Regional mapping of planetary surfaces with imaging spectroscopy

We present a method to determine spectral differences and compositional variability of planetary surfaces when remotely sensed by means of imaging spectroscopy instrumentation. The quantity frequently measured in remote sensing of planetary bodies in the 0.3-2.5 μm spectral range is the reflectance spectrum and it is used to study the mineralogic composition of the surface being sensed. Very often, however, this quantity is difficult to measure owing to lack of knowledge either of atmospheric extinction or of analogous solar spectra. The method we describe allows all the regions in a surface having spectral similarity to be identified and does not require the reflectance spectrum to be measured, taking full advantage of imaging spectroscopy and classification methods. In practice, all the spectra of the image are divided by the spectrum of a pixel (or an average spectrum) taken in the image itself. In this way, both the instrument and the atmospheric transfer functions are eliminated and it is possible to compare the spectra with one another. These new relative spectra are then classified using a clustering algorithm in order to recognize spectral similarities. The final result is a map showing all the regions in the image having similar spectra which can be linked to the mineralogical composition of the surface under study. The method has been applied to Earth-based observations of the Moon but can be equally used with other high spatial imaging spectroscopy data provided by future interplanetary missions.