湖泊科学2011，Vol.23Issue(3)：357-365,9.
基于偏最小二乘法的巢湖悬浮物浓度反演
Inversion of suspended matter concentration in Lake Chaohu based on Partial Least- souares Regression
摘要
Abstract
Suspended mater concentration is an important parameter of water quality evaluation.Hyperspeetral data measured in Lake Chaohu in June, 2009 were processed by wavelet transform in order to remove data redundancy and reduce modeling time.Three evaluation indexes were selected considering the effect of different wavelet functions and decomposed scales on the data compression,and the wavelet function Db4 and decomposed scale 4 were determined finally.The original hyperspectral data of 451 bands were compressed to 34 feature variables by the wavelet transform.Then 20 samples were used to construct Partial Leastsquares Regression (PLS) inversion model of suspended matter concentration, and other 9 samples were used for model verification.The results show that PLS model is suited when the number of principal components is 3 and its R2 is 0.93, R2 (pred) is 0.89 and PRESS is 3.29.These three principal components explain 98.60％ of independent variables information and 92.37％ of dependent variables information.PLS model with R2 of 0.93, RMSE of 4.77mg/L, and MAPE of 9.02％ can make full use of the information of hyperspectral data, and hence have higher accuracy and stability.In addition, single band model, spectral one-order differential model and band ratio model were used to compare with PLS model The results show that PLS model is better than traditional empirical models no matter on the accuracy of modeling samples or the error of validation samples, indicating that it is suitable for the inversion of suspended matter concentration by using hyperspectral data.关键词
小波变换/偏最小二乘法/高光谱数据/悬浮物/巢湖Key words
Wavelet transform/ Partial Least-squares Regression/ hyperspectral data/ suspended matter/ Lake Chaohu引用本文复制引用
刘忠华,李云梅,吕恒,徐袆凡,徐昕,黄家柱,檀静,郭宇龙..基于偏最小二乘法的巢湖悬浮物浓度反演[J].湖泊科学,2011,23(3):357-365,9.基金项目
高分辨率对地观测系统国家科技重大专项项目(E0203/1112/JC01)、国家自然科学基金项目(40971215)、江苏省2008年度普通高校研究生科研创新计划(CX09B-301Z)和南京师范大学优秀博士论文培养计划项目(12432116011036)联合资助. （E0203/1112/JC01）
