An Assessment Of Image Classifiers For Generating Machine-Learning Training Samples For Mapping The Invasive Campuloclinium Macrocephalum (Less.) Dc (Pompom Weed) Using Desis Hyperspectral Imagery
Machine-learning algorithms may require large numbers of reference samples to train depending on the spatial and spectral heterogeneity of the mapping area. Acquiring these reference samples using traditional field data collection methods is a challenge due to time constraints, logistical limitations, and terrain inaccessibility. The aim of study was to assess how parametric, nonparametric, and spectral matching image classifiers can be used to generate a large number of accurate training samples from minimal ground control points to train machine-learning algorithms for mapping the invasive pompom weed using 30 m DESIS hyperspectral data. Three image classifiers, namely, maximum likelihood classifier (MLC), support vector machine (SVM) and spectral angle mapper (SAM) were selected to represent each of the three types of image classifiers under investigation in this study. Results show that the SAM, MLC and SVM classifiers had pixel-based classification accuracies of 87%, 73% and 67% for the pompom-containing pixels class, respectively. Furthermore, an independent field verification for the SAM classification was conducted yielding a 92% overall mapping accuracy for the pompom-containing pixels class. A total of 4000 pompom-containing and 8000 non-pompom-containing training samples were generated from an SAM classification that was trained using only 20 endmembers. Overall, this study presents a potential solution strategy that has significant implications for generating large numbers of reference training samples for mapping invasive alien plants from new generation spaceborne hyperspectral imagery using machine-learning algorithms.