Data Augmentation (DA) is significant for Hyperspectral Image (HSI) classification especially in the case of limited labeled training data. Various DA models have been introduced to augment HSI data by using image processing techniques, prior knowledge or contextual information of HSI. However, spatial information in HSI could be inefficiently adopted in these DA models, leading to the lack of diversity and richness of the augmented data. To handle the issue, inspired by the recently proposed Principal Component Analysis based DA (PCA-DA) and Superpixelwise PCA (SuperPCA), we introduce SuperPCA based DA (SuperPCA-DA) in this paper. Specifically, an HSI is firstly divided into various superpixel blocks by typical image segmentation techniques, followed by fixed-size window and superpixel based local reconstruction for HSI denoising, and then the proposed SuperPCA-DA is employed in each superpixel block for HSI data augmentation where PCA is locally conducted in each superpixel to extract low-dimensional features, which will be projected back onto the original high-dimensional spectral space with random noises added into the projection matrix of PCA. The novel DA model can effectively generate new samples with diversity and richness by employing superpixels based spatial information of HSI, which could outperform classic and state-of-the-art DA models and then improve the accuracy of the subsequent classification models, especially when the number of training data is limited. The experimental results on three HSI datasets demonstrate the effectiveness of the proposed method. The code of the proposed model is available at https://github.com/XinweiJiang/SuperPCA-DA .
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