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Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model

机译:使用极端梯度增强决策树模型从遥感数据中对藻华物种进行分类

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摘要

Coastal and open ocean regions throughout the world are now subject to an array of toxic, harmful, or more intense algal blooms with an increasing trend of incidence over large geographical areas due to anthropogenic factors such as pollution and climate shifts. To date, detection capabilities of causative species based on remote sensing data are greatly limited because of the difficulties in interpreting the composite reflectance signal from different water features and types. In the present study, an accurate and reliable method is developed to automatically detect the onset of blooms and correctly classify the bloom species in Arabian Sea and Bay of Bengal waters using remote sensing data. A data-driven approach using machine learning algorithm is devised based on reflectance spectral signatures and tested on several MODIS-Aqua (Moderate Resolution Imaging Spectroradiometer) data for classifying the dominant water categories, including clear ocean waters devoid of sediments and algal blooms, sediment-laden coastal waters, and three major algal blooms, Trichodesmium erythraeum, Noctiluca scintillans and Cochlodinium polykrikoides. An extreme gradient boosted decision tree (XGBoost) model is chosen to improve the prediction accuracy by prevention of overfitting, which increases the scalability of the model on several unseen test data. This model was trained using 1.5 million samples and resulted in a classification accuracy of over 98%. When the results were validated using forty thousand random samples from the known blooms, an overall accuracy more than 96.8% was achieved. The applicability of the trained XGBoost model was further verified using MODIS-Aqua images, and it showed promise for successful detection and identification of well-documented blooms. The use of spectral information to classify algal blooms makes this method more robust and easily adaptable to different ocean colour sensors with a scope to accommodate other major algal blooms.
机译:现在,由于污染和气候变化等人为因素,全世界的沿海地区和开阔海洋地区都遭受着一系列有毒,有害或更强烈的藻华,在大地理区域内的发病率呈上升趋势。迄今为止,由于难以解释来自不同水特征和类型的复合反射信号,因此基于遥感数据的致病物种的检测能力受到很大限制。在本研究中,开发了一种准确可靠的方法,可以使用遥感数据自动检测水华的发生并正确分类阿拉伯海和孟加拉湾水域的水华物种。设计了一种基于机器学习算法的数据驱动方法,该方法基于反射光谱特征,并在几种MODIS-Aqua(中等分辨率成像光谱仪)数据上进行了测试,以对主要水类别进行分类,包括没有沉积物和藻华的清澈海水,满载的沿海水域和三处主要的藻华,红藻毛滴虫(Trichodesmium erythraeum),夜光藻(Noctiluca scintillans)和多球藻(Cochlodinium polykrikoides)。选择了极端梯度增强决策树(XGBoost)模型以通过防止过度拟合来提高预测准确性,从而增加了该模型在一些看不见的测试数据上的可伸缩性。该模型使用150万个样本进行了训练,分类精度超过98%。当使用来自已知花样的四万个随机样本验证结果时,总体准确度超过96.8%。使用MODIS-Aqua图像进一步验证了训练后的XGBoost模型的适用性,它显示了成功检测和识别记录充分的水华的希望。使用光谱信息对藻华进行分类可以使该方法更加健壮,并且可以轻松适应不同的海洋颜色传感器,并具有适应其他主要藻华的范围。

著录项

  • 来源
    《International journal of remote sensing》 |2019年第24期|9412-9438|共27页
  • 作者

  • 作者单位

    Indian Inst Technol Madras Dept Ocean Engn Ocean Opt & Imaging Lab Chennai Tamil Nadu India;

  • 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
  • 中图分类
  • 关键词

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