力。
5.为了更好处理分类中数据的非线性问题,将光谱角匹配(SAM)算法进行核扩展,形成核SAM(KSAM)算法,并应用于遥感图像分类。实验结果表明:基于KSAM方法的分类精度高于SAM方法。在KSAM方法中,核函数Poly和Sigmoid对核参数过于敏感,最佳分类的核参数值可选范围窄;而核函数ERBF和RBF,不仅分类精度更高,而且最佳分类的核参数值可选范围宽。
6.为了进一步验证论文提出的SSKFCM、NSDM-WKFCM和KSAM等核模式分类算法的分类性能,进行了详细的综合分类实验对比。实验结果表明:SSKFCM、NSDM-WKFCM和KSAM算法在同类型对比算法中都显示出最强的分类能力。
These problems of nonlinearity, computational complexity, fuzziness and few labeled data exist in remote sensing image classification. In this thesis, several algorithms, such as generalized discriminant analysis (GDA), fuzzy C-means (FCM), and spectral angle match (SAM), are extended to their kernel patterns by introducing the kernel method, the semi-supervised learning technique, and the neighbor sample characteristic, et al. Kernel-based framework for remote sensing image classification is constructed. Those new kernel pattern related algorithms are applied in training data reduction, nonlinear feature extraction, and remote sensing image classification for improving the classification accuracy and efficiency, and reducing the computational complexity. The main work and results are as follows:
1. A method is proposed to reduce training data of remote sensing image classification in large datasets with support vectors from nonlinear support vector machine (NSVM). The NSVM method reduces training data and computational complexity of training classifier while retaining the generalization of the classifier.
2. Nonlinear feature extraction has high computational complexity in large datasets. A greedy GDA (GGDA) method is proposed to reduce training data and deal with the nonlinear feature extraction problem, and used in data of remote sensing image. The simulation results indicate that the feature extraction performance of both GGDA and GDA methods outperforms one of these compared methods. In addition of retaining the performance of the GDA method, the GGDA method reduces the computational complexity of the nonlinear feature extraction in large datasets.
3. These problems of nonlinearity, fuzziness and few labeled data are rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means (SSKFCM) algorithm is proposed to overcome these disadvantages of remote sensing image classification. On the one hand, with the kernel method, the input data is mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear in the SSKFCM algorithm. On the other hand, by the semi-supervised learning technique, the SSKFCM algorithm combines the labeled and unlabeled data together to improve the classification accuracy of remote sensing images.
4. Both FCM and kernel FCM (KFCM) algorithms have a disadvantage of equal partition trend for data sets with minimizing the sum of error squares objective function. Several weighted FCM (WFCM) and weighted KFCM (WKFCM) algorithms are proposed to overcome the disadvantage of FCM and KFCM, by involving the neighbor sample density (NSD), the neighbor sample membership (NSM), and both the neighbor sample density and membership (NSDM) into the FCM and KFCM algorithms, respectively. The weighted coefficient of the NSD exerts an influence on the sum of error squares objective function, the higher the value, the larger the influence; on the other hand, the neighbor samples have the tendency of the approximately same membership value by the weighted coefficient of the NSM. Experimental results indicate that these weighted algorithms improve the classification performance to some extent, and significantly improve the unsupervised classification capacity of remote sensing images compared with FCM and KFCM.
5. A kernel spectral angle match (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of remote sensing image. The KSAM algorithm extends the spectral angle match (SAM) algorithm by introducing the kernel method. Experimental results indicate that the classification accuracy of the KSAM algorithm is superior to one of the SAM algorithm. Experimental results indicate still that kernel parameters of poly and sigmoid kernel are excessively sensitive, and a narrow bound of kernel parameters can be chosen for the optimal classification; the classification performance of ERBF and RBF kernel is superior to one of Poly and Sigmoid kernel, and a wide bound of kernel parameters in ERBF and RBF kernel can be chosen for the optimal classification in the KSAM algorithm.
6. Comprehensive classification experiment is accomplished to validate further the classification performance of these proposed kernel pattern classification algorithms. The experiment results indicate that the classification performance of SSKFCM, NSDM-WKFCM and KSAM is superior to one of the same type compared algorithm.
