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首页> 外文期刊>International journal of remote sensing >Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data
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Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

机译：使用EO-1 Hyperion高光谱数据估算甘蔗叶氮浓度的随机森林回归和光谱带选择

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摘要

Nitrogen (N) is one of the most important limiting nutrients for sugarcane production. Conventionally, sugarcane N concentration is examined using direct methods such as collecting leaf samples from the field followed by analytical assays in the laboratory. These methods do not offer real-time, quick, and non-destructive strategies for estimating sugarcane N concentration. Methods that take advantage of remote sensing, particularly hyperspectral data, can present reliable techniques for predicting sugarcane leaf N concentration. Hyperspectral data are extremely large and of high dimensionality. Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant spectral features. The aim of this study was to explore the potential of a random forest (RF) regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration. To achieve this, two Hyperion images were captured from fields of 6-7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used as a feature-selection and regression method to analyse the spectral data. Stepwise multiple linear (SML) regression was also examined to predict the concentration of sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The results showed that sugarcane leaf N concentration can be predicted using both nonlinear RF regression (coefficient of determination, R~2 = 0.67; root mean square error of validation (RMSEV) = 0.15%; 8.44% of the mean) and SML regression models (R~2 = 0.71; RMSEV = 0.19%; 10.39% of the mean) derived from the first-order derivative of reflectance. It was concluded that the RF regression algorithm has potential for predicting sugarcane leaf N concentration using hyperspectral data.
机译：氮（N）是甘蔗生产中最重要的限制性营养素之一。常规上，甘蔗氮浓度的测定采用直接方法，例如从田间收集叶片样品，然后在实验室进行分析。这些方法没有提供用于估计甘蔗氮浓度的实时，快速和无损策略。利用遥感，尤其是高光谱数据的方法，可以提供预测甘蔗叶氮浓度的可靠技术。高光谱数据非常大且具有高维度。由于相邻波段之间的强相关性，许多高光谱特征是多余的。因此，高光谱数据的分析很复杂，需要通过选择最相关的光谱特征来简化。这项研究的目的是探索潜在的随机森林（RF）回归算法在预测甘蔗叶N浓度所需的高光谱数据中选择光谱特征的潜力。为此，从6-7个月大的N19甘蔗田中捕获了两个Hyperion图像。机器学习RF算法被用作特征选择和回归方法来分析光谱数据。还检查了逐步多元线性（SML）回归，以预测高光谱数据中冗余减少后甘蔗叶N的浓度。结果表明，可以使用非线性RF回归（测定系数，R〜2 = 0.67；验证的均方根误差（RMSEV）= 0.15％；平均值的8.44％）和SML回归模型来预测甘蔗叶N浓度（R〜2 = 0.71; RMSEV = 0.19％;平均值的10.39％）由反射率的一阶导数得出。结论是，RF回归算法具有使用高光谱数据预测甘蔗叶氮浓度的潜力。

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  • 来源
    《International journal of remote sensing》 |2013年第2期|712-728|共17页
  • 作者

    Elfatih M. Abdel-Rahman; Fethi B. Ahmed; Riyad Ismail;

  • 作者单位

    School of Environmental Sciences, Howard College Campus, University of KwaZulu-Natal,Durban 4041, South Africa,Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan;

    School of Environmental Sciences, Howard College Campus, University of KwaZulu-Natal,Durban 4041, South Africa;

    School of Environmental Sciences, Howard College Campus, University of KwaZulu-Natal,Durban 4041, South Africa;

  • 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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