Spectral variability constraints on multispectral and hyperspectral mapping performance

Common approaches to multispectral imagery (MSI) and hyperspectral imagery (HSI) data analysis often utilize key image endmember spectra as proxies for ground measurements to classify imagery based on their spectral signatures. Most of these, however, take an average spectral signature approach and do not consider spectral variability. Multiple spectral measurements, whether from imagery data or utilizing a field spectrometer, demonstrate high variability linked not only to inherent material variability, but to acquisition parameters such as spatial and spectral resolution, and spectral mixing. This research explores causes and characteristics of spectral variability in remotely sensed data and its effect on spectral classification and mapping. WorldView-2 multispectral imagery and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral (HSI) data at similar spatial resolutions were corrected to reflectance using a model-based approach supplemented by field spectral measurements. A second AVIRIS dataset at lower spatial resolution was also used. These data were then analyzed using ground and image spectral endmember spectra. Endmember spectra were assessed in terms of their spectral variability, and statistical and spectral-feature-based classification approaches were tested and compared. Results illustrate that improved mapping can be achieved when spectral variability of individual endmembers is taken into account in the classification.