Kernel Joint Sparse Representation Hyperspectral Image Classification Based on Adaptive Matrix
Aiming at the problem of insufficient utilization of rich spatial information and spectral information in hyperspectral images,a kernel joint sparse representation method based on adaptive matrices is proposed for hyperspectral image classification.In the feature representation stage,adaptive matrix features are defined to describe the original spectral pixels by combining the feature of adaptive neighborhood block strategy with a nonlinear correntropy measurement,which fully integrates shape-variable spatial information and nonlinear spectral information.In the classification stage,considering the adaptive matrix and the nonlinearity of hyperspectral images,a kernel joint sparse representation model is constructed using a logarithmic Euclidean kernel function to obtain reconstruction errors.Meanwhile,matrix correlation is constructed using dictionary space information,and a balanced parameter is introduced to achieve joint classification of sparse reconstruction error and matrix correlation.Experimental results on two datasets demonstrate that the proposed algorithm fully utilizes the spatial and spectral information of hyperspectral images,and can effectively improve classification accuracy.
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