Kernel nonparametric weighted feature extraction for hyperspectral image classification

  • Kuo B
  • Li C
  • Yang J
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Abstract

In recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data, and some results show that kernel-based classifiers have satisfying performances. Many studies about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. However, NWFE is still based on linear transformation. In this paper, the kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation, and the experimental results show that KNWFE outperforms NWFE, decision-boundary feature extraction, independent component analysis, kernel-based principal component analysis, and generalized discriminant analysis.

Author-supplied keywords

  • Feature extraction
  • Image classification

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Authors

  • Bor Chen Kuo

  • Cheng Hsuan Li

  • Jinn Min Yang

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