Improving the classification of invasive plant species by using continuous wavelet analysis and feature reduction techniques

Abstract

Abstract 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.