Abstract
Knowledge about the spectral variability in a field or region of interest becomes important when it comes to defining a representative spectrum of a certain spatial extent, used for example as an endmember in spectral unmixing techniques. An approach is presented using high spatial resolution panchromatic data to assess the spectral variability of a hyperspectral dataset. The spatial variability is combined with spectral variability using spatial statistics such as auto correlation and correlation length applied to the co-registered database of the two sources. Data fusion methods and analysis applied in this manner can help to support the interpretation of high resolution spectral and spatial data in order to support future applications such as precision farming.
Kneubühler, Mathias