An extinction profile (EP) is an effective spatial-spectral feature extraction method for hyperspectral images (HSIs), which has recently drawn much attention. However, the existing methods utilize the EPs in a stacking way, which is hard to fully explore the information in EPs for HSI classification. In this paper, a novel fusion framework termed EPs-fusion (EPs-F) is proposed to exploit the information within and among EPs for HSI classification. In general, EPs-F includes the following two stages. In the first stage, by extracting the EPs from three independent components of an HSI, three complementary groups of EPs can be constructed. For each EP, an adaptive superpixel-based composite kernel strategy is proposed to explore the spatial information within an EP. The weights to create the composite kernel and the number of superpixels are automatically determined based on the spatial information of each EP. In the second stage, since the different EPs contain highly complementary information, a simple yet effective decision fusion method is further applied to obtain the final classification result. Experiments on three real HSI data sets verify the qualitative and quantitative superiority of the proposed EPs-F method over several state-of-the-art HSI classifiers.