针对土壤有机质含量高光谱估测研究中变量维数过高与特征谱段筛选问题, 提出了一种结合随机森林和自适应搜索算法的混合特征选择方法。 首先依据随机森林变量重要性原理获取初始优化集, 然后利用遗传二进制粒子群封装算法对初始优化集进一步自适应筛选。 对于土壤有机质含量估测建模问题, 选择稳健性强且能有效处理高维变量的随机森林算法。 以典型黑土区采集的土壤样品为研究对象, 将ASD光谱仪获取的可见光-近红外区间光谱数据和经化学分析得到的土壤有机质含量为数据源, 对原始光谱进行光谱变换和重采样处理后, 采用随机森林-遗传二进制粒子群混合选择方法提取特征光谱区间, 构建有机质含量随机森林估测模型。 与利用全光谱、 随机森林方法筛选的光谱和自适应搜索算法筛选的光谱构建随机森林模型得到的预测精度进行比较。 结果表明, 利用随机森林-遗传二进制粒子群混合特征选择算法筛选的波谱变量参与随机森林建模, 预测决定系数, 均方根误差和相对分析误差分别为0.838, 0.54%, 2.534。 该方案应用最少的变量个数获得最高的预测精度, 能够较高效地估测黑土有机质含量, 也能为其他类型土壤在有机质含量估测研究的变量筛选与建模问题上提供参考。

To solve the problem of high-dimensional variables and characteristic wavelengths selection on soil organic matter content estimation using hyperspectral data, a hybrid feature selection method that combined random forest and self-adaptive searching method was proposed. In this hybrid method, random forest was employed to select spectral variables as the preliminary optimal dataset, which had great importance in the modeling process. The wrapper approach which combined genetic algorithm and binary particle swarm optimization was used as the self-adaptive searching algorithm to further search variables in the preliminary dataset. As for the prediction model, random forest was picked on because of the strong robustness and the excellent performance of dealing with high-dimensional variables. In this paper, the soil samples collected in the typical black soil region were used as the research object, and the Vis-NIR spectral data of the soil obtained from ASD spectrometer and the organic matter content through chemical analysis were used as the data sources. Following reflectance transformation and spectral resampling, the proposed hybrid selection method was employed to extract the characteristic spectral regions that were used as the input data for random forest. The prediction accuracy was compared with the results from random forest algorithm with the spectral datasets which were respectively extracted by no-selected method, only random forest method and only self-adaptive searching method. The results showed that using random forest model with the characteristic wavelengths extracted by proposed method obtained the highest predicted accuracy, in which the R2, RMSE and the RPD were 0.838, 0.54% and 2.534, respectively. Moreover, the proposed method was more efficient to selected features than other approaches. It can be concluded that the hybrid feature selection method and random forest algorithm can be effectively applied to black soil organic matter content estimation using hyperspectral data and it also provides a reference for solving the problem of variables selection and modeling on other types of soil organic matter content estimation.