基于流形主动学习的遥感图像分类算法
|
摘要
为了高效地解决遥感图像分类问题,提出一种基于流形学习和支持向量机(SVM)的图像分类算法。在初始阶段,该算法首先利用初始训练集训练SVM,并且使用SVM找出离分类界面最近的样本;然后在所选样本中利用拉普拉斯图构建样本空间的流形结构,选出最具有代表性的样本加入训练集;最后利用高光谱图像进行实验进行验证。通过与现有的主动学习算法进行比较,结果表明该算法获得了更高的分类准确率。
To efficiently solve remote sensing image classification problem,a new classification algorithm based on manifold structure and Support Vector Machine(SVM) was proposed.Firstly,the proposed algorithm trained the SVM with initial training set and found the samples close to the decision hyperplane,then built the manifold structure of the samples by using Laplacian graph of the selected samples.The manifold structure was applied to find the representative samples for the classifier.The experimental evaluations were conducted on the hyperspectral images,and the effectiveness of the proposed algorithm was evaluated by comparing it with other active learning techniques exiting in the literature.The experimental results on data set confirm that the algorithm has higher classification accuracy.
To efficiently solve remote sensing image classification problem,a new classification algorithm based on manifold structure and Support Vector Machine(SVM) was proposed.Firstly,the proposed algorithm trained the SVM with initial training set and found the samples close to the decision hyperplane,then built the manifold structure of the samples by using Laplacian graph of the selected samples.The manifold structure was applied to find the representative samples for the classifier.The experimental evaluations were conducted on the hyperspectral images,and the effectiveness of the proposed algorithm was evaluated by comparing it with other active learning techniques exiting in the literature.The experimental results on data set confirm that the algorithm has higher classification accuracy.
引文
[1]HASTIE T,TIBSHIRANI R,FRIEDMAN J.The elements of statis-tical learning:data mining,inference,and prediction[M].2nded.New York:Springer,2009.
[2]BOSER B E,GUYON I M,VAPNIK V N.A training algorithm foroptimal margin classifiers[C]//COLT'92:Proceedings of the FifthAnnual Workshop on Computational Learning Theory.New York:ACM,1992:144-152.
[3]HAYKIN S O.Neural networks and learning machines[M].3rded.Cambridge:Prentice-Hall,2008.
[4]SETTLES B.Active learning literature survey,Computer ScienceTechnique Report 1648[R].Madison,WI:University of Wiscon-sin-Madison,2010.
[5]TONG S,KOLLER D.Support vector machine active learning withapplications to text classification[J].Journal of Machine LearningResearch,2002,2:45-66.
[6]OLSSON F.A literature survey of active machine learning in thecontext of natural language processing,SICS Technical ReportT2009:06[R].Kista,Sweden:Swedish Institution Computer Sci-ence,2009.
[7]蒋华,戚玉顺.基于球结构支持向量机的多标签分类的主动学习[J].计算机应用,2012,32(5):1359-1361.
[8]de SA V R.Learning classification with unlabeled data[C]//Pro-ceedings of Advances in Neural Information Processing Systems.SanFrancisco:Morgan Kaufmann,1994:112-119.
[9](美)CRISTIANINI N,SHAWE-TAYLOR J.支持向量机导论[M].李国正,王蒙,曾华军,译.北京:电子工业出版社,2004.
[10]张伟,柳先辉,丁毅,等.基于支持向量回归的多时间序列自回归方法[J].计算机应用,2012,32(9):2508-2511.
[11]HADSELL R,CHOPRA S,LECUN Y.Dimensionality reductionby learning an invariant mapping[C]//CVPR'06:Proceedings ofthe 2006 IEEE Computer Society Conference on Computer Visionand Pattern Recognition.Washington,DC:IEEE Computer Socie-ty,2006,2:1735-1742.
[12]石陆魁,张军,宫晓腾.基于邻域保持的流形学习算法评价模型[J].计算机应用,2012,32(9):2516-2519.
[13]张瑞丽,张继福.基于w-距离均值的模糊聚类算法[J].计算机应用,2012,32(7):1978-1982.
[14]邵超,张慧娟.应用于不完整流形的ISOMAP算法[J].计算机应用,2012,32(7):1987-1990.
[15]易淼,刘小兰.基于相对变换的半监督分类算法[J].计算机应用,2011,31(10):2793-2795.
[2]BOSER B E,GUYON I M,VAPNIK V N.A training algorithm foroptimal margin classifiers[C]//COLT'92:Proceedings of the FifthAnnual Workshop on Computational Learning Theory.New York:ACM,1992:144-152.
[3]HAYKIN S O.Neural networks and learning machines[M].3rded.Cambridge:Prentice-Hall,2008.
[4]SETTLES B.Active learning literature survey,Computer ScienceTechnique Report 1648[R].Madison,WI:University of Wiscon-sin-Madison,2010.
[5]TONG S,KOLLER D.Support vector machine active learning withapplications to text classification[J].Journal of Machine LearningResearch,2002,2:45-66.
[6]OLSSON F.A literature survey of active machine learning in thecontext of natural language processing,SICS Technical ReportT2009:06[R].Kista,Sweden:Swedish Institution Computer Sci-ence,2009.
[7]蒋华,戚玉顺.基于球结构支持向量机的多标签分类的主动学习[J].计算机应用,2012,32(5):1359-1361.
[8]de SA V R.Learning classification with unlabeled data[C]//Pro-ceedings of Advances in Neural Information Processing Systems.SanFrancisco:Morgan Kaufmann,1994:112-119.
[9](美)CRISTIANINI N,SHAWE-TAYLOR J.支持向量机导论[M].李国正,王蒙,曾华军,译.北京:电子工业出版社,2004.
[10]张伟,柳先辉,丁毅,等.基于支持向量回归的多时间序列自回归方法[J].计算机应用,2012,32(9):2508-2511.
[11]HADSELL R,CHOPRA S,LECUN Y.Dimensionality reductionby learning an invariant mapping[C]//CVPR'06:Proceedings ofthe 2006 IEEE Computer Society Conference on Computer Visionand Pattern Recognition.Washington,DC:IEEE Computer Socie-ty,2006,2:1735-1742.
[12]石陆魁,张军,宫晓腾.基于邻域保持的流形学习算法评价模型[J].计算机应用,2012,32(9):2516-2519.
[13]张瑞丽,张继福.基于w-距离均值的模糊聚类算法[J].计算机应用,2012,32(7):1978-1982.
[14]邵超,张慧娟.应用于不完整流形的ISOMAP算法[J].计算机应用,2012,32(7):1987-1990.
[15]易淼,刘小兰.基于相对变换的半监督分类算法[J].计算机应用,2011,31(10):2793-2795.