Spatial-spectral classification of hyperspectral image via group tensor decomposition

In this paper, a novel group tensor decomposition (GTD) method is proposed to alleviate within-class spectral variation by fully exploit the low-rank property of 3D HSI, which can significantly improve the classification performance. Specifically, the spectral dimension of the HSI is firstly reduced with principal component analysis (PCA) algorithm. Then, the dimension reduced image is segmented into a set of overlapping 3D tensor patches, which are then clustered into groups by K-means algorithm. By unfolding the similar tensors of each group into a set of matrices and stacking them, these similar tensor patches are constructed as a new tensor. Next, the intrinsic spectra tensor and its corresponding spectral variation tensor of each new tensor are estimated with a low-rank tensor decomposition (LRTD) algorithm. By aggregating all intrinsic spectra tensor in each group, we can obtain an integral intrinsic spectra tensor and separate its corresponding spectral variation tensor. Finally, the pixel-wise classification is performed only on the intrinsic spectra tensor, which can reflect the material-dependent properties of different objects. Experimental results on real HSI data sets demonstrate the superiority of the proposed GTD algorithm over several well-known classification approaches.