Hyperspectral images (HSIs) are a kind of optical remote sensing image with a high spectral resolution. Hyperspectral images (HSIs) have attracted much attention recently as they possess unique properties and contain massive information. The newly developed deep learning methods are applied successfully in HSI classification, achieving higher accuracy than traditional methods. The earlier DL-based HSI classification methods were based on fully connected neural networks, such as stacked autoencoders (SAEs) and recursive autoencoders (RAEs). Therefore, they destroyed the spatial structure information of an HSI as they could only handle one-dimensional vectors.