Three-dimensional local binary patterns for hyperspectral imagery classification

Local binary patterns (LBP) features extracted from hyperspectral imagery (HSI) have gained impressive performance in hyperspectral classification tasks, for which LBP got considerable attention. However, existing LBP-based hyperspectral imagery classification methods utilized two-dimensional LBP (2DLBP) that could capture gray variation signal in space, which did not excavate the contextual information that hides in spectral-spatial structure considering that hyperspectral imagery characterizes by three dimension. Aimed at this problem, this paper presents a three-dimensional LBP-based (3DLBP) hyperspectral imagery classification method where 2DLBP textures histogram on three orthogonal planes are concatenated to form 3DLBP texture features to be classified by sparse representation. A serial of experiments are conducted on the Pavia university dataset, and the experimental results show that the performance of 3DLBP is significantly superior to that of 2DLBP.