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Deep learning and, in particular, convolution\u2010based approaches are the current state\u2010of\u2010the\u2010art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context\u2010based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA\u2009+\u2009DCN, and our context\u2010based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA\u2009+\u2009DCN perform well on the original datasets but not on the noisy datasets. 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