TY - GEN
T1 - Feature Extraction for Hyperspectral Imagery
T2 - The Evolution from Shallow to Deep: Overview and Toolbox
AU - Rasti, Behnood
AU - Hong, Danfeng
AU - Hang, Renlong
AU - Ghamisi, Pedram
AU - Kang, Xudong
AU - Chanussot, Jocelyn
AU - Benediktsson, Jon Atli
N1 - Funding Information: We would like to thank Prof. Melba Crawford for providing the Indian Pines 2010 Data and the National Center for Airborne Laser Mapping, the Hyperspectral Image Analysis Laboratory at the University of Houston, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee. This work is partially supported by an Alexander von Humboldt research grant. We also would like to thank the AXA Research Fund for supporting the work of Prof. Joc-elyn Chanussot and the corresponding author of this paper, Dr. Danfeng Hong. Publisher Copyright: © 2013 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85084241070
U2 - 10.1109/MGRS.2020.2979764
DO - 10.1109/MGRS.2020.2979764
M3 - Article
SN - 2473-2397
VL - 8
SP - 60
EP - 88
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
ER -