基于SPA-BPNN的成都市天府新区东部土壤As含量高光谱估测建模  

Modeling of soil As content hyperspectral estimation based on SPA-BPNN in east of Tianfu New District,Chengdu

作　　者：张宇 简季[1] ZHANG Yu;JIAN Ji(College of Earth Science,Chengdu University of Technology,Chengdu 610059,China)

机构地区：[1]成都理工大学地球科学学院,成都610059

出　　处：《桂林理工大学学报》2024年第1期58-66,共9页Journal of Guilin University of Technology

基　　金：国家自然科学基金项目(41771444)。

摘　　要：为明确成都平原城市边缘带土壤中重金属As含量,以四川省成都市天府新区东部为研究区,对土壤原始高光谱数据进行一阶微分(FD)、二阶微分(SD)、去包络线(CR)和标准正态变换(SNV)处理,利用皮尔逊相关系数(PCC)和连续投影算法(SPA)筛选出最佳变换光谱的特征波段,分别建立偏最小二乘(PLSR)、极限学习机(ELM)、随机森林(RF)和BP神经网络(BPNN)4种回归模型,利用高光谱数据进行土壤重金属As含量估测并进行精度验证。结果表明,经去包络线一阶微分(CR-FD)变换的光谱与土壤重金属As含量相关性显著提升,由0.473提高到0.848;无论是基于PCC还是SPA算法筛选出的特征波段,非线性模型的拟合度以及预测精度均高于线性模型;相对于PCC算法,利用SPA算法筛选的特征波段建立的模型预测精度明显提升,PLSR、ELM、RF、BPNN模型验证集的决定系数(R2)分别为0.786、0.847、0.856、0.942。因此,以SPA算法筛选出的光谱波段作为自变量构建的BPNN模型(SPA-BPNN)是研究区内As含量的最优估测模型。The problem of soil pollution by heavy metals has become increasingly severe,which is threatening the ecological environment and human health.In order to clarify the heavy metal As in soils in the urban fringe zone of Chengdu plain,this study takes the east of Tianfu New District in Chengdu,Sichuan as the research ob-ject to perform the soil raw spectral data by first-order differential(FD),second-order differential(SD),de-en-velope processing(CR),standard normal transform(SNV).The Pearson correlation coefficient(PCC)and suc-cessive projections algorithm(SPA)are used to filter the characteristic bands of the best transformed spectra.Four regression models,namely,partial least squares(PLSR),extreme learning machine(ELM),random for-est(RF)and BP neural network(BPNN),are developed for the estimation of soil heavy metal content based on the hyperspectral data,and then validate the accuracy.The results show that the correlation between the spectra and the As content of soil is significantly improved by de-enveloping first-order differential(CR-FD)transforma-tion,from 0.473 to 0.848;the non-linear model is higher than the linear model in terms of model fit and pre-diction model accuracy,whether based on the PCC or SPA algorithm to extract the feature bands.Compared with the modelling results based on the PCC algorithm,the prediction accuracy of the models built with the fea-ture variables filtered by the SPA algorithm is significantly improved,with R2 of 0.786,0.847,0.856 and 0.942 for the PLSR,ELM,RE and BPNN model validation sets respectively.The BPNN model,which is con-structed using the spectral bands selected by the SPA as the independent variables,gives the best results for the estimation of the heavy metal content in the study area.

关 键 词：土壤重金属 砷含量 高光谱 光谱变换 特征波段 估测模型对比 成都市 

分 类 号：P407.8[天文地球—大气科学及气象学] TP751.1[自动化与计算机技术—检测技术与自动化装置]