A statistical challenge to analyse hyperspectral data is the multicollinearity between spectral bands. Partial least squares (PLS) has been extensively used as a dimensionality reduction technique through constructing lower dimensional latent variables from the spectral bands that correlate with the response variables. However, it does not take into account the grouping structure of the full spectrum where spectral subsets may exhibit distinct relationships with the response variables. We propose a two-step group penalized PLS regression approach by performing a PLS regression on each group of predictors identified from a clustering approach in the first step. In the second step, a group penalty is imposed on the latent components to select the group with the highest predictive power. Our proposed method demonstrated a superior prediction performance, higher R-squared value and faster computation time over other PLS variations when applied to simulations and a real-world observational data set. Interpretations of the model performance are illustrated using the real-world data example of leaf spectra to indirectly quantify leaf traits. The method is implemented in an R package called “groupPLS”, which is accessible from github.com/jialiwang1211/groupPLS.

分析高光谱数据的统计挑战是光谱带之间的多重共线性。通过从与响应变量相关的光谱带中构建较低维的潜在变量，偏最小二乘（PLS）已被广泛用作降维技术。但是，它没有考虑整个光谱的分组结构，其中光谱子集可能表现出与响应变量的明显关系。通过对第一步中从聚类方法中识别出的每组预测变量执行PLS回归，我们提出了两步分组惩罚PLS回归方法。在第二步中，对潜在组件施加组惩罚以选择具有最高预测能力的组。当应用于模拟和现实世界的观测数据集时，我们提出的方法与其他PLS变量相比，具有更好的预测性能，更高的R平方值和更快的计算时间。使用叶光谱的实际数据示例间接量化叶性状来说明模型性能的解释。该方法在名为“groupPLS ”，可从github.com/jialiwang1211/groupPLS进行访问。