TY - GEN
T1 - Sparse feature extraction for hyperspectral image classification
AU - Wang, Lu
AU - Xie, Xiaoming
AU - Li, Wei
AU - Du, Qian
AU - Li, Guojun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/31
Y1 - 2015/8/31
N2 - Due to the high dimensionality and redundant spectral information in a hyperspectral image (HSI), principal component analysis (PCA) and linear discriminant analysis (LDA) are commonly-used for its feature extraction. By converting PCA and LDA to regression problems and imposing l1-norm constraint on the regression coefficients, sparse principal component analysis (SPCA) and sparse discriminant analysis (SDA) have been developed for improved feature extraction. Furthermore, recently sparse tensor discriminant analysis (STDA), reserving useful structural information and obtaining multiple interrelated is also proposed. Their performance in HSI classification is investigated in this paper. Experiment results demonstrate the effectiveness of these sparse feature extraction methods, especially for STDA, which outperforms the traditional linear counterparts without maintaining spatial relationships among pixels, such as PCA and LDA.
AB - Due to the high dimensionality and redundant spectral information in a hyperspectral image (HSI), principal component analysis (PCA) and linear discriminant analysis (LDA) are commonly-used for its feature extraction. By converting PCA and LDA to regression problems and imposing l1-norm constraint on the regression coefficients, sparse principal component analysis (SPCA) and sparse discriminant analysis (SDA) have been developed for improved feature extraction. Furthermore, recently sparse tensor discriminant analysis (STDA), reserving useful structural information and obtaining multiple interrelated is also proposed. Their performance in HSI classification is investigated in this paper. Experiment results demonstrate the effectiveness of these sparse feature extraction methods, especially for STDA, which outperforms the traditional linear counterparts without maintaining spatial relationships among pixels, such as PCA and LDA.
KW - Sparse projections
KW - elastic net
KW - feature extraction
KW - hyperspectral imagery
KW - tensor decomposition
UR - http://www.scopus.com/inward/record.url?scp=84957595496&partnerID=8YFLogxK
U2 - 10.1109/ChinaSIP.2015.7230568
DO - 10.1109/ChinaSIP.2015.7230568
M3 - Conference contribution
AN - SCOPUS:84957595496
T3 - 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
SP - 1067
EP - 1070
BT - 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015
Y2 - 12 July 2015 through 15 July 2015
ER -