In recent years, Gabor filtering has been successfully applied in spectral–spatial hyperspectral image (HSI) classification tasks due to its strong power to characterize surface materials. Generally, the standard Gabor filter involves the real and imaginary parts. However, the traditional usage to integrate both the two parts, i.e., the Gabor magnitude feature, might weaken some unique characteristics of each part, therefore leading to possible information loss. To solve this problem, we proposed a flexible 3-D Gabor features fusion (F3DGF) approach to make better use of two parts of Gabor features, based on the phase-induced 3-D Gabor feature, which is rarely exploited before. As the term suggests, the phase-induced feature is guided by a phase parameter P, which can flexibly combine the information from two parts of Gabor components. The proposed F3DGF scheme explores all the phase-induced 3-D Gabor features by means of a decision-level fusion strategy, where the obtained probability outputs are directly gathered to generate the decision map. Experimental results on three real HSIs demonstrate that our approach exhibits good improvements, as compared to the traditional real part and the magnitude Gabor features-based classification methods. The results show great potential to introduce phase-induced 3-D Gabor features for classification tasks.