[1]丛茂勤,罗 宏,韦玉春.基于主成分方法的浑浊水体组分的光谱探测分析[J].南京师范大学学报(工程技术版),2019,19(02):068.[doi:10.3969/j.issn.1672-1292.2019.02.009]
　Cong Maoqin,Luo Hong,Wei Yuchun.Spectral Detecting of Water Components in the TurbidWater Body by Principle Component Analysis[J].Journal of Nanjing Normal University(Engineering and Technology),2019,19(02):068.[doi:10.3969/j.issn.1672-1292.2019.02.009]
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基于主成分方法的浑浊水体组分的光谱探测分析
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
19卷
期数:
2019年02期
页码:
068
栏目:
计算机与信息工程
出版日期:
2019-06-30

文章信息/Info

Title:
Spectral Detecting of Water Components in the TurbidWater Body by Principle Component Analysis
文章编号:
1672-1292(2019)02-0068-07
作者:
丛茂勤; 罗 宏; 韦玉春
南京师范大学地理科学学院,江苏 南京 210023
Author(s):
Cong Maoqin; Luo Hong; Wei Yuchun
School of Geography,Nanjing Normal University,Nanjing 210023,China
关键词:
主成分分析; 浑浊水体; 组分识别; 奇异值分解; 非线性迭代偏最小二乘法
Keywords:
principle component analysis; turbid water; component identification; singular value decomposition; nonlinear iterative partial least squares
分类号:
X131.2
DOI:
10.3969/j.issn.1672-1292.2019.02.009
文献标志码:
A
摘要:
浑浊水体是水色遥感的重要研究对象,其光谱特征由浮游植物(叶绿素a含量)、无机悬浮物和可溶性有机物3个组分控制,利用光谱识别浑浊水体的组分信息对于光谱解混和组分的定量反演具有重要意义. 基于2010—2016多年太湖水体野外测量的数据,以水体光谱数据的协方差矩阵和相关矩阵为输入进行主成分分析,对比特征值分解(EVD)、奇异值分解(SVD)、非线性迭代偏最小二乘法(NIPALS)3种求解方法以及白化后处理对水体组分的识别作用. 结果表明,基于相关矩阵的SVD方法对浑浊水体组分的识别效果优于其他算法,获得的前3个主成分载荷累计贡献率为98.8%,依次代表了水、叶绿素a组分和悬浮泥沙组分信息; 白化后处理没有明显的优化作用.
Abstract:
The turbid water is an important research object in water color remote sensing,whose spectral characteristics are determined by the phytoplankton(chlorophyll-a concentration),inorganic suspended matter and dissolved organic matter. Using spectrum data collected from field measurement in Taihu Lake,the paper compares the performance of identifying the components from turbid water spectrum among three methods,eigenvalue decomposition(EVD),singular value decomposition(SVD),nonlinear iterative partial least squares(NIPALS),and whitening post-processing,taking the covariance matrix and the correlation matrix as input. The results shows that SVD with input of correlation matrix presents the better performance,that the first three principal component loadings of SVD have 98.8% cumulative contribution rate,and represents component information about the water,chlorophyll-a dominant and suspends sediment dominant,respectively,while the whitening does not show an obvious effect.

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备注/Memo

备注/Memo:
收稿日期:2019-06-19.
基金项目:国家自然科学基金(41471283).
通讯联系人:韦玉春,博士,教授,博士生导师,研究方向:环境遥感. E-mail:weiyuchun@njnu.edu.cn
更新日期/Last Update: 2019-06-30