Hyperspectral data classification using classifier overproduction and fusion strategies

A new hybrid algorithm based on bagging and random subspace methods is proposed for improving hyperspectral data classification problem. The effects of using original data and transformed data in bagging, random subspace and the proposed algorithm are also explored. Real data experiment result shows that the proposed method performs well in both original and NWFE feature spaces.