An assessment of the effectiveness of a random forest classifier for land-cover classification
An assessment of the effectiveness of a random forest classifier for land-cover classification
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DOI：
10.1016/j.isprsjprs.2011.11.002
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发表时间：
2012-01-01
影响因子：
12.7
通讯作者：
Rigol-Sanchez, J. P.
中科院分区：
文献类型：
--
作者：
Rodriguez-Galiano, V. F.;Ghimire, B.;Rigol-Sanchez, J. P.
利用遥感数据进行土地覆盖监测需要有强有力的分类方法，以便能够准确地绘制复杂的土地覆盖和土地利用类别的地图。随机森林（RF）是一种功能强大的机器学习分类器，在陆地遥感中相对未知，与更传统的模式识别技术相比，遥感界尚未对其进行彻底评估。RF的主要优势包括：其非参数性质;高分类准确性;以及确定变量重要性的能力。然而，用于分类的分裂规则是未知的，因此RF可以被认为是黑盒类型分类器。RF提供了一个算法，估计缺失值;和灵活性，以执行几种类型的数据分析，包括回归，分类，生存分析，和无监督learning.In本文中，RF分类器的性能进行了探讨，土地覆盖分类的复杂地区。评估基于几个标准：映射准确性，对数据集大小和噪声的敏感性。Landsat-5专题制图仪在欧洲春季和夏季捕获的数据与来自数字地形模型的辅助变量一起用于对西班牙南部14个不同的土地类别进行分类。结果表明，RF算法产生准确的土地覆盖分类，92%的整体精度和Kappa指数为0.92。RF对训练数据减少和噪声具有鲁棒性，因为仅在数据减少和噪声增加值分别大于50%和20%时观察到kappa值的显著差异。此外，RF确定的对土地覆盖分类最重要的变量与预期相符。McNemar检验表明，在0.00001显著性水平下，随机森林模型在单个决策树上的整体性能更好。(C)2011年国际摄影测量与遥感学会（ISRS）由Elsevier B. V.发布保留所有权利。
Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning.In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.