Spectral-Spatial Graph Attention Network for Semisupervised Hyperspectral Image Classification

Hyperspectral image (HSI) classification with a small number of training samples has been an urgently demanded task because collecting labeled samples for hyperspectral data is expensive and time-consuming. Recently, graph attention network (GAT) has shown promising performance by means of semisupervised learning. It combines the information of labeled and unlabeled samples so that the weakness of inadequate labeled samples is alleviated. In this letter, we propose a novel method, spectral-spatial GAT (SSGAT), for semisupervised HSI classification. The proposed SSGAT takes all samples (training and testing samples) as nodes and establishes an edge set among them to form a graph structure. In particular, the edge set is constructed in an unsupervised manner based on a large neighborhood to make full use of spectral-spatial information. Furthermore, the proposed method computes attention coefficients between a node and its neighbor nodes and aggregates them to generate more discriminative features, thus improving the performance of HSI classification. Experimental results on public data sets demonstrate the superiority of our proposed method compared with several state-of-the-art methods.

Automated summary

The study introduces a spectral-spatial GAT (SSGAT) method for semi-supervised hyperspectral image classification. SSGAT forms a graph structure by establishing edge sets among samples and constructs the edge set in an unsupervised manner to fully utilize spectral-spatial information, thereby improving classification performance.