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In this article, we explore a conditional hyperspectral image (HSI) synthesis method with generative adversarial networks (GAN). A new multistage and multipole generative adversarial network, which is suitable for conditional HSI generation and classification (HSIGAN), is proposed. For HSIs synthesis, it is crucial to learn a great deal of spatial–spectral distribution features from source data. The multistage progressive training makes the generator effectively imitate the real data by fully exploiting the high-dimension learning capability of GAN models. The coarse-to-fine information extraction method helps the discriminator to understand the semantic feature better while the multiscale classification prediction presents a positive impact on results. A spectral classifier joins the adversarial network, which offers a helping hand to stabilize and optimize the model. Moreover, we apply the 3-D DropBlock layer in the generator to remove semantic information in a contiguous spatial–spectral region and avoid model collapse. Experimental results of the quantitative and qualitative evaluation show that HSIGAN could generate high-fidelity, diverse hyperspectral cubes while achieving top-ranking accuracy for supervised classification. This result is encouraging for using GANs as a data augmentation strategy in the HSI vision task.