基于GA-SVM的耕地土壤重金属含量高光谱反演方法的研究

为提高耕地土壤重金属含量高光谱反演模型精度，以岳阳县某地区耕地土壤重金属铁（Fe）、砷（As）、铬（Cr）为例，提出了一种遗传算法（GA）优化支持向量机（SVM）的重金属含量反演模型。在对光谱进行SG平滑和10 nm重采样后，利用一阶/二阶微分、倒数对数和连续统去除光谱变换方法增强光谱特征，通过相关性分析筛选最优变换光谱，使用皮尔森相关系数与主成分分析提取各重金属光谱特征变量，分别建立SVM和GA-SVM土壤重金属高光谱反演模型并进行精度验证。结果表明，二阶微分变换光谱与各重金属含量相关性整体最突出；三种重金属在可见光波段490 nm、500 nm、510 nm和530 nm具有共同敏感特征；经GA算法优化SVM参数后，对比SVM回归模型，预测精度有明显提高，其重金属Fe、As和Cr的验证集R2分别为0.968、0.821和0.976；研究结果可为应用遥感技术反演耕地土壤重金属含量提供新的参考。

The Hyperspectral Inversion Method of Heavy Metal Contents in Cultivated Soils Based on GA-SVM

In order to improve the accuracy of hyperspectral inversion model for heavy metal content in cultivated soil, a genetic algorithm (GA) optimized support vector machine (SVM) was proposed to retrieve the heavy metal content of cultivated soil in a certain area of Yueyang County. After SG smoothing and 10 nm resampling, the first-order / second-order differential, reciprocal logarithm and continuum removal spectral transformation methods were used to enhance the spectral characteristics. The optimal transform spectra were selected by correlation analysis. Pearson correlation coefficient and principal component analysis were used to extract the spectral characteristic variables of heavy metals. SVM and GA-SVM were used to establish soil heavy metal hyperspectral inversion models and their accuracies were verified. The results showed that the correlation between the second-order differential transform spectra and the contents of heavy metals was the most prominent. The visible light bands of 490 nm, 500 nm, 510 nm and 530 nm were the most prominent compared with the SVM regression model. The prediction accuracy was significantly improved, and the verification set R2 values of Fe, As and Cr were 0.968, 0.821 and 0.976, respectively. The research results could provide a new reference for the application of remote sensing technology to retrieve the content of heavy metals in cultivated soil.