extraction procedures successfully produced 
continuum-removed spectra that show this offset. 
Spot checking of the imaging spectrometer data for 
areas of known mineralogy shows a good match 
between extracted absorption features, laboratory 
measurements, and the expert system's automated 
identification 
The final step in the analysis of the AVIRIS 
data was to map the spatial distribution of the 
minerals using the expert system. Figure 8 outlines 
the actual procedures used to implement the single 
spectrum analysis on an entire imaging spectrometer 
cube. The analysis was segmented, with production 
of several intermediate image cubes to allow 
evaluation of each step. Interactive viewing of the 
derived cubes showed that each contained valuable 
C. Step 3. Binary encoding of library and image 
cube. 
CUBE OF 
BINARY 
MATCHES 
information. The final image cube containing the 
automated mineral identifications and probabilities 
appears to match known mineralogy fairly well, 
however, its overall accuracy is still being evaluated. 
D. Step 4. Expert system analysis. 
BAND PARAMETERS 
push ion 
DEPTH 
WIDTH 
ASYMMETRY 
g[f 1QNS 
MINERAL B 
MINERAL C 
REFLECTANCE 
CUBE 
CONTINUUM 
REMOVED 
CUBE 
Figure 8. 
Procedures for analysis of Imaging 
Spectrometer data. 
CUBE OF 
ABSORPTION — 
BAND 
EXPERT 
A. Step 1 
Continuum removal. 
PARAMETERS 
ANALYSIS 
MINERAL Z 
VEGETATION 
50 nm 
^0.40 |im 
INFORMATION 
CUBE 
PROBABILITIES 
NEARLY CERTAIN 
VERY HIGH 
HIGH 
MEDIUM HIGH 
MEDIUM 
MEDIUM LOW 
LOW 
VERY LOW 
NOT LIKELY 
CONCLUSIONS 
An expert system has been developed that 
allows automated identification of Earth surface 
materials based on their spectral characteristics in 
imaging spectrometer data. Automated, techniques 
have been developed for the extraction and 
characterization of absorption features by analyzing a 
suite of laboratory spectra of some of the most 
common minerals, vegetation types, and vegetation 
components. Critical absorption band characteristics 
for identification have been defined and these have 
B. Step 2 Feature extraction and determination of 
absorption band parameters. 
BAND PARAMETERS 
POSITION 
BAND 
PARAMETERS 
been used to develop facts and rules defining a 
generalized knowledge base for analysis of reflectance 
spectra. 
A tree hierarchy is being used to implement 
the facts and rules in a logical fashion that allows the 
computer to make decisions similar to those that 
would be made by an experienced analyst. Each 
spectrum is broadly classified based on its strongest 
absorption features. Primary band characteristics and 
secondary/tertiary absorption bands are used to 
progress through the tree until an identification is 
made. If the decision process fails at any point 
because there is insufficient information to identify a 
specific material, then the last classification is used to 
give the best possible answer. 
The feature extraction procedures and the 
expert system have been successfully used to analyze 
lab and field spectra of unknown materials and