DFL-LC: Deep Feature Learning With Label Consistencies for Hyperspectral Image Classification

Abstract

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.