Hyperspectral image classification via discriminative sparse representation with extended LBP texture

Hyperspectral images (HSI) have rich texture information, so combining texture information and image spectral information can improve the recognition accuracy. Sparse representation has significant success in image classification. In this paper, we propose a new discriminative sparse-based classification framework using spectral data and extended Local Binary Patterns (LBP) texture. Firstly, we propose an extended LBP coding for HSI classification. Then we formulate an optimization problem that combines the objective function of classification with the representation error by sparsity. Furthermore, we use a procedure similar to K-SVD algorithm to learn the discriminative dictionary. The experimental results show that the proposed discriminative spasity-based classification of image including the extended LBP texture outperforms the classical HSI classification algorithms.