Toward a maximally effective means for analysis of hyperspectral data

In this paper we describe efforts toward a hyperspectral land remote sensing data analysis procedure that would be maximally effective for use by a broad community of future users. Though there would be dependence of performance achieved on the spectral subspace and the classification algorithm used, the major dependence is on how well the user quantitatively defines the classes desired. Thus the attempt is to measure this dependence for typical users and to introduce means that mitigate the problem of class and training definition.