To effectively extract the spatial spectral structural features of hyperspectral remote sensing images, enhance feature discrimination and improve classification accuracy, a classification method for hyperspectral remote sensing images is proposed based on local ternary pattern encoded fractional order Gabor. Firstly, effective extraction of local features is achieved using fractional order 3D Gabor filters. Secondly, local ternary mode encoding is applied to Gabor phase features to improve their discriminability. Then, Gabor phase features are classified using a random forest algorithm to obtain confidence cubes. Finally, by fusing multiple sets of Gabor based confidence cubes, the textural features with complementarity and strong expressivity are extracted. Three training samples are selected for validation on the Indian Pines, Salinas, and Trento datasets, and the overall classification accuracy reaches 63.50%, 81.78%, and 86.89%, respectively. The experimental results verifies that the proposed method has better classification performance.