RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization
Abstract: 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.
Keywords: hyperspectral
classification, non-negative matrix factorization, relevance vector machine, kernel
method
Author: Lin Bai, Defa Hu, Meng
Hui, Yanbo Li
Jounal Code: jptkomputergg150126
