A novel transductive SVM for semisupervised classification of remote-sensing images
A novel transductive SVM for semisupervised classification of remote-sensing images
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DOI：
10.1109/tgrs.2006.877950
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发表时间：
2006-11-01
影响因子：
8.2
通讯作者：
Marconcini, Mattia
中科院分区：
文献类型：
--
作者：
Bruzzone, Lorenzo;Chi, Mingmin;Marconcini, Mattia
本文介绍了一种半监督分类方法，利用标记和未标记的样本解决不适定问题的支持向量机（SVM）。该方法是基于最近的发展，统计学习理论有关的转导推理，特别是转导支持向量机（TSVM）。TSVM利用特定的迭代算法，逐渐搜索一个可靠的分离超平面（在内核空间）与一个转导的过程中，将标记和未标记的样本在训练阶段。基于文献中提出的TSVM的属性分析，提出了一种新的改进的TSVM分类器设计用于解决不适定的遥感问题。特别是，所提出的技术：1）是基于一种新的转导过程，利用加权策略未标记。模式，基于时间依赖的标准; 2）能够减轻次优模型选择的影响（这在小规模训练集的存在下是不可避免的）;和3）可以解决多类情况。实验结果证实了所提出的方法的有效性上的一组不适定的遥感分类问题，代表不同的操作条件。
This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled. patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass, cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions.