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首页> 外文期刊>International journal of remote sensing >Predicting chlorophyll-a using Landsat 8 OLI sensor data and the non-linear RANSAC method - a case study of Nakdong River, South Korea
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Predicting chlorophyll-a using Landsat 8 OLI sensor data and the non-linear RANSAC method - a case study of Nakdong River, South Korea

机译：利用Landsat 8 OLI传感器数据和非线性RANSAC方法预测叶绿素a-以韩国洛东河为例

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摘要

To predict the degree of chlorophyll-a (chl-a) concentration as a water quality indicator, we propose a novel method that uses data collected by satellite remote sensors and a non-linear random sample consensus (NL-RANSAC) algorithm. In this study, multispectral images obtained from the Landsat OLI sensor and in situ sampling data obtained from the Nakdong River in South Korea are used for estimating a regression model. Because the RANSAC algorithm based on linear models is not appropriate for modelling non-linear data distribution, such as chl-a concentration and influential outlier data, in this article an NL-RANSAC model represented by an explicit polynomial curve is proposed instead of the RANSAC. The results of experiments in which NL-RANSAC-based regressions and normal regression models were compared using a calibration and a validation set clearly show that the proposed second-order NL-RANSAC is a good choice for estimating chl-a concentration, because it shows an average 0.3987 higher R-2 assessment than normal regression models in validation set. In addition, we prove that the second-order NL-RANSAC model is the most appropriate regression model for estimating chl-a concentration by using Landsat 8 OLI sensor imagery in the midstream of the Nakdong River.
机译：为了预测叶绿素-a（chl-a）浓度作为水质指标的程度，我们提出了一种使用卫星遥感器收集的数据和非线性随机样本共识（NL-RANSAC）算法的新方法。在这项研究中，从Landsat OLI传感器获得的多光谱图像和从韩国Nakdong河获得的原位采样数据被用于估计回归模型。由于基于线性模型的RANSAC算法不适用于对chl-a浓度和有影响的离群数据等非线性数据分布进行建模，因此本文提出了一种由显式多项式曲线表示的NL-RANSAC模型，而不是RANSAC 。使用校准和验证集比较基于NL-RANSAC的回归模型和正态回归模型的实验结果清楚地表明，建议的二阶NL-RANSAC是估算chl-a浓度的不错选择，因为它表明验证集中的R-2评估平均比正常回归模型高0.3987。此外，我们证明了二阶NL-RANSAC模型是最合适的回归模型，通过利用那洞河中游的Landsat 8 OLI传感器图像估算chl-a浓度。

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  • 来源
    《International journal of remote sensing》 |2016年第14期|3255-3271|共17页
  • 作者

    Kim Hyeong Hun; Ko Byoung Chul; Nam Jae Yeal;

  • 作者单位

    Keimyung Univ, Dept Comp Engn, Daegu 704701, South Korea;

    Keimyung Univ, Dept Comp Engn, Daegu 704701, South Korea;

    Keimyung Univ, Dept Comp Engn, Daegu 704701, South Korea;

  • 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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