Three Dimensional Local Binary Patterns for Hyperspectral Imagery Classification in Matlab

Three Dimensional Local Binary Patterns for Hyperspectral Imagery Classification in Matlab

Abstract:

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.