期刊
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
卷 8, 期 4, 页码 60-88出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MGRS.2020.2979764
关键词
Training data; Hyperspectral imaging; Feature extraction; Machine learning; Data mining
资金
- Alexander von Humboldt research grant
- AXA Research Fund
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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