TY - JOUR
T1 - Semisupervised local discriminant analysis for feature extraction in hyperspectral images
AU - Liao, Wenzhi
AU - Pizurica, Aleksandra
AU - Scheunders, Paul
AU - Philips, Wilfried
AU - Pi, Youguo
PY - 2013/1/31
Y1 - 2013/1/31
N2 - We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.
AB - We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.
KW - feature extraction
KW - hyperspectral remote sensing
KW - semisupervised
KW - classification
UR - http://hdl.handle.net/1854/LU-2962890
U2 - 10.1109/TGRS.2012.2200106
DO - 10.1109/TGRS.2012.2200106
M3 - Article
SN - 0196-2892
VL - 51
SP - 184
EP - 198
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 1
ER -