LIU Jing,LI Qing-yan,LIU Yi.Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion[J].Optics and Precision Engineering,2021,29(06):1397-1405.
LIU Jing,LI Qing-yan,LIU Yi.Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion[J].Optics and Precision Engineering,2021,29(06):1397-1405. DOI: 10.37188/OPE.20212906.1397.
Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion
To extract efficient nonlinear discriminant features when foreign objects, with similar spectra and outlier classes, are present in hyperspectral remote sensing images (HRSIs), a kernel class pair-weighted (KCP-weighted) criterion is proposed. First, we derive a class pair form of the kernel linear discriminant analysis (KLDA) criterion, viz. the kernel class pair (KCP) criterion, in which the kernel-between-class and kernel-within-class scatter matrices are both expressed in the form of class pairs. Then, the KCP-weighted criterion is proposed to weight the kernel-between-class and kernel-within-class scatter matrices of each class pair according to their separability in a kernel space. The KCP-weighted criterion can ensure that the separabilities of class pairs are balanced in the KCP-weighted feature subspace. Finally, the K-nearest neighbor and minimum distance classifiers are used to evaluate the feature extraction performance. Experimental results of two real HRSIs show that, compared with the original space and KLDA methods as well as the kernel weighted pairwise Fisher criterion, the presented KCP-weighted criterion can effectively improve the overall terrain classification rate while reducing the dimensionality of the data.
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