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METHOD OF PREDICTING CONCENTRATION OF PATHOGENIC MICROORGANISM USING REMOTE SENSING HYPERSPECTRAL IMAGE

机译:遥感高光谱图像预测病原微生物浓度的方法

摘要

A main objective of the present invention is to provide a method which uses a remote sensing hyperspectral image to calculate a primary water quality index, and uses the primary water quality index and measured concentration data of a pathogenic microorganism to construct a water quality database; and then uses the water quality database to predict concentration of a pathogenic microorganism of a water system which is not measured. To achieve this objective, the method of predicting a concentration of the pathogenic microorganism using the remote sensing hyperspectral image comprises: a step of acquiring a hyperspectral image of a target water system by remote photographing; a step of using the acquired hyperspectral image to calculate a primary water quality index consisting of one or more water quality items capable of being calculated by a regression analysis model; a step of using the calculated primary water quality index and concentration data of the pathogenic microorganism measured in the target water system as a single set to construct a water quality database; and a step of using a machine learning model trained by a primary water quality index calculated from a new hyperspectral image remotely photographed in the target water system and the water quality database to predict a concentration of the pathogenic microorganism not measured.;COPYRIGHT KIPO 2017
机译:本发明的主要目的是提供一种利用遥感高光谱图像计算一次水质指标,并利用一次水质指标和病原微生物测得的浓度数据建立水质数据库的方法。然后使用水质数据库预测未测量的水系统中病原微生物的浓度。为了实现该目的,使用遥感高光谱图像来预测病原微生物的浓度的方法包括:通过远程拍摄来获取目标水系统的高光谱图像的步骤;使用获取的高光谱图像来计算主要水质指数的步骤,该主要水质指数由一个或多个能够通过回归分析模型计算的水质项目组成;利用计算出的一级水质指数和在目标水系统中测得的病原微生物的浓度数据作为一个整体,建立水质数据库的步骤;以及使用机器学习模型训练的机器学习模型的步骤,该模型是根据在目标水系统中远程拍摄的新高光谱图像和水质数据库计算出的主要水质指数来预测未测得的病原微生物的浓度的; COPYRIGHT KIPO 2017

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