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Imaging Spectrometry: Spectral Resolution And Analytical Identification Of Spectral Features

Spaceborne imaging spectrometers such as the High Resolution Imaging Spectrometer (HIRIS) are presently under development. Major trade-offs in weight, cost, and performance are related to the spatial, spectral, and radiometric resolution. In this paper, we have investigated the relationship between spectral and radiometric resolution by applying maximum likelihood classification and binary vector analysis to a set of model data. The model data consist of Gaussian spectral absorption features to which various levels of random noise have been applied. The results show that if the rapid, binary vector analysis techniques are to be used, feature depth-to-noise ratios of 10 or greater and Nyquist sampling are required to achieve acceptable classification errors. This translates into a NEAp of 0.5% for a 5% feature depth.