Spectral band discrimination for species observed from hyperspectral remote sensing

Abstract

In vegetation spectroscopy, compositional information of leaves contained at band level or across the electromagnetic spectrum (EMS) and parts thereof, plays a huge rule in the analysis of spectra and their relations to the reflectance patterns across the spectrum. Spectral matching is often achieved by means of matching algorithms such as the Spectral Angle Mapper (SAM), Spectral information divergence (SID) and mixed measures of SAM and SID using either the tangent or the sine trigonometric functions, SID(TAN) or SID(SIN). The performance of these measures in distinguishing between objects of interest, such as species, is often compared using the relative spectral discriminatory probability (RSDPB). In this study, these measures are used to assess whether various sets of bands including the full spectrum, the visible (VIS), the near infrared (NIR), the shortwave infra-red (SWIR) region, as well as sets of bands identified by the stepwise discriminant analysis (SDA), can be used to discriminate the different species. This is done to identify the important regions of the EMS to distinguish seven common savannah tree species observed in the Kruger National Park, South Africa's largest game reserve. The magnitude of variation of the species in any part of the spectrum can be linked to the importance of that spectral region in distinguishing the species. In addition, classification accuracy of these sets of bands was assessed and the SDA bands often gave better classification accuracy compared to using all bands, bands in the NIR, and SWIR parts of the EMS.