1.针对大数据集进行训练数据减少问题,提出用非线性支持向量机(NSVM)的支持向量来减少遥感图像分类中的训练数据。NSVM方法在保证分类器泛化能力的情况下,减少了训练分类器的数据,降低了计算复杂度。
2.针对大数据集进行非线性特征提取的计算复杂度高问题,提出Greedy GDA(GGDA)的训练数据减少和非线性特征提取方法,并应用于遥感图像数据。实验结果表明:GGDA和GDA方法的特征提取性能优于其它对比方法;GGDA方法不仅较好地保持了GDA方法的特征提取性能,而且减少了大数据集进行非线性特征提取的计算复杂度。
3.针对传统分类器缺乏考虑遥感图像分类中的非线性、模糊性和少的标记数据等问题,提出一种半监督核FCM(SSKFCM)算法的遥感图像分类方法。该算法使原来在低维空间非线性不可分模式在高维空间变成线性可分,同时,该算法通过半监督学习技术使用标记和未标记数据一起提高了遥感图像的分类精度。
4.针对FCM和核FCM(KFCM)算法的最小化误差平方和目标函数具有对数据集进行等划分趋势的缺陷,提出近邻样本密度加权、近邻样本隶属度加权、近邻样本密度和隶属度加权的FCM和KFCM算法。即用近邻样本密度加权系数来影响最小化误差平方和的目标函数,使加权系数高的样本对误差的影响大;用近邻样本隶属度加权系数,使近邻样本有趋向近似相同的隶属度。实验结果表明:几种加权的FCM和KFCM算法都在一定程度克服了FCM和KFCM算法的分类性能,提高了遥感图像的无监督分类能力。
5.为了更好处理分类中数据的非线性问题,将光谱角匹配(SAM)算法进行核扩展,形成核SAM(KSAM)算法,并应用于遥感图像分类。实验结果表明:基于KSAM方法的分类精度高于SAM方法。在KSAM方法中,核函数Poly和Sigmoid对核参数过于敏感,最佳分类的核参数值可选范围窄;而核函数ERBF和RBF,不仅分类精度更高,而且最佳分类的核参数值可选范围宽。
6.为了进一步验证论文提出的SSKFCM、NSDM-WKFCM和KSAM等核模式分类算法的分类性能,进行了详细的综合分类实验对比。实验结果表明:SSKFCM、NSDM-WKFCM和KSAM算法在同类型对比算法中都显示出最强的分类能力。
These problems of nonlinearity, computational complexity, fuzziness and few labeled data exist in remote sensing image classification. In this thesis, several algorithms, such as generalized discriminant analysis (GDA), fuzzy C-means (FCM), and spectral angle match (SAM), are extended to their kernel patterns by introducing the kernel method, the semi-supervised learning technique, and the neighbor sample characteristic, et al. Kernel-based framework for remote sensing image classification is constructed. Those new kernel pattern related algorithms are applied in training data reduction, nonlinear feature extraction, and remote sensing image classification for improving the classification accuracy and efficiency, and reducing the computational complexity. The main work and results are as follows:
1. A method is proposed to reduce training data of remote sensing image classification in large datasets with support vectors from nonlinear support vector machine (NSVM). The NSVM method reduces training data and computational complexity of training classifier while retaining the generalization of the classifier.
2. Nonlinear feature extraction has high computational complexity in large datasets. A greedy GDA (GGDA) method is proposed to reduce training data and deal with the nonlinear feature extraction problem, and used in data of remote sensing image. The simulation results indicate that the feature extraction performance of both GGDA and GDA methods outperforms one of these compared methods. In addition of retaining the performance of the GDA method, the GGDA method reduces the computational complexity of the nonlinear feature extraction in large datasets.
3. These problems of nonlinearity, fuzziness and few labeled data are rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means (SSKFCM) algorithm is proposed to overcome these disadvantages of remote sensing image classification. On the one hand, with the kernel method, the input data is mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear in the SSKFCM algorithm. On the other hand, by the semi-supervised learning technique, the SSKFCM algorithm combines the labeled and unlabeled data together to improve the classification accuracy of remote sensing images.
4. Both FCM and kernel FCM (KFCM) algorithms have a disadvantage of equal partition trend for data sets with minimizing the sum of error squares objective function. Several weighted FCM (WFCM) and weighted KFCM (WKFCM) algorithms are proposed to overcome the disadvantage of FCM and KFCM, by involving the neighbor sample density (NSD), the neighbor sample membership (NSM), and both the neighbor sample density and membership (NSDM) into the FCM and KFCM algorithms, respectively. The weighted coefficient of the NSD exerts an influence on the sum of error squares objective function, the higher the value, the larger the influence; on the other hand, the neighbor samples have the tendency of the approximately same membership value by the weighted coefficient of the NSM. Experimental results indicate that these weighted algorithms improve the classification performance to some extent, and significantly improve the unsupervised classification capacity of remote sensing images compared with FCM and KFCM.
5. A kernel spectral angle match (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of remote sensing image. The KSAM algorithm extends the spectral angle match (SAM) algorithm by introducing the kernel method. Experimental results indicate that the classification accuracy of the KSAM algorithm is superior to one of the SAM algorithm. Experimental results indicate still that kernel parameters of poly and sigmoid kernel are excessively sensitive, and a narrow bound of kernel parameters can be chosen for the optimal classification; the classification performance of ERBF and RBF kernel is superior to one of Poly and Sigmoid kernel, and a wide bound of kernel parameters in ERBF and RBF kernel can be chosen for the optimal classification in the KSAM algorithm.
6. Comprehensive classification experiment is accomplished to validate further the classification performance of these proposed kernel pattern classification algorithms. The experiment results indicate that the classification performance of SSKFCM, NSDM-WKFCM and KSAM is superior to one of the same type compared algorithm.
引文
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