叶绿素a浓度反演模型:堆栈自编码器粒子群优化BP神经网络

Chlorophyll-a Concentration Inversion Model:Stacked Auto-encoder Particle Swarm Optimization BP Neural Network

内陆水体叶绿素a浓度是衡量水体富营养化程度的主要指标,是影响水体反射率光谱特征的重要因素之一。本文以白洋淀烧车淀、圈头乡各村庄等水域为研究区,采集了高光谱数据和水样,并在实验室测定叶绿素a等水质参数,应用于白洋淀区域的叶绿素a高光谱遥感反演。针对线性降维方法特征提取能力不足和神经网络构建叶绿素a遥感反演模型时学习效率低、泛化能力差的问题,提出了堆栈自编码器粒子群优化BP神经网络模型。该模型利用堆栈自编码器强大的非线性变换能力,通过最小化重构误差来学习高光谱数据特征,在实现数据降维的同时最大程度保留原始光谱数据中的水体辐射信息,提取出实测水体光谱的深度特征,将BP神经网络初始权重作为粒子的位置向量,通过粒子群算法搜寻网络初始权重的最优值,降低出现局部极值的概率,提高模型的稳定性和反演的精确度。堆栈自编码器粒子群优化BP神经网络模型(R2=0.82,RMSE=2.65μg/L,MAE=1.89μg/L)相较于对高光谱数据不降维的BP神经网络模型(R2=0.75,RMSE=3.16μg/L,MAE=2.39μg/L)、基于主成分分析法降维的BP神经网络模型(R2=0.79,RMSE=2.85μg/L,MAE=2.29μg/L)和基于逐步回归分析法降维的BP神经网络模型(R2=0.80,RMSE=2.79μg/L,MAE=2.38μg/L)反演结果相比,堆栈自编码器粒子群优化BP神经网络模型对内陆水体叶绿素a高光谱遥感反演具有较高的精度,为内陆二类水体叶绿素a高光谱遥感反演提供一定的理论依据和技术支持,助力白洋淀水质持续监测,也为以后高光谱卫星遥感影像反演叶绿素a提供新思路。

The concentration of Chlorophyll-a(Chl-a)has been the main indicator of eutrophication of inland waters and one of the important factors affecting the spectral characteristics of the reflectance of water.Monitoring the concentration of Chl-a in inland water bodies can provide valuable information for managing and mitigating the effects of eutrophication.In this study,hyperspectral data and water samples were collected from Baiyangdian Lake and villages in Baotou County,and water quality parameters such as Chl-a were determined in the laboratory,which were applied to Chl-a hyperspectral remote sensing inversion in Baiyangdian region.The stacked auto-encoder particle swarm optimization BP neural network model,the BP neural network model of hyperspectral data without dimensionality reduction,the BP neural network model of dimensionality reduction based on principal component analysis,and the BP neural network model of dimensionality reduction based on stepwise regression analysis were respectively established.To solve the problems of insufficient feature extraction ability of linear dimension reduction method and low learning efficiency and poor generalization ability of Chl-a hyperspectral remote sensing inversion model constructed by neural network,an inversion model of Chl-a concentration was proposed based on stacked auto-encoder particle swarm optimization BP neural network.This model used the powerful nonlinear transformation ability of stacked auto-encoder to learn the features of hyperspectral data by minimizing the reconstruction error.It achieved the dimensionality reduction of data while preserving the radiation information of the original spectral data to the greatest extent,and extracted the depth features of the measured water spectrum.The initial weight of BP neural network was taken as the position vector of the particle.Particle swarm optimization algorithm was used to search for the optimal initial weight of the network,reduce the probability of local extreme value,and improve the stability of the model and the accuracy of inversion.Compared to the BP neural network model without dimensionality reduction of hyperspectral data(R2=0.75,RMSE=3.16μg/L,MAE=2.39μg/L),the BP neural network model based on principal component analysis for dimensionality reduction(R2=0.79,RMSE=2.85μg/L,MAE=2.29μg/L),and the BP neural network model based on stepwise regression analysis for dimensionality reduction(R2=0.80,RMSE=2.79μg/L,MAE=2.38μg/L),the stacked auto-encoder particle swarm optimization BP neural network model(R2=0.82,RMSE=2.65μg/L,MAE=1.89μg/L)had higher accuracy in hyperspectral remote sensing inversion of Chl-a in inland water bodies.This study provides a theoretical basis and technical support for hyperspectral remote sensing inversion of Chl-a in inland Class II water bodies,helps with continuous monitoring of water quality in Baiyangdian Lake,and provides new ideas for future hyperspectral satellite remote sensing image inversion of Chl-a.