Decision tree classification of land cover from remotely sensed data
Decision tree classification of land cover from remotely sensed data
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DOI：
10.1016/s0034-4257(97)00049-7
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发表时间：
1997-09-01
影响因子：
13.5
通讯作者：
Brodley, CE
中科院分区：
文献类型：
--
作者：
Friedl, MA;Brodley, CE
决策树分类算法对于土地覆盖绘图问题具有巨大的潜力，并且相对于更传统的模式识别技术（例如最大似然分类），尚未经过遥感界的详细测试。在本文中，我们提出了几种类型的决策树分类算法，并在三个不同的遥感数据集上对其进行了评估。测试的决策树分类算法包括单变量决策树、多变量决策树和能够在单个决策树结构内包括几种不同类型的分类算法的混合决策树。将每种决策树算法产生的分类精度与最大似然分类器和线性判别函数分类器进行比较。该分析结果表明，决策树算法在分类精度方面始终优于最大似然判别函数分类器和 Zi-near 判别函数分类器。特别是，混合树始终为测试的数据集产生最高的分类精度。更一般地说，这项工作的结果表明，决策树凭借其相对简单、明确和直观的分类结构，在遥感应用方面具有多种优势。此外，决策树算法是严格非参数的，因此不对输入数据的分布做出任何假设，并且对于输入特征和类标签之间的非线性和噪声关系而言是灵活且鲁棒的。 (C) 爱思唯尔科学公司，1997 年。
Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms and evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and Zi-near discriminant function classifiers in regard to classification accuracy. In particular the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages far remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels. (C) Elsevier Science Inc., 1997.