The distinction of similar classes has always been the core issue in image classification. In this paper, a new hierarchical classification process based on three-dimensional attention soft augmentation (HC-3DAA) is proposed to improve the accuracy of classifiers, especially for the accuracy between similar classes. In HC-3DAA processing, the merging matrix is firstly constructed based on the validation confusion matrix to measure the similarity among different classes. Specifically, the 3D attention soft augmentation module combined with CutMix is designed for guiding the network model to focus on the key discriminative features. Then the extracted 3D feature differences between similar classes are inserted into the attention module for the reclassification to get higher classification accuracy. To evaluate the performance of HC-3DAA, CutMix models with different feature dimensions and the hierarchical classification module are separately discussed on two widely used hyperspectral datasets. Two different classifiers 3D-CNN and ResNet are included in the comparative analysis. Besides, experimental results also demonstrate that the proposed HC-3DAA outperforms several state-of-the-art attention-based methods.

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