High spectral resolution and correlation hinder the application of classification in hyperspectral data. To improve classification accuracy

a hyperspectral decision fusion classification method based on principal component analysis (PCA) and windowed wavelet transform is proposed in this study. A correlation coefficient matrix is used to group original hyperspectral data. PCA is applied to reduce the spectral dimensions of data for each group. The proposed windowed wavelet transform method is used to extract spatial features. Linear opinion pool is employed to fuse the classification results from multi-classifiers. Using two hyperspectral data sets from different sensors

the proposed algorithm obtain higher classification accuracy and Kappa coefficient than five existing algorithms. The classification accuracy of the proposed algorithm outperforms that of support vector machine-radial basis function (SVM-RBF) by approximately 8%. Experimental results show that the proposed method can explore spectral-spatial information from hyperspectral imagery

improve classification accuracy efficiently

and provide outstanding classification performance under a small sample size and noisy environments.