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Edited-bootstrapped support vector machines for one-class data classification.

机译：用于一类数据分类的自举引导支持向量机。

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摘要

This research focuses on one-class remote sensing data classification problems. In general remote sensing data consists of samples that belong to different classes and are also highly overlapped in the feature space. Classification of such non-linear data becomes even more difficult if only one class is of interest. Therefore first and the foremost challenge lie in choosing a classifier. In this work, support vector machines (SVMs) are implemented as they are recently developed classifiers aiming at maximizing the margin between two clusters in a projected feature space. The SVM was mainly adapted to solve one-class problems, i.e., novelty detection. The one-class SVM (OCSVM) has a parameter nu to control the percentage of outliers or minority data. If outliers are present in the learning data set then classifiers tend to pick them as potential support vectors, which in turn degrade the classification performance. Different bootstrapping techniques were proposed in which pseudo replicate data sets were created by re-sampling to eliminate the affect of outliers during the training process.; We have proposed an edited-bootstrapped SVM for one-class data classification. This is an iterative process in which OCSVM is used for data classification. The parameter nu is assigned to a small value as the percentage of outliers is unknown. In each iteration the detected outliers are removed from the training data set. The iterations stop when there are no more outliers to be detected from the training data set. This algorithm was used to address one-class remote sensing problems associated with the United States Department of Agriculture (USDA)'s Conservation Reserve Program (CRP). The CRP program seeks to convert highly erodible lands with active crop production to permanent vegetative cover. Specifically, there are two essential needs pertaining to CRP management and evaluation, i.e., CRP compliance monitoring and CRP mapping. Multi-spectral and multi-temporal Landsat TM imageries are used for the data classification. Compliance monitoring checks if the farmers are following the contract stipulations. CRP mapping produces up-to-date and accurate maps of CRP lands based on satellite imageries. In each CRP tract most of the area is assumed to be compliant. Our algorithm fails if most of the CRP tract is noncompliant. Multiple edited-bootstrapped OCSVMs are trained on different CRP cover types, and are applied for CRP classification individually. The complete CRP map is obtained by merging the different classifier outputs. The main advantage of the proposed algorithm is that both the CRP issues are combined into one flow. The simulation results proved that the proposed algorithm produces better classification results when compared with the existing bootstrapping techniques.
机译：这项研究集中在一类遥感数据分类问题上。通常，遥感数据由属于不同类别的样本组成，并且在特征空间中也高度重叠。如果仅关注一类，则此类非线性数据的分类将变得更加困难。因此，首要的挑战在于选择分类器。在这项工作中，由于支持向量机（SVM）是最近开发的分类器，因此得以实现，其目的是使投影特征空间中两个聚类之间的裕度最大化。 SVM主要适用于解决一类问题，即新颖性检测。一类SVM（OCSVM）具有参数nu，用于控制离群值或少数数据的百分比。如果学习数据集中存在异常值，则分类器倾向于将它们选为潜在的支持向量，从而降低了分类性能。提出了不同的引导技术，其中通过重新采样创建伪复制数据集，以消除训练过程中异常值的影响。我们为一类数据分类提出了一种自举式SVM。这是一个迭代过程，其中OCSVM用于数据分类。由于离群值的百分比未知，因此将参数nu分配给一个较小的值。在每次迭代中，从训练数据集中删除检测到的离群值。当不再需要从训练数据集中检测到异常值时，迭代就会停止。该算法用于解决与美国农业部（USDA）的保护储备计划（CRP）相关的一类遥感问题。 CRP计划旨在将作物生产活跃的高侵蚀性土地转变为永久性植被。具体而言，有两个与CRP管理和评估有关的基本需求，即CRP合规性监视和CRP映射。多光谱和多时间Landsat TM影像用于数据分类。遵守情况监控检查农民是否遵守合同规定。 CRP制图可根据卫星图像生成CRP土地的最新，准确的地图。在每个CRP区域中，大部分区域被认为是合规的。如果大多数CRP文件不符合要求，我们的算法就会失败。对多个自举引导的OCSVM进行了不同CRP封面类型的培训，并分别应用于CRP分类。通过合并不同的分类器输出来获得完整的CRP图。所提出算法的主要优点是将两个CRP问题合并为一个流程。仿真结果表明，与现有的自举技术相比，该算法具有更好的分类效果。

著录项

  • 作者

    Sravanthi, Anne Krishna.;

  • 作者单位

    Oklahoma State University.;

  • 授予单位 Oklahoma State University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2006
  • 页码 86 p.
  • 总页数 86
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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