DISCRIMINATION OF POORLY EXPOSED LITHOLOGIES IN IMAGING SPECTROMETER DATA

High spectral resolution imagery produced by imaging spectrometers enables the discrimination of geologic materials whose surface expression is subpixel in scale. Moreover, the use of such data makes it possible to distinguish materials which are characterized only by subtle differences in the spectral continuum. We define the ''continuum'' as the reflectance or radiance spanning the space between spectral features. The capability to distinguish subpixel targets will prove invaluable in studies of the geology of the Earth and planets from airborne and spaceborne imaging spectrometers. However, subpixel targets can only be uniquely identified in a truly optimal sense through the application of data reduction techniques that model the spectral contribution of both target and background materials. Two such techniques are utilized herein. They are a spectral mixture analysis approach and a low probability detection routine based on orthogonal subspace projection. These techniques were applied to the problem of detecting two different volcanic tuff units, one basaltic and one rhyolitic, in two different scenes of data measured by the airborne visible/infrared imaging spectrometer (AVIRIS). These tuff units have limited exposures from an overhead perspective and have spectral signatures which differ from those of background materials only in terms of subtle slope changes in the reflectance continuum. Of the two approaches, it was found that the low probability detection algorithm was more effective in highlighting those pixels that contained the target tuff units while suppressing the response of undesired background materials.