This paper proposes a new hyperspectral texture descriptor, which is a variant of Local Binary Pattern (LBP) for hyperspectral imaging. This descriptor effectively describes the texture information in hyperspectral images while addressing the issue of high dimensionality. The proposed descriptor enhances the LBP by using an ordering approach along the spectral dimension to capture local neighboring relationships across different spectral bands. It then approximates the second-order derivatives by calculating differences between adjacent ordered bands to capture the ordered spectral variation. Finally, it applies rotation invariant uniform LBP separately on each ordered and differenced band. The proposed descriptor, Order Difference Local Binary Pattern (ODLBP), concatenates the resulting histograms. It is tested on several hyperspectral image datasets for texture classification. The results demonstrate that the proposed ODLBP has comparable performance with the most discriminative LBP descriptors while maintaining a reduced dimensionality space, indicating its effectiveness in hyperspectral texture analysis.