Deep learning-based methods, especially deep convolutional neural networks (CNNs), have shown their effectiveness for hyperspectral image (HSI) classification. In previous deep CNN-based HSI classification methods, a cuboid is empirically determined as the input. The dimensionalities of the cuboid, including height and weight, are crucial to the final classification results. Unfortunately, these superparameters (i.e., the dimensionalities of input cube) are hand-crafted, which means the inputs of a classifier are not optimized according to the specific hyperspectral dataset. In this letter, spatial transformation network (STN) is explored to obtain the optimal input for CNN-based HSI classification for the first time. STN is used to translate, rotate, and scale the original input to obtain optimized input for the following CNN. Moreover, in order to mitigate the overfitting problem in CNN-based HSI classification, DropBlock is introduced as a regularization technique for HSI accurate classification. Compared with dropout, which is a popular regularization technique, DropBlock obtains better classification accuracy. The proposed methods are tested on two widely used hyperspectral data sets (i.e., Salinas and Kennedy Space Center). The obtained experimental results show that the proposed methods provide competitive results compared with state-of-the-art methods including deep CNN-based methods.