摘要翻译：
自从卫星图像应用于土地覆盖制图以来，图像分类一直是研究的热点之一。图像分类器是从卫星图像中提取土地覆盖信息的算法。大多数最初的研究集中在开发和应用算法来更好地改进现有的和新兴的分类器。本文提出了一种范式转换，即使用一个分类器委员会来确定最终的分类输出。集成系统的两个关键组成部分是分类器之间应该有多样性，以及应该有一个将结果组合的机制。本文中集成系统的成员包括：线性支持向量机、高斯支持向量机和二次支持向量机。最终的输出是通过单个分类器的简单多数票决定的。从获得的结果可以观察到，由集成系统生成的最终派生映射可以潜在地改善由组成集成系统的单个分类器派生的结果。在这种情况下，集成系统的分类精度优于线性和二次支持向量机的结果。然而，它小于RBF支持向量机的结果。进一步研究的领域可以集中在提高本研究中使用的集成系统的多样性上。
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英文标题：
《An svm multiclassifier approach to land cover mapping》
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作者：
Gidudu Anthony, Hulley Gregg, and Marwala Tshilidzi
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.
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PDF链接：
https://arxiv.org/pdf/1007.1766