Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art 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-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

中文翻译：

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用于高度不平衡高光谱分类的稳健的基于上下文的深度学习方法


高光谱成像是一个活跃的研究领域，在遥感、矿物勘探和环境监测方面有许多应用。深度学习，特别是基于卷积的方法是当前最先进的分类模型。然而，在存在噪声高光谱数据集的情况下，这些深度卷积神经网络表现不佳。在本文中，我们提出了一种特征增强方法来提高不平衡高光谱分类中的噪声抵抗力。我们的方法计算基于上下文的特征，并使用深度卷积神经网络（DCN）。我们在 Pavia 数据集上测试了我们提出的方法，并使用原始数据集和加噪声的数据集比较了三种模型：DCN、PCA + DCN 和我们基于上下文的 DCN。我们的实验结果表明，DCN 和 PCA + DCN 在原始数据集上表现良好，但在噪声数据集上表现不佳。我们强大的基于上下文的 DCN 在存在噪声的情况下能够胜过其他 DCN，并且能够在干净的高光谱图像上保持可比的分类精度。