the composition of the Earth's crust and the 
interaction of complex ecosystems. 
Because of the large volume of data that will 
be generated by imaging spectrometers, development 
of automated data reduction and analysis capabilities 
is required to allow extraction of useful information. 
NASA's operational aircraft system, the 224 channel 
Airborne Visible/Infrared Imaging Spectrometer 
(AVIRIS) (Porter and Enmark, 1987) is being used to 
develop prototype systems for automated analysis of 
HIRIS data (Kruse et al., 1988; 1990a). 
SPECTRUM ANALYSIS 
Feature Extraction and Characterization 
Numerical analysis and characterization of 
digital reflectance measurements were used to 
establish quantitative criteria for identifying 
minerals, mineral mixtures, and vegetation. The 
absorption feature information was extracted from 
each spectrum using the following automated 
techniques (Kruse et al., 1988; 1990a). 
1) . A continuum was defined for each spectrum by 
finding the high points (local maxima) and 
fitting straight line segments between these 
points. Figure 2a shows a fitted continuum for a 
laboratory calcite spectrum. 
2) . The continuum was divided into the original 
spectrum to normalize the absorption bands to a 
common reference (Figure 2b). 
3) . The minima of the continuum-removed 
spectrum were determined and the 10 strongest 
absorption features extracted. 
4) . The wavelength position, depth, full width at 
half the maximum depth (FWHM), and 
asymmetry for each of these 10 features were 
determined and tabulated (Figures 2b, 2c). 
Figure 2a. Laboratory calcite spectrum showing 
fitted continuum. 
Figure 2b. 
Continuum removed calcite spectrum 
showing the absorption band 
parameters: 1) band position, 2) band 
depth, and 3) full width at half 
maximum (FWHM). 
If more than one minimum occurred between 
two high points then the band was defined as a 
multiple band. Band "order" indicates the number of 
bands in the multiple band and the relative depth of 
the band compared with the other members of the 
multiple band. The asymmetry was defined as the 
sum of the reflectance values for feature channels to 
the right of the minimum divided by the sum of the 
reflectance values for feature channels to the left 
(Figure 2c). Symmetrical bands have an asymmetry 
value of one. Bands that are asymmetrical towards 
shorter wavelengths have asymmetry less than one, 
while bands that are asymmetrical towards longer 
wavelengths have asymmetry greater than one. 
Figure 2c. Schematic diagram showing absorption 
band asymmetry. 
Hierarchical Tree 
Spectral features were digitally extracted at AVIRIS 
resolution (10 nm) for a suite of 17 common 
minerals and both green and dry vegetation. The 
five parameters derived using the feature extraction 
procedure were used in conjunction with published 
spectral information to determine the critical 
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