Self-Organizing ANNs for Planetary Surfarce Composition Research

摘要

The mineralogic composition of planetary surfaces is often mapped from remotely sensed spectral images. Advanced hyperspectral sensors today provide more detailed and more voluminous measurements than traditional classification algorithms can efficiently exploit. ANNs, and specifically Self-Organizing Maps, have been used at the Lunar and Planetary Laboratory, University of Arizona, to address these challenges.