Kernel-OPBS Algorithm: A Nonlinear Feature Selection Method for Hyperspectral Imagery

Xiaorun Li

doi:10.1109/LGRS.2019.2924972

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Kernel-OPBS Algorithm: A Nonlinear Feature Selection Method for Hyperspectral Imagery

2020-03-01

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DOI：10.1109/LGRS.2019.2924972

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Abstract

En 中文

The orthogonal-projection-based band selection (OPBS) algorithm is one of the newly proposed band selection methods. In this letter, we present a nonlinear version of the OPBS method, which is denoted as the Kernel-OPBS method. The OPBS method selects the desired bands one by one, and in each round of lookup, it chooses the band that has the maximum distance to the hyperplane spanned by the currently selected bands. Extending this algorithm to a feature space associated with the original input space through a certain nonlinear mapping function can provide a nonlinear version of the OPBS algorithm. Although it is basically intractable to compute the mapped bands due to the high dimensionality of the feature space produced by the nonlinear mapping function, the selection criterion of the Kernel-OPBS method is actually related to only the inner products of the mapped bands; thus, the kernel function can be applied and it is unnecessary to define the nonlinear mapping function. Experimental results on different data sets demonstrate that the selected bands obtained by the Kernel-OPBS method can achieve higher pixel classification performances than that by the OPBS method.

Keywords:

Kernel

Hyperspectral imaging

Indexes

Feature extraction

Dimensionality reduction

Redundancy

Classification

dimensionality reduction (DR)

hyperspectral remote sensing

kernels

unsupervised feature selection

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