The local binary pattern (LBP) is a simple and efficient texture descriptor for image processing. Recently, LBP has been introduced for feature extraction of hyperspectral imagery. Specifically, the LBP codes are extracted from the 2-D band images to capture the spatial correlation among neighboring pixels, and then the statistical histogram features from all bands, which could estimate the underlying distribution in local area, are concatenated together for pixel-wise classification. However, since hyperspectral imagery contains rich spectral and spatial information, which is actually a 3-D data cube, the 2-D LBP (2-DLBP) model cannot fully exploit the joint spectral-spatial structure. In this paper, the 2-DLBP has been extended into 3-D LBP (3-DLBP) model through forming a 3-D regular octahedral frame to characterize the spectral-spatial relationship. In order to reflect the local continuous property of hyperspectral data in both the spectral and spatial domains, while ensuring the rotational invariance of the 3-DLBP model, the code patterns of 3-DLBP model have been divided into eight groups (including seven groups of "dense" patterns and one group of "nondense" patterns) based on the consistency of spectral-spatial topology structure. Specifically, the patterns in seven "dense" groups correspond to the microstructures in the 3-D domains (such as spots, edges, and flat areas), which has a high percentage in all the 3-DLBP patterns, while the rest patterns are aggregated and treated as the "nondense" patterns. The proposed method is thus called 3-D dense LBP (3-(DLBP)-L-2) model. Moreover, instead of taking zero as the hard threshold, a slack variable has been introduced to enable the difference between the central pixel and the neighboring ones varying in a small interval, which could greatly decrease the impact of spectral variability and noise, and the discriminative power of the features has been further boosted. The slack threshold-based 3-(DLBP)-L-2 model is named ST-3-(DLBP)-L-2. A series of experiments is conducted on three real hyperspectral imageries to demonstrate the effectiveness of the proposed two 3-(DLBP)-L-2-based methods. The experimental results show that the performance of the proposed ST-3-(DLBP)-L-2 is significantly superior to that of 2-DLBP, which is also better than the 3-(DLBP)-L-2 model and several state-of-the-art hyperspectral classification methods.