Deep learning approaches have recently been widely applied to the classification of hyperspectral images (HSIs) and achieve good capability. Deep learning can effectively extract features from HSI data compared with other traditional hand-crafted methods. Most deep learning methods extract image features through traditional convolution, which has demonstrated impressive ability in HSI classification. However, traditional convolution can only operate convolutions with fixed size and weight on regular square image regions. Moreover, it refers to the spectral features of the adjacent pixels but ignores the spectral features of long-range data with the training sample. Although a graph convolution network (GCN) can process irregular image regions, the pixels’ relationships for graph construction cannot be well ensured with limited iterations. Hence, the extracted features have limited performance with the GCN. Aiming to extract more representative and discriminative image features, in this article, the deep feature learning with label consistencies (DFL-LC) method is developed to realize HSI classification. In the proposed method, a multiscale convolutional neural network is adopted to obtain basic HSI features, and the GCN can further capture relationships between pixels and extract more representative HSI features. For obtaining discriminative features, we add the label consistency of single pixels and label consistency of group pixels regularization in the objective function. It can maintain label consistency for the general and long-range data and alleviate deficiently labeled samples. The experimental results on three representative datasets fully demonstrate that the DFL-LC method is superior to other methods in both quantitative and qualitative aspects.

中文翻译：

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DFL-LC：具有高一致性图像分类标签一致性的深度特征学习

深度学习方法最近已广泛应用于高光谱图像（HSI）的分类，并具有良好的性能。与其他传统的手工方法相比，深度学习可以从HSI数据中有效提取特征。大多数深度学习方法都是通过传统卷积提取图像特征的，这在HSI分类中已显示出令人印象深刻的能力。但是，传统卷积只能在规则的正方形图像区域上以固定的大小和权重进行卷积。此外，它指的是相邻像素的光谱特征，但忽略了训练样本的远程数据的光谱特征。尽管图卷积网络（GCN）可以处理不规则的图像区域，但是在有限的迭代中不能很好地确保像素与图的关系。因此，提取的功能在GCN中的性能有限。为了提取更多具有代表性和判别力的图像特征，本文开发了一种基于标签一致性的深度特征学习方法（DFL-LC）来实现HSI分类。该方法采用了多尺度卷积神经网络来获取基本的HSI特征，而GCN可以进一步捕获像素之间的关系并提取出更具代表性的HSI特征。为了获得区分特征，我们在目标函数中添加了单个像素的标签一致性和组像素正则化的标签一致性。它可以保持常规数据和远程数据的标签一致性，并减少标记不足的样本。