摘要
改进了小麦叶面积指数的可见/近红外光谱测定模型。以不同方法实现了小麦冠层反射光谱的预处理,并采用偏最小二乘回归算法(PLS)建立小麦叶面积指数估测模型对其进行比较分析,发现小波除噪结合一阶导数能最有效地消除原始光谱的噪声与背景信息,此时PLS模型校正集与预测集R2分别为0.849与0.835。为进一步优化模型,对经一阶导数结合小波除噪后的光谱采用主成分分析法(PCA)降维,以前4个主成分(含原始光谱84.867%特征信息)为输入变量,采用小二乘支撑向量机回归算法(LS-SVR)建立了小麦叶面积指数估测模型,其校正集与预测集R2分别达0.905与0.883,具有比PLS算法更高的精度。结果表明:以小波除噪结合一阶导数去除小麦冠层反射光谱中的土壤背景信息以提高模型精度是可行的,且LS-SVR是建模的优选方法。
The model to measure the leaf area index（LAI）of wheat with visible/near-infrared reflectance spectra is improved.The wheat canopy reflectance spectra is pretreated by different methods,and then the LAI estimation models are established by partial least square（PLS）algorithm to comparative analysis different pretreatments.It is found that the pretreatment method of wavelet denoising combined with first derivative can eliminate the noise and background information of the original spectra most effectively,with the calibration R-square 0.849 and prediction R-square 0.835,respectively.For optimizing the model,the pretreated spectra are analyzed using principal component analysis（PCA）,and the anterior 4 principal components,which accounted for 84.867% variation of the original spectral information,are used as the input variables to built the LAI estimation model by least square support vector regression（LS-SVR）algorithm.The calibration R-square and prediction R-square of LS-SVR model are 0.905 and 0.883,respectively,higher than that of PLS model,which indicates that the LS-SVR model is more accurate.The results suggest that it is feasible to improve the accuracy of the LAI estimation model by eliminating the soil background information of original spectra with the pretreatment method of wavelet denoising combined with first derivative,and the LS-SVR algorithm is a preferred method for model building.
出处
《激光与红外》
CAS
CSCD
北大核心
2010年第11期1205-1210,共6页
Laser & Infrared
基金
国家自然科学基金项目(No.30570279)
国家发展与计划委员会项目(No.A00300100584)
中南林业科技大学林业遥感信息工程研究中心开放性研究基金项目(No.RS2008k03)
中南大学拔尖博士研究生学位论文创新项目(No.1960-71131100007)
中南大学优秀博士论文扶持项目(No.2008yb024)资助
关键词
可见/近红外光谱
叶面积指数
小波除噪
导数
支撑向量回归
小麦
visible/near-infrared spectra
leaf area index（LAI）
wavelet denoising
differential coefficient
support vector regression（SVR）
wheat
