Sparse representation and smooth filtering for hyperspectral image classification

Sparse representation-based classification (SRC) has gained great interest recently. A pixel to be classified is sparsely approximately by labeled samples, and it is assigned to the class whose labeled samples provide the smallest representation error. In this paper, we extend SRC by exploiting the benefits of using a smoothing filter based on sparse gradient minimization. The smoothing filter is expected to provide less intra class variability and more spatial regularity, which eliminating the inherent variations within a small neighborhood. Classification performance on two real hyperspectral datasets demonstrates that our proposed method has improved classification accuracy and the resulting accuracies are persistently higher at all small training sample size situations compared to some traditional classifiers.