Hyperspectral data classification using image fusion based on curvelet transform

A new supervised classifier based on image fusion of hyperspectral data is proposed. The technique first selects the suitable bands as the candidates for fusion. Then, the bands based on curvelet transform are fused into several components. The fused hyperspectral components as the extracted features are fed into the supervised classifier based on Gaussian Mixture Model. After the estimation of the GMM with Expectation Maximization, the pixels are classified based on the Bayesian decision rule. One requirement of the technique is that the training samples should be provided from the hyperspectral data to be analyzed. The main merits of the new method contain tow folds. One is the novel feature extraction based on curvelet transform which fully makes use of the spectral properties of the hyperspectral data. The other one is the low computing complexity by reducing the data dimension significantly. Experimental result on the real hyperspectral data demonstrate that the proposed technique is practically useful and posses encouraging advantages.