Combined multiple spectral–spatial features and multikernel support tensor machine for hyperspectral image classification

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Due to the problem that current hyperspectral image tensor classification methods cannot make full use of the multiple spectral–spatial features of hyperspectral images (HSIs), a HSI spectral–spatial classification method based on multikernel support tensor machine is proposed. In the tensor structural space, using the 3-D Gabor wavelet and morphological attribute filters to obtain multiple texture features and multiple attribute structural features, respectively, our study combines multiple spectral–spatial features to classify the HSIs in the multikernel learning framework. By forming the multiple kernel function with the linear weighted combination of the single kernel functions, the proposed method calculates the weights using the iterative method to effectively utilize the multicomplementary features of hyperspectral imagery to perform collaborative classification. The experiments are performed on the widely used Pavia University and Salinas hyperspectral data sets. The classification results indicate that the proposed method can effectively integrate complementary spatial–spectral features, which have higher classification accuracy and better spatial continuity classification mapping when applied to the classification images.