Journal
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Volume 8, Issue 4, Pages 60-88Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MGRS.2020.2979764
Keywords
Training data; Hyperspectral imaging; Feature extraction; Machine learning; Data mining
Funding
- Alexander von Humboldt research grant
- AXA Research Fund
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Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called curse of dimensionality) for accurate analysis of HSIs.
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