A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada
A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada
复制标题
DOI：
10.1016/j.jag.2013.12.016
复制
发表时间：
2014-06-01
影响因子：
7.5
通讯作者：
Shang, Jiali
中科院分区：
文献类型：
--
作者：
Champagne, Catherine;McNairn, Heather;Shang, Jiali
利用遥感进行的土地覆盖和土地利用分类正日益成为监测环境变化的制度化框架数据集。因此，对分类准确性的可靠声明的需求至关重要。本文介绍了一种方法来估计分类模型的准确性，使用自助方法的信心。使用这种方法，它被发现，分类的准确性和信心，而密切相关的，可以用互补的方式提供更多的信息地图的准确性和定义组的类，并告知未来的参考采样策略。整体分类精度的增加与调查的领域，其中的宽度的分类置信界限减少的数量。个别类的准确性和信心是非线性相关的调查领域的数量。结果表明，一些类可以准确和自信地估计较少数量的样本，而其他人需要更大的参考数据集，以达到令人满意的结果。这种方法是对估计类别准确度和置信度的其他方法的改进，因为它使用重复采样来产生对分类准确度和置信度的范围的更现实的估计，该范围可以用不同的参考数据输入来获得。皇冠版权所有（C）2014由爱思唯尔B.V.出版保留所有权利。
Land cover and land use classifications from remote sensing are increasingly becoming institutionalized framework data sets for monitoring environmental change. As such, the need for robust statements of classification accuracy is critical. This paper describes a method to estimate confidence in classification model accuracy using a bootstrap approach. Using this method, it was found that classification accuracy and confidence, while closely related, can be used in complementary ways to provide additional information on map accuracy and define groups of classes and to inform the future reference sampling strategies. Overall classification accuracy increases with an increase in the number of fields surveyed, where the width of classification confidence bounds decreases. Individual class accuracies and confidence were non-linearly related to the number of fields surveyed. Results indicate that some classes can be estimated accurately and confidently with fewer numbers of samples, whereas others require larger reference data sets to achieve satisfactory results. This approach is an improvement over other approaches for estimating class accuracy and confidence as it uses repetitive sampling to produce a more realistic estimate of the range in classification accuracy and confidence that can be obtained with different reference data inputs. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.