Hyperspectral image (HSI) consists of hundreds of narrow and continuous spectral bands, in which abundant spectral information enhances the ability to identify land cover materials.1–5 With the rapid development of imaging sensor technology, the acquisition of high spatial resolution HSI is no longer a challenge, which provides an ideal research base for environmental protection,6 land monitoring,7 and urban development.8 Hyperspectral image classification (HSIC), which provides a unique land cover category for each pixel according to spectral signatures and spatial context, is an important task in these remote sensing applications.9–11 Typical HSIC methods consist of two separate steps: feature extraction and classifier training. However, facing the challenges of high-dimensional nonlinear distribution and redundant bands of information, traditional methods generally suffer from low generalization ability and limited degree of feature representation.