6533b7d1fe1ef96bd125d5d1
RESEARCH PRODUCT
Explicit signal to noise ratio in reproducing kernel Hilbert spaces
Luis Gómez-chovaAllan Aasbjerg NielsenGustau Camps-vallssubject
Kernel methodSignal-to-noise ratiobusiness.industryNoise (signal processing)Covariance matrixKernel (statistics)Feature extractionPattern recognitionArtificial intelligencebusinessKernel principal component analysisMathematicsReproducing kernel Hilbert spacedescription
This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted features with the explicit KMNF also improve hyperspectral image classification.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-07-01 | 2011 IEEE International Geoscience and Remote Sensing Symposium |