Hyperspectral classification employing spatial–spectral low rank representation in hidden fields

This paper presents a novel classification method based on spatial–spectral low-rank representation in the hidden field under a Bayesian framework for hyperspectral imagery. The key idea of the method is to simultaneously explore the low-rank property in the spectral domain and nonlocal self-similarity in the spatial domain of the hidden field, which is estimated by sparse multinomial logistic regression in a supervised manner. First, the low rank property in the spectral domain is exploited in local cubic patches. Following this, similar cubic patches are clustered into several groups in a nonlocal sense and patches in each group are assumed to lie in a low-rank subspace. The final model could be efficiently solved by the augmented Lagrangian method. Experimental results on two real hyperspectral datasets validate that the proposed classifier produces a superior performance compared to other state-of-the-art classifiers in terms of overall accuracy, average accuracy and the kappa statistic (k).