Discrimination of subtly different vegetative species via hyperspectral data

Abstract

The authors of this paper investigate the use of hyperspectral reflectance curves for the discrimination of cogangrass (Imperata cylindrica) from other subtly different vegetation species. Receiver operating characteristics (ROC) curves are used to determine which spectral bands should be considered as candidate features. Multivariate statistical analysis is then applied to the candidate features to determine the optimum subset of spectral bands. Linear discriminant analysis (LDA) is used to compute the optimum linear combination of the selected subset to be used as a feature for classification. Similarly, ROC analysis, multivariate statistical analysis, and LDA are utilized to determine the most advantageous wavelet-based scalar feature for classification. Nearest-neighbor classification results show that cogongrass can be detected with an accuracy of /spl ap/90%.