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Recently, deep learning-based algorithms have been widely used for classification of hyperspectral images (HSIs) by extracting invariant and abstract features. In our conference paper presented at IEEE International Geoscience and Remote Sensing Symposium 2018, 1-D-capsule network (CapsNet) and 2-D-CapsNet were proposed and validated for HSI feature extraction and classification. To further improve the classification performance, the robust 3-D-CapsNet architecture is proposed in this article by following our previous work, which introduces the maximum correntropy criterion to address the noise and outliers problem, generating a robust and better generalization model. As such, discriminative features can be extracted even if some samples are corrupted more or less. In addition, a novel dual channel framework based on robust CapsNet is further proposed to fuse the hyperspectral data and light detection and ranging-derived elevation data for classification. Three widely used hyperspectral datasets are employed to demonstrate the superiority of our proposed deep learning models.