Graph Convolutional Network With Relaxed Collaborative Representation for Hyperspectral Image Classification

Graph convolutional networks (GCNs) have been skillfully employed in hyperspectral image (HSI) classification, exhibiting remarkable performance owing to their unique superiority in handling non-Euclidean graph-structured data. However, the inherent absence of predefined connections between pixels in HSI results in the underutilization of the structural and attribute information of the graph edges. Furthermore, the construction of adjacency matrices for large-scale HSI data imposes a huge computational burden on traditional GCNs. Therefore, in this article, a novel method combining relaxed collaborative representation (RCR) and GCN (RCR-GCN) for hyperspectral classification is proposed. Specifically, RCR is adopted to compute the representation coefficients of each feature, reflecting the similarity and diversity among different sample features. Meanwhile, the representation coefficients are applied as edge attributes in the graph, denoting the weights of the connections between neighboring nodes. After that, GCN is employed to classify the graph nodes. Moreover, an efficient version of the RCR-GCN method is developed to boost the computation, which constructs the graph based on superpixel nodes instead of the pixel nodes by using simple linear iterative clustering (SLIC). Extensive experiments on three HSI image datasets demonstrate that the proposed method outperforms other state-of-the-art methods and achieves more efficiency and feasibility in HSI image classification.