摘要
目前,支持向量机技术(SVM)在遥感信息获取中普遍受到参数选择不准确和小样本问题的制约。针对这些问题,提出一种新的半监督集成SVM(EPS3VM)分类模型。模型一方面利用自适应变异粒子群优化算法对SVM参数寻优以提高基分类器精度(PSVM);另一方面采用自训练算法(Self-training),充分利用大量廉价的未标记样本产生性能差异的半监督分类器个体(PS3VM),其中,在未标记样本标注过程中,引入模糊聚类算法(Gustafson-kessel)来控制错误类别的输入,最后对个体分类器采用加权集成策略,以进一步提高分类模型的泛化能力。为了测试其性能,应用该模型进行多光谱遥感影像的土地覆盖分类实验,并与PSVM、PS3VM进行对比,分类精度从PSVM的88.48%提高到96.88%,Kappa系数由0.8546提高到0.9606。结果表明,EPS3VM在克服传统SVM参数选择不准确的同时,有效地应对了小样本问题,分类性能更优。
Nowadays, most SVM-based remote sensing classification methods are challenged by incorrectly selecting pa- rameters values and the small sample problems. This paper proposed a novel ensemble model with semisupervised SVM （EPS3VM） to address the problem of remote sensing images classification. The key characteristics of this approach are to 1）self-adaptive mutation particle swarm optimizer is introduced to improve the generalization performance of the SVM classifier （PSVM）, 2）self-training semisupervised learning method that leverages large amounts of relatively inex- pensive unlabeled data is presented to produce a number of semisupervised classifiers （PS3VM）. Then by the weighted voting method, these classifiers are combined so as to improve the generalization ability of the classification model. In or- der to reduce the impact of this issue by incorrect labels, Gustafson-kessel fuzzy clustering algorithm （GKclust） is used for selecting the useful points from the unlabeled set. The effectiveness of the proposed classification approach is de- monstrated for identifying different land cover regions in multispectral remote sensing imagery. In particular, the perfor- mance of the EPS3VM is compared with PSVM and PS3VM in terms of classification accuracy and kappa coefficient. On an average, the EPS3VM model yields an overall accuracy of 96. 88% against 88. 48% for PSVM and outperformed PS3VM in terms of overall accuracy （by about 5 %）. The obtained results clearly confirm the effectiveness and robust- ness of the EPS3VM approach to the remote sensing land cover classification.
出处
《计算机科学》
CSCD
北大核心
2013年第7期206-210,共5页
Computer Science
基金
国家重点基础研究发展计划(973计划)课题(2009CB421103)
中国科学院重点部署项目课题(KZZD-EW-08-02)
吉林省科技发展计划项目(20130522177JH)资助
