Comparison of spectral matching techniques for vegetation species delineation of the National Arboretum

Identification of differing vegetation species has been a lauded ability of hyperspectral imagery and analysis but continues to be a challenging problem. Hyperspectral imagery has been used for years in applications such as vegetation analysis and delineation, terrain categorization, explosive mine detection, environmental impacts and effects, and agriculture and crop evaluation. Unlike applications which focus on detection of specific targets with constant spectral signatures, vegetation signatures continually vary across their growth cycle. In order to identify various vegetation species, either large collections of time-varying reference signatures are required, or ground truth/training data is needed. These are not always viable options and in many cases only in-scene data can be used. In this study we compare the performance of various spectral matching methods in separating vegetation at the species level. Parametric, non-parametric, derivative techniques, and other methods are compared. These methods are applied to a complex scene, the National Arboretum in Washington DC, which was imaged by an airborne hyperspectral sensor in August, 2008. This survey assesses performance of spectral matching methods for vegetation species delineation and makes recommendations for its application in hyperspectral data analysis.