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首页> 外文期刊>International journal of remote sensing >Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification
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Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification

机译：在基于对象的分类中使用参考多边形时，训练和验证样本选择对分类准确性和准确性评估的影响

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摘要

Reference polygons are homogenous areas that aim to provide the best available assessment of ground condition that the user can identify. Delineation of such polygons provides a convenient and efficient approach for researchers to identify training and validation data for supervised classification. However, the spatial dependence of training and validation data should be taken into account when the two data sets are obtained from a common set of reference polygons. We investigate the effect on classification accuracy and the accuracy estimates derived from the validation data when training and validation data are obtained from four selection schemes. The four schemes are composed of two sampling designs (simple random and systematic) and two methods for splitting sample points between training and validation (validation points in separate polygons from training points and validation points and training points split within the same polygons). A supervised object-based classification of the study region was repeated 30 times for each selection scheme. The selection scheme did not impact classification accuracy, but estimates of overall (OA), user's (UA), and producer's (PA) accuracies produced from the validation data overestimated accuracy for the study region by about 10%. The degree of overestimation was slightly greater when the validation sample points were allowed to be in the same polygons as the training data points. These results suggest that accuracy estimates derived from splitting training and validation within a limited set of reference polygons should be regarded with suspicion. To be fully confident in the validity of the accuracy estimates, additional validation sample points selected from the region outside the reference polygons may be needed to augment the validation sample selected from the reference polygons.
机译：参考多边形是同质区域，旨在提供用户可以识别的最佳地面状况评估。此类多边形的轮廓为研究人员识别监督分类的训练和验证数据提供了一种便捷有效的方法。但是，当从共同的一组参考多边形中获得两个数据集时，应考虑训练和验证数据的空间依赖性。当从四个选择方案中获得训练和验证数据时，我们研究了对分类准确性和从验证数据得出的准确性估计值的影响。这四种方案由两种采样设计（简单随机和系统的）和两种在训练和验证之间划分样本点的方法（与训练点分离的多边形中的验证点以及在同一多边形内划分的验证点和训练点）组成。对于每个选择方案，对研究区域进行有监督的基于对象的分类重复30次。选择方案不会影响分类准确性，但是根据验证数据得出的总体（OA），用户（UA）和生产者（PA）准确性的估计值会高估研究区域的准确性约10％。当验证样本点与训练数据点位于相同的多边形中时，高估程度会稍高。这些结果表明，应该怀疑从有限的一组参考多边形中分割训练和验证得出的精度估计。为了对准确性估计的有效性完全有信心，可能需要从参考多边形之外的区域中选择其他验证样本点，以增强从参考多边形中选择的验证样本。

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  • 来源
    《International journal of remote sensing》 |2013年第20期|6914-6930|共17页
  • 作者

    Zhen Zhen; Lindi J. Quackenbush; Stephen V. Stehman; Lianjun Zhang;

  • 作者单位

    Department of Forest and Natural Resources Management, College of Environmental Science andForestry, State University of New York, Syracuse, NY 13210, USA;

    Department of Environmental and Resource Engineering, College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA;

    Department of Forest and Natural Resources Management, College of Environmental Science andForestry, State University of New York, Syracuse, NY 13210, USA;

    Department of Forest and Natural Resources Management, College of Environmental Science andForestry, State University of New York, Syracuse, NY 13210, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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