LiDAR-guided analysis of airborne hyperspectral data

Abstract

This paper describes a new framework to the collection and fusion of multisensor airborne LiDAR and hyperspectral data. We describe a data fusion philosophy that provides a spatially precise positioning of hyperspectral data based on discrete first and last return LiDAR data. Three dimensional objects defined by the LiDAR data are then used to sample optimal spectra for subsequent analysis. The sampled spectra retain their positioning metadata and so can be mapped back into geographic space for further analysis. While the paper presents this philosophy within the context of a species classification, other analytical analysis can be performed.