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RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

Journal from gdlhub / 2016-11-17 03:37:31
By : Lin Bai, Defa Hu, Meng Hui, Yanbo Li, Telkomnika
Created : 2015-09-01, with 1 files

Keyword : hyperspectral classification, non-negative matrix factorization, relevance vector machine, kernel method
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1805

A novel kernel framework for hyperspectral image classification based on relevance vector machine (RVM) is presented in this paper. The new feature extraction algorithm based on Mexican hat wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method of nonlinear mapping capability based on kernel NMF can be improved. The new classification framework of hyperspectral image data combined with the novel WKNMF and RVM. The simulation experimental results on HYDICE and AVIRIS data sets are both show that the classification accuracy of proposed method compared with other experiment methods even can be improved over 10% in some cases and the classification precision of small sample data area can be improved effectively.

Description Alternative :

A novel kernel framework for hyperspectral image classification based on relevance vector machine (RVM) is presented in this paper. The new feature extraction algorithm based on Mexican hat wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method of nonlinear mapping capability based on kernel NMF can be improved. The new classification framework of hyperspectral image data combined with the novel WKNMF and RVM. The simulation experimental results on HYDICE and AVIRIS data sets are both show that the classification accuracy of proposed method compared with other experiment methods even can be improved over 10% in some cases and the classification precision of small sample data area can be improved effectively.

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