River turbidity, serving as an important evaluation index for monitoring water contamination and guiding pollution control, is mainly measured based on the data gathered from contacting turbidity sensors or contactless space satellites. Nevertheless, the prevalence of those abovementioned two measurements is strongly limited due to the disadvantages of low-density spatial distribution of sensor data and extremely high price of satellite data. To solve such difficulty, depending on the Google earth engine (GEE) that freely supplies hyperspectral remote sensing data, in this article, we propose a novel river turbidity measurement model based on random forest ensemble. First, by fully taking advantage of each spectral information and their tuned spectral information, a newly proposed full combination subspace is deployed to generate all the possible base random forests. Second, we introduce a novel error-minimization-based pruning algorithm to circularly delete poor base random forests in accordance with the dynamic threshold. Finally, a weighted average method solved by regularized linear regression is used to aggregate the entire remainder base random forests that are preserved after pruning, thereby yielding the final measurement result of river turbidity. Experiments corroborate the superiority of our proposed model over state-of-the-art competitors and its simplified counterparts.

中文翻译：

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用于从空间遥感数据测量河流浊度的随机森林集合

河流浊度作为监测水污染和指导污染控制的重要评价指标，主要基于接触式浊度传感器或非接触式空间卫星采集的数据进行测量。然而，由于传感器数据的低密度空间分布和卫星数据的极高价格等缺点，上述两种测量方法的普及受到很大限制。为解决这一难题，本文依靠免费提供高光谱遥感数据的谷歌地球引擎（GEE），提出了一种基于随机森林集合的新型河流浊度测量模型。首先，通过充分利用每个光谱信息及其调谐的光谱信息，部署了一个新提出的完全组合子空间来生成所有可能的基础随机森林。其次，我们引入了一种新的基于误差最小化的剪枝算法，根据动态阈值循环删除较差的基础随机森林。最后，通过正则化线性回归求解的加权平均法聚合修剪后保留的整个剩余基随机森林，从而得到河流浊度的最终测量结果。实验证实了我们提出的模型优于最先进的竞争对手及其简化的同行。使用正则化线性回归求解的加权平均法聚合修剪后保留的整个剩余基随机森林，从而得到河流浊度的最终测量结果。实验证实了我们提出的模型优于最先进的竞争对手及其简化的同行。使用正则化线性回归求解的加权平均法聚合修剪后保留的整个剩余基随机森林，从而得到河流浊度的最终测量结果。实验证实了我们提出的模型优于最先进的竞争对手及其简化的同行。