The impacts of invasive plant species on the environment and economy make effectively detecting and mapping them crucial. Using leaf spectral reflectance and the advantages of continuous wavelet analysis (CWA), we aimed to utilize the CWA and features reduction techniques (principal component analysis (PCA), regularized random forest (RRF), and guided regularized random forest (GRRF)) and two famous classifiers (random forest (RF) and support vector machine (SVM)) to discriminate between five invasive plant species. The sample used in the study consisted of 562 leaves representing five species (Senna uniflora, Hyptis suaveolens, Parthenium hysterophorus, Prosopis juliflora, and Xanthium strumarium), which were collected from two sites. Both spectra (smoothed and original) were analyzed using CWA with different scales. 120 models of feature reduction methods (PCA, RRF, and GRRF) were established, combined with two classifiers (RF and SVM) and then compared. 90% of the smoothed CWA models (54 models) showed improvements in the overall accuracy values [1.18%, 19.38%] as compared to the smoothed spectra models alone. 94% of the non-smoothed CWA models (54 models) showed improvements in the overall accuracy values [0.18%, 19.38%] as compared to the non-smoothed spectra models alone. The highest overall accuracy was achieved at 98.87% with a model of CWA at scale 16 by using the GRRF and SVM; whereas, the models of smoothed and non-smoothed spectra without CWA had overall accuracies of 90.22% and 89.87%, respectively.

Moreover, the models of CWA coupled with GRRF or RRF had better performance rates than the models of CWA with PCA. We concluded that the classification accuracy is improved when CWA with appropriate scales are used, and the feature selection process with the GRRF or RRF methods is also recommended for improving the classification performance.

外来入侵植物物种对环境和经济的影响使得有效地检测和绘制它们至关重要。利用叶片光谱反射率和连续小波分析（CWA）的优势，我们旨在利用CWA和特征减少技术（主要成分分析（PCA），正规化随机森林（RRF）和引导正规化随机森林（GRRF））和两个著名的分类器（随机森林（RF）和支持向量机（SVM））来区分五种入侵植物。研究中使用的样品由562种叶片组成，分别代表五种物种（番泻叶，Hyptis suaveolens，Parthenium hysterophorus，Prosopis juliflora和Xanthium strumarium），它们是从两个站点收集的。使用CWA以不同比例分析了两个光谱（平滑的和原始的）。建立了特征缩减方法（PCA，RRF和GRRF）的120个模型，并与两个分类器（RF和SVM）组合，然后进行了比较。与单独的平滑光谱模型相比，90％的平滑CWA模型（54个模型）显示出整体准确度值的改善[1.18％，19.38％]。与单独的非平滑光谱模型相比，94％的非平滑CWA模型（54个模型）显示出整体准确度值的改善[0.18％，19.38％]。通过使用GRRF和SVM，在等级16的CWA模型中，最高的整体精度达到98.87％。而没有CWA的平滑和非平滑光谱模型的总体精度分别为90.22％和89.87％。

而且，结合GRRF或RRF的CWA模型比具有PCA的CWA模型具有更好的性能。我们得出的结论是，使用具有适当比例的CWA可以提高分类精度，并且还建议使用GRRF或RRF方法进行特征选择过程以提高分类性能。