Traditional supervised classifications for remote sensing-based water quality monitoring count on a set of classifiers to retrieve features and improve their prediction accuracies based on ground truth samples. However, many existing feature extraction methods in remote sensing are unable to exhibit multiple-instance nonlinear spatial pattern recognition at scales via ensemble learning. This paper designed for lake algal bloom monitoring presents intelligent feature extraction for harmonizing local and global features via tensor flow-based ensemble learning with integrated biomimetic and computational intelligence. To explore such complexity, an Integrated Biomimetic and Ensemble Learning Algorithm (IBELA) was developed to synthesize the contribution from different classifiers associated with the biomimetic philosophy of integrated bands. It leads to strengthened multiple-instance spatial pattern recognition in lake algal bloom monitoring via image fusion at the decision level. With the implementation of IBELA, a case study of a eutrophic freshwater lake, Lake Managua, for water quality monitoring leads to demonstrate six input visual senses showing different impacts on retrieving Chl-a concentrations in the dry and wet season, respectively. The input of total nitrogen from the watershed plays the most important role in water quality variations in both seasons in a watershed-based food–water nexus. Although ultraviolet and microwave bands are important in the dry season, Secchi disk depth is critical in the wet season for water quality monitoring.

用于基于遥感的水质监测的传统监督分类依靠一组分类器来检索特征并基于地面实况样本提高其预测精度。然而，许多现有的遥感特征提取方法无法通过集成学习在尺度上表现出多实例非线性空间模式识别。本文设计用于湖泊藻华监测，提出了智能特征提取，通过基于张量流的集成学习与集成的仿生和计算智能来协调局部和全局特征。为了探索这种复杂性，开发了一种集成仿生和集成学习算法 (IBELA)，以综合与集成带的仿生哲学相关的不同分类器的贡献。通过决策层的图像融合，加强了湖泊藻华监测中的多实例空间模式识别。随着 IBELA 的实施，富营养化淡水湖马那瓜湖的水质监测案例研究表明，六种输入视觉感官分别显示了在旱季和雨季检索叶绿素 a 浓度的不同影响。在以流域为基础的食物-水关系中，流域总氮的输入在两个季节的水质变化中起着最重要的作用。虽然紫外线和微波波段在旱季很重要，但 Secchi 圆盘深度在雨季对水质监测至关重要。随着 IBELA 的实施，富营养化淡水湖马那瓜湖的水质监测案例研究表明，六种输入视觉感官分别显示了在旱季和雨季检索叶绿素 a 浓度的不同影响。在以流域为基础的食物-水关系中，流域总氮的输入在两个季节的水质变化中起着最重要的作用。虽然紫外线和微波波段在旱季很重要，但 Secchi 圆盘深度在雨季对水质监测至关重要。随着 IBELA 的实施，富营养化淡水湖马那瓜湖的水质监测案例研究表明，六种输入视觉感官分别显示了在旱季和雨季检索叶绿素 a 浓度的不同影响。在以流域为基础的食物-水关系中，流域总氮的输入在两个季节的水质变化中起着最重要的作用。虽然紫外线和微波波段在旱季很重要，但 Secchi 圆盘深度在雨季对水质监测至关重要。在以流域为基础的食物-水关系中，流域总氮的输入在两个季节的水质变化中起着最重要的作用。虽然紫外线和微波波段在旱季很重要，但 Secchi 圆盘深度在雨季对水质监测至关重要。在以流域为基础的食物-水关系中，流域总氮的输入在两个季节的水质变化中起着最重要的作用。虽然紫外线和微波波段在旱季很重要，但 Secchi 圆盘深度在雨季对水质监测至关重要。