研究基于支持向量机构建水稻镉（Cd）含量高光谱预测模型的可行性. 利用ASD光谱仪测量研究区水稻冠层反射光谱，通过实验室化学分析得到土壤镉含量和水稻叶片镉含量，对研究区水稻光谱进行均一化平滑处理以及小波变换降噪，构建基于支持向量机（support vector machines, SVM）的水稻镉含量高光谱预测模型. 结果表明，小波变换降噪处理对SVM建立的镉含量预测模型精度有所提高，SVM高光谱预测模型的相关系数为0.867 4, 均方误差为0.001 2. 该研究为利用高光谱遥感大面积、快速监测农田作物重金属污染提供技术支持.

Research was carried out to explore possibility of using support vector machines (SVM) to estimate Cd concentration from hyperspectral reflectance. Canopy spectral measurements from rice plants were collected using an ASD field spectrometer in the experiment sites. Soil samples and rice samples were collected
for chemical analysis of Cd concentrations. A normalization spectral pre-processing method was employed to improve performance of the estimation model. Wavelet transforms were adopted to denoise the rice hyperspectral. Estimation of Cd concentration was achieved by an SVM approach. Compared to the original
(undenoised) hyperspectrl estimation model, the SVM model based on wavelet transforms yielded promising results with a coefficient of determination of 0.867 4 and a mean square error (MSE) of 0.001 2. The results indicate that it is possible to estimate Cd concentration in rice using wavelet transforms and SVM. This study can provide technical support for large area monitoring of heavy metals stressed crops using hyperspectral remote sensing.