TY - GEN
T1 - Randomized nonlinear component analysis for dimensionality reduction of hyperspectral images
AU - Damodaran, Bharath Bhushan
AU - Courty, Nicolas
AU - Tavenard, Romain
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Kernel based feature extraction method overcomes the curse of dimensionality and captures the non-linearities present in the data. However, these methods are not scalable with large number of pixels found with hyperspectral images. Thus, a small subset of pixels are randomly selected to make the solution of kernel based methods tractable. In this paper, we propose scalable nonlinear component analysis for dimensionality reduction of hyperspectral images. The proposed method relies on the randomized feature maps to capture the non-linearities between the variables in the hyperspectral data. Experiments conducted with three hyperspectral datasets show that our proposed method has provided better quality components and outperformed the state-of-the-art in terms of classification performance.
AB - Kernel based feature extraction method overcomes the curse of dimensionality and captures the non-linearities present in the data. However, these methods are not scalable with large number of pixels found with hyperspectral images. Thus, a small subset of pixels are randomly selected to make the solution of kernel based methods tractable. In this paper, we propose scalable nonlinear component analysis for dimensionality reduction of hyperspectral images. The proposed method relies on the randomized feature maps to capture the non-linearities between the variables in the hyperspectral data. Experiments conducted with three hyperspectral datasets show that our proposed method has provided better quality components and outperformed the state-of-the-art in terms of classification performance.
KW - Hyperspectral image classification
KW - Kernel MNF
KW - Kernel PCA
KW - Kernel approximation
KW - Nonlinear component analysis
KW - Random Fourier feature
UR - https://www.scopus.com/pages/publications/85041846445
U2 - 10.1109/IGARSS.2017.8126819
DO - 10.1109/IGARSS.2017.8126819
M3 - Conference contribution
AN - SCOPUS:85041846445
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5
EP - 8
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -