A Novel Method for Hyperspectral Image Classification: Deep Network With Adaptive Graph Structure Integration

Abstract

Hyperspectral image (HSI) classification has always been one of the hot issues in the study of geographic remote sensing information, and graph neural networks have attracted much attention in recent years. Several graph neural network-based approaches have been introduced into HSI study to explore the spatial information of HSI within a constructed graph. However, the existing methods of building HSI-based graphs are always unsuitable and inaccurate due to the complicated spatial variability of spectral signatures. Meanwhile, these graph-based HSI classification methods usually suffer from the over-smoothing problem. Motivated by these, this article presents a novel deep network with adaptive graph structure integration (DNAGSI), which could learn a graph structure of HSI dynamically and promote its discriminative ability with devising a much deeper network architecture. Specifically, dynamic graphs are first built across different layers and adaptively integrated with the initial graph structure to boost the robust graph representation of HSI. Second, the initial residual and identity mapping are employed to significantly increase the depth of the network and obtain more abstract deep features. Finally, a joint loss with center loss is devised to learn the similarity relationship between hyperspectral pixels explicitly, thereby gathering the intraclass graph features. Benefiting from the integration of center loss, initial residual, and identity mapping, the proposed method can alleviate the over-smoothing problem effectively to some extent. Experiments on benchmark HSI datasets demonstrate the superiority of DNAGSI over state-of-the-art methods.