Abstract
The NASA image-based geological expert system was applied to analyze remotely sensed hyperspectral image data. The major objective is for geologists to identify the earth surface mineral properties directly from the airborne and spaceborne imaging spectrometer data. With certain constraints, we showed that the system can identify correctly different classes of mineral. It has the built-in learning paradigm to enhance the confidence factor of mineral identification. We also incorporated a very powerful natural language system as the user-friendly front end and tested the concurrent processing efficiency of our frame-based knowledge representation in the hypercube microsupercomputer simulation.
© 1985 Optical Society of America
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