Multi-method ensemble selection of spectral bands related to leaf biochemistry
Multi-method ensemble selection of spectral bands related to leaf biochemistry
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DOI：
10.1016/j.rse.2015.03.033
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发表时间：
2015-07-01
影响因子：
13.5
通讯作者：
Martin, Roberta E.
中科院分区：
文献类型：
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作者：
Feilhauer, Hannes;Asner, Gregory P.;Martin, Roberta E.
多方法组合通常被认为比单独应用一种方法能产生更可靠的结果。在这里，我们测试了对于叶片特征的量化，与单一技术的结果相比，由偏最小二乘(PLSR)、随机森林(RFR)和支持向量机回归(SVMR)模型组成的回归模型集合是否能够提高光谱波段选择过程的稳健性。利用1个人工和5个叶水平光谱的测量数据集以及有关叶片叶绿素、干物质和水分的相应信息对集合方法进行了测试。以拟合优度优化的PLSR模型是一种既定的频段选择方法，用于评估乐团的性能。尽管模型在集合内的拟合度比用参考方法获得的拟合度要差，但该集合能够提供在所有数据集上具有更高一致性的波段选择。由于系综内方法的选择特性，系综选择是适度狭窄和限制性的，但与文献中公布的已知吸收特征很好地一致。我们的结论是，分析不同模型类型的一致性范围是选择与所研究的叶片特性相关的一组稳健光谱带的有效方法。这可能有助于加深我们对叶片和冠层中生化和生物物理特性的光谱响应的理解。(C)2015 Elsevier Inc.保留所有权利。
Multi-method ensembles are generally believed to return more reliable results than the application of one method alone. Here, we test if for the quantification of leaf traits an ensemble of regression models, consisting of Partial Least Squares (PLSR), Random Forest (RFR), and Support Vector Machine regression (SVMR) models, is able to improve the robustness of the spectral band selection process compared to the outcome of a single technique alone. The ensemble approach was tested using one artificial and five measured data sets of leaf level spectra and corresponding information on leaf chlorophyll, dry matter, and water content. PLSR models optimized for the goodness of fit, an established approach for band selection, were used to evaluate the performance of the ensemble. Although the fits of the models within the ensemble were poorer than the fits achieved with the reference approach, the ensemble was able to provide a band selection with higher consistency across all data sets. Due to the selection characteristics of the methods within the ensemble, the ensemble selection is moderately narrow and restrictive but in good agreement with known absorption features published in literature. We conclude that analyzing the range of agreement of different model types is an efficient way to select a robust set of spectral bands related to the foliar properties under investigation. This may help to deepen our understanding of the spectral response of biochemical and biophysical traits in foliage and canopies. (C) 2015 Elsevier Inc. All rights reserved.