支持向量机在土壤镁含量高光谱估算中的应用

Application of Support Vector Machine on Soil Magnesium Content Estimation Based on Hyper-Spectra

研究利用土壤样本实验反射光谱,分析了土壤镁(Mg)含量与土壤反射光谱的关系,比较了主成分回归分析(PCR)、偏最小二乘回归分析(PLSR)和支持向量机回归分析(SVMR)等方法,以及土壤反射光谱及其变换光谱与土壤Mg含量之间的估算模型,为土壤Mg含量高光谱估算提供依据。结果表明:PCR、PLSR、SVMR 3种建模方法在Mg含量的估算中,SVMR的估算精度相对较高,估算精度平均达到80.96%,分别比PCR和PLSR提高了6.16%、4.20%;对于不同的数学变换处理方法,一阶微分变换相对较好,估算精度平均为80.76%,分别比反射率、倒数对数变换提高了4.95%、4.61%。因此,运用土壤反射光谱一阶微分变换的SVMR进行建模,可以相对较好地估算全Mg含量,精度达84.04%。

Based on the measured reflectance spectra of soil samples in the laboratory, the relationship between soil magnesium content and reflectance spectra was analyzed, and the methods of principal component regression （PCR） and partial least squares regression （PLSR） were compared with support vector machine regression （SVMR） analysis for soil magnesium content estimation, then the estimation models of magnesium content between the reflectance spectra and its transforms were established to provide the basis for soil magnesium content estimation by hyper-spectra. The results showed that the average prediction accuracy of SVMR model reached 80.96%, higher than PCR and PLSR models by 6.16% and 4.20%, respectively. The first derivative of the reflectance spectra obtained the best outcome in the different mathematical transforms with an average estimation accuracy of 80.76%, higher by 4.95% and 4.61% than reflectivity and reciprocal logarithmic transforms, respectively. Therefore, SVMR model of first derivative transform was optimal to estimate the total magnesium content with an accuracy of 84.04%.