TY - JOUR
T1 - Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification
T2 - A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques
AU - Li, Wei
AU - Feng, Fubiao
AU - Li, Hengchao
AU - Du, Qian
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/3
Y1 - 2018/3
N2 - Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral signatures, making it possible to distinguish materials through subtle spectral discrepancies. Because these spectral bands are highly correlated, dimensionality reduction, as the name suggests, seeks to reduce data dimensionality without losing desirable information. This article reviews discriminant analysisbased dimensionality-reduction approaches for hyperspectral imagery, including typical linear discriminant analysis (LDA), state-of-the-art sparse graph-based discriminant analysis (SGDA), and their extensions.
AB - Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral signatures, making it possible to distinguish materials through subtle spectral discrepancies. Because these spectral bands are highly correlated, dimensionality reduction, as the name suggests, seeks to reduce data dimensionality without losing desirable information. This article reviews discriminant analysisbased dimensionality-reduction approaches for hyperspectral imagery, including typical linear discriminant analysis (LDA), state-of-the-art sparse graph-based discriminant analysis (SGDA), and their extensions.
UR - http://www.scopus.com/inward/record.url?scp=85044966279&partnerID=8YFLogxK
U2 - 10.1109/MGRS.2018.2793873
DO - 10.1109/MGRS.2018.2793873
M3 - Review article
AN - SCOPUS:85044966279
SN - 2473-2397
VL - 6
SP - 15
EP - 34
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
IS - 1
ER